Construction method and system of electric power balance optimization model, and balance optimization method and system

Abstract

The method comprises the following steps of: constructing a main problem model by taking the minimum total operation standby cost of a power supply system as a main problem optimization target and taking a unit operation state and system operation standby as constraints; the minimum total output cost of the system and the minimum total wind curtailment and light curtailment cost of the system under the potential output most extreme scene of the wind power plant and the photovoltaic power station serve as a first sub-problem optimization target and a second sub-problem optimization target respectively, and a sub-problem model is constructed through the first sub-problem optimization target, the second sub-problem optimization target and constraint conditions; determining an uncertainty set according to the maximum deviation value of the potential output of the wind power plant and the photovoltaic power station and the potential output expected value and the 01 variable; and constructing a power balance optimization model by the main problem model, the sub-problem model and the uncertainty set. According to the invention, by controlling the boundary of the uncertainty set to which the potential wind power and photovoltaic output belongs, the conservative control of the model optimization result is realized.

Description

Technical Field

[0001] The present invention relates to the field of power balance, and in particular to the construction of a power balance optimization model, a balance optimization method and a system. Background Art

[0002] In recent years, the balance of electricity supply and demand has faced an increasingly complex environment and increasing uncertainty, and the requirements for the economic, safe and clean operation of the power system have also increased. From the demand side, driven by economic and temperature factors, electricity demand has maintained rapid growth, load characteristics have continued to evolve, and the requirements for system regulation capabilities have increased; from the supply side, the proportion of new energy installed capacity such as wind power and photovoltaics has continued to increase, and the growth space for conventional power sources such as coal-fired power has been limited, and the volatility and randomness of power supply capacity have increased.

[0003] The risks faced by the balance of power supply and demand mainly come from uncertain factors. The characterization of uncertain factors in traditional research is mainly based on the idea of ​​probabilistic modeling, that is, selecting the main uncertain factors affecting the balance of power supply and demand (such as wind power, photovoltaic output, etc.), and through methods such as historical data statistical analysis, clarifying the value range and probability of each value of the uncertain factors, and then constructing a probabilistic analysis model.

[0004] In the existing probabilistic analysis ideas, more emphasis is placed on the analysis of the expected value of uncertain factors, and on the analysis of high-probability and normalized scenarios, while insufficient consideration is given to low-probability and extreme scenarios. In recent years, from the perspective of the external environment, the frequency of extreme climate events has increased under the background of global warming; from the perspective of the internal structure, as the proportion of new energy installed capacity continues to increase, the sensitivity of power supply capacity to climate temperature continues to increase. Therefore, the impact of "low-probability" events in the traditional sense on my country's power supply and demand balance continues to deepen. Although extreme scenarios such as "extreme heat without wind" and "extreme cold without light" have a low probability of occurrence, they have a serious impact on power supply and demand, and must be given full attention. The analysis ideas for power supply and demand balance urgently need to change from the traditional "high-probability, normalized" thinking to "bottom line thinking". Summary of the invention

[0005] In order to solve the problem that the existing probabilistic analysis ideas focus more on the analysis of the expected value of uncertain factors, focus on the analysis of high-probability and normalized scenarios, and insufficient consideration of low-probability and extreme scenarios, the present invention proposes a method for constructing a power balance optimization model, including:

[0006] Taking the minimization of the total operation and reserve cost of the power supply system as the optimization objective of the main problem, the main problem model is constructed with the unit operation status and system operation and reserve as constraints;

[0007] The optimization objective of sub-problem 1 is to minimize the total system output cost under the most extreme scenario of potential output of wind farms and photovoltaic power stations, and the optimization objective of sub-problem 2 is to minimize the total system wind and solar curtailment cost under the most extreme scenario of potential output of wind farms and photovoltaic power stations. The sub-problem model is constructed by the optimization objective of sub-problem 1, the optimization objective of sub-problem 2 and the constraint conditions.

[0008] The maximum deviation between the potential output of the wind farm and the expected value of the potential output of the photovoltaic power station and the uncertainty set to which the wind power potential output belongs and the uncertainty set to which the photovoltaic potential output belongs are determined respectively by the 01 variable;

[0009] A power balance optimization model is constructed by the main problem model, the sub-problem model, the uncertainty set to which the potential output of wind power belongs, and the uncertainty set to which the potential output of photovoltaic power belongs.

[0010] Optionally, the main problem optimization objective is to minimize the total operating reserve cost of the power supply system, and the main problem model is constructed with the unit operating status and system operating reserve as constraints, including:

[0011] The optimization objective is to minimize the total operating reserve cost of the power supply system;

[0012] The constraints are the continuity constraint of unit operation status, the positive reserve constraint of system operation, and the negative reserve constraint of system operation;

[0013] The main problem model is constructed by the optimization objective of the main problem and the continuity constraint of the unit operation state, the positive standby constraint of the system operation, and the negative standby constraint of the system operation.

[0014] Optionally, the optimization objective of the main problem is as follows:

[0015]

[0016] In the formula, Z F is the total system operation backup cost, N T is the number of thermal power units in the system; i is the unit subscript; T is the number of time; t is the time subscript; c TBit is the operating reserve cost of the i-th thermal power unit at time t, x it Indicates the operating status of the i-th thermal power unit at the t-th time.

[0017] Optionally, the optimization objective of subproblem 1 is to minimize the total system output cost under the most extreme scenario of potential output of wind farms and photovoltaic power stations, and the optimization objective of subproblem 2 is to minimize the total system wind and light abandonment cost under the most extreme scenario of potential output of wind farms and photovoltaic power stations. The subproblem model is constructed by the optimization objective of subproblem 1, the optimization objective of subproblem 2 and the constraint conditions, including:

[0018] The optimization objective of sub-problem 1 is to minimize the total system output cost under the most extreme scenario of potential output of wind farms and photovoltaic power stations;

[0019] The optimization objective of sub-problem 2 is to minimize the total wind and solar curtailment cost of the system under the most extreme scenario of potential output of wind farms and photovoltaic power stations;

[0020] Set constraints for the optimization objective of sub-problem 1 and the optimization objective of sub-problem 2 respectively;

[0021] The sum of the optimization target of subproblem 1 and the optimization target of subproblem 2 in one operation cycle is used as the subproblem optimization target;

[0022] Constructing a sub-problem model based on the sub-problem optimization objectives and constraint conditions;

[0023] Among them, the constraints include: power balance constraints, demand response power constraints, hydropower unit output constraints, thermal power unit output constraints, nuclear power unit output constraints, energy storage power station output constraints, system peak regulation margin constraints, wind farm actual output constraints, photovoltaic power station actual output constraints, wind abandonment constraints, solar abandonment constraints, wind power potential output uncertainty constraints and photovoltaic potential output uncertainty constraints.

[0024] Optionally, the optimization objective of sub-problem 1 is as follows:

[0025]

[0026] In the formula, is the total system output cost, are the output cost coefficient of the i-th hydropower unit, the i-th thermal power unit, the i-th nuclear power unit, the i-th wind farm, the i-th photovoltaic power station, the i-th energy storage power station and the i-th load demand response cost coefficient in the system respectively; They represent the output of the i-th hydropower unit at the t-th time, the output of the i-th thermal power unit at the t-th time, the output of the i-th nuclear power unit at the t-th time, the output of the i-th wind farm at the t-th time, the abandoned wind power of the i-th wind farm at the t-th time, the output of the i-th photovoltaic power station at the t-th time, the abandoned light power of the i-th photovoltaic power station at the t-th time, the output of the i-th energy storage power station at the t-th time, the i-th load demand response power at the t-th time, the potential output of the i-th wind farm at the t-th time, and the potential output of the i-th photovoltaic power station at the t-th time. N H is the number of hydropower units in the system, N T is the number of thermal power units in the system, i is the unit subscript, N N is the number of nuclear power units in the system, N W is the number of wind farms in the system; N S is the number of photovoltaic power stations in the system, N C is the number of energy storage power stations in the system, ND is the number of loads in the system.

[0027] Optionally, the optimization objective of sub-problem 2 is as follows:

[0028]

[0029] In the formula, is the total wind and solar curtailment cost of the system, are the wind abandonment cost coefficient of the i-th wind farm and the light abandonment cost coefficient of the i-th photovoltaic power station in the system respectively.

[0030] Optionally, the sub-problem optimization objective is as follows:

[0031]

[0032] In the formula, Z S Optimize the objective of the subproblem, t represents the time, and T represents the operation cycle.

[0033] Optionally, determining the maximum deviation between the potential output of the wind farm and the expected value of the potential output includes:

[0034] The wind power output difference is calculated by subtracting the expected potential output of the wind farm from the maximum output of the wind farm. If the wind power output difference or the expected potential output of the wind farm is less than the set wind power ratio, the minimum value between the expected potential output of the wind farm and the wind power output difference is used as the maximum deviation between the potential output of the wind farm and the expected potential output; otherwise, it is determined by the wind power deviation calculation formula.

[0035] Optionally, the wind power setting ratio is calculated as follows:

[0036]

[0037] In the formula, is the wind power interval deviation parameter of the i-th wind farm at the t-th time with a set value interval of (0,1), is the standard deviation of the potential wind power output of the i-th wind farm at the t-th time.

[0038] Optionally, the wind power deviation calculation formula is as follows:

[0039]

[0040] In the formula, is the wind power interval deviation parameter of the i-th wind farm at the t-th time with a set value interval of (0,1), is the standard deviation of the potential wind power output of the i-th wind farm at the t-th time.

[0041] Optionally, determining the maximum deviation between the potential output of the photovoltaic power station and the expected potential output includes:

[0042] The photovoltaic output difference is calculated by subtracting the expected potential output of the photovoltaic power station from the maximum output of the photovoltaic power station. If the photovoltaic output difference or the expected potential output of the photovoltaic power station is less than the photovoltaic set ratio, the minimum value of the expected potential output of the photovoltaic power station and the photovoltaic output difference is used as the maximum deviation between the potential output of the photovoltaic power station and the expected potential output; otherwise, it is determined by the photovoltaic deviation calculation formula.

[0043] Optionally, the photovoltaic setting ratio is calculated as follows:

[0044]

[0045] In the formula, is the standard deviation of the potential output of the ith PV power station at time t, It is the photovoltaic interval deviation parameter of the i-th photovoltaic power station at the t-th time with a manually given value interval of (0,1).

[0046] Optionally, the photovoltaic deviation calculation formula is as follows:

[0047]

[0048] In the formula, is the maximum deviation between the potential output of the photovoltaic power station and the expected potential output, is the standard deviation of the potential output of the ith PV power station at time t, It is the photovoltaic interval deviation parameter of the i-th photovoltaic power station at the t-th time with a manually given value interval of (0,1).

[0049] In another aspect, the present invention further provides a system for constructing a power balance optimization model, comprising:

[0050] The main problem model building module is used to take the minimum total operation and standby cost of the power supply system as the main problem optimization target, and to build the main problem model with the unit operation status and system operation and standby as constraints;

[0051] The sub-problem model construction module is used to minimize the total system output cost under the most extreme scenario of potential output of wind farms and photovoltaic power stations as the optimization target of sub-problem 1, and minimize the total system wind and light abandonment cost under the most extreme scenario of potential output of wind farms and photovoltaic power stations as the optimization target of sub-problem 2. The sub-problem model is constructed by the optimization target of sub-problem 1, the optimization target of sub-problem 2 and the constraint conditions;

[0052] A set determination module is used to determine the uncertainty set to which the potential output of wind power belongs and the uncertainty set to which the potential output of photovoltaic power station belongs, respectively, based on the maximum deviation between the potential output of wind farm and photovoltaic power station and the expected value of the potential output and the 01 variable;

[0053] The combination module is used to construct a power balance optimization model based on the main problem model, the sub-problem model, the uncertainty set to which the wind power potential output belongs, and the uncertainty set to which the photovoltaic potential output belongs.

[0054] Optionally, the main problem model building module is specifically used to:

[0055] The optimization objective is to minimize the total operating reserve cost of the power supply system;

[0056] The constraints are the continuity constraint of unit operation status, the positive reserve constraint of system operation, and the negative reserve constraint of system operation;

[0057] The main problem model is constructed by the optimization objective of the main problem and the continuity constraint of the unit operation state, the positive standby constraint of the system operation, and the negative standby constraint of the system operation.

[0058] Optionally, the optimization objective of the main problem is as follows:

[0059]

[0060] In the formula, Z F is the total system operation backup cost, N T is the number of thermal power units in the system; i is the unit subscript; T is the number of time; t is the time subscript; c TBit is the operating reserve cost of the i-th thermal power unit at time t, x it Indicates the operating status of the i-th thermal power unit at the t-th time.

[0061] Optionally, the sub-problem model building module is specifically used to:

[0062] The optimization objective of sub-problem 1 is to minimize the total system output cost under the most extreme scenario of potential output of wind farms and photovoltaic power stations;

[0063] The optimization objective of sub-problem 2 is to minimize the total wind and solar curtailment cost of the system under the most extreme scenario of potential output of wind farms and photovoltaic power stations;

[0064] Set constraints for the optimization objective of sub-problem 1 and the optimization objective of sub-problem 2 respectively;

[0065] The sum of the optimization target of subproblem 1 and the optimization target of subproblem 2 in one operation cycle is used as the subproblem optimization target;

[0066] Constructing a sub-problem model based on the sub-problem optimization objectives and constraint conditions;

[0067] Among them, the constraints include: power balance constraints, demand response power constraints, hydropower unit output constraints, thermal power unit output constraints, nuclear power unit output constraints, energy storage power station output constraints, system peak regulation margin constraints, wind farm actual output constraints, photovoltaic power station actual output constraints, wind abandonment constraints, solar abandonment constraints, wind power potential output uncertainty constraints and photovoltaic potential output uncertainty constraints.

[0068] Optionally, the optimization objective of sub-problem 1 is as follows:

[0069]

[0070] In the formula, is the total system output cost, are the output cost coefficient of the i-th hydropower unit, the i-th thermal power unit, the i-th nuclear power unit, the i-th wind farm, the i-th photovoltaic power station, the i-th energy storage power station and the i-th load demand response cost coefficient in the system respectively; They represent the output of the i-th hydropower unit at the t-th time, the output of the i-th thermal power unit at the t-th time, the output of the i-th nuclear power unit at the t-th time, the output of the i-th wind farm at the t-th time, the abandoned wind power of the i-th wind farm at the t-th time, the output of the i-th photovoltaic power station at the t-th time, the abandoned light power of the i-th photovoltaic power station at the t-th time, the output of the i-th energy storage power station at the t-th time, the i-th load demand response power at the t-th time, the potential output of the i-th wind farm at the t-th time, and the potential output of the i-th photovoltaic power station at the t-th time. N H is the number of hydropower units in the system, N T is the number of thermal power units in the system, i is the unit subscript, N N is the number of nuclear power units in the system, N W is the number of wind farms in the system; N S is the number of photovoltaic power stations in the system, N C is the number of energy storage power stations in the system, N D is the number of loads in the system.

[0071] Optionally, the optimization objective of sub-problem 2 is as follows:

[0072]

[0073] In the formula, is the total wind and solar curtailment cost of the system, are the wind abandonment cost coefficient of the i-th wind farm and the light abandonment cost coefficient of the i-th photovoltaic power station in the system respectively.

[0074] Optionally, the sub-problem optimization objective is as follows:

[0075]

[0076] In the formula, Z S Optimize the objective of the subproblem, t represents the time, and T represents the operation cycle.

[0077] Optionally, the sub-problem model building module further includes a photovoltaic deviation determination module, which is used to:

[0078] The photovoltaic output difference is calculated by subtracting the expected potential output of the photovoltaic power station from the maximum output of the photovoltaic power station. If the photovoltaic output difference or the expected potential output of the photovoltaic power station is less than the photovoltaic set ratio, the minimum value of the expected potential output of the photovoltaic power station and the photovoltaic output difference is used as the maximum deviation between the potential output of the photovoltaic power station and the expected potential output; otherwise, it is determined by the photovoltaic deviation calculation formula.

[0079] Optionally, the photovoltaic setting ratio is calculated as follows:

[0080]

[0081] In the formula, is the standard deviation of the potential output of the ith PV power station at time t, It is the photovoltaic interval deviation parameter of the i-th photovoltaic power station at the t-th time with a manually given value interval of (0,1).

[0082] Optionally, the photovoltaic deviation calculation formula is as follows:

[0083]

[0084] In the formula, is the maximum deviation between the potential output of the photovoltaic power station and the expected potential output, is the standard deviation of the potential output of the ith PV power station at time t, It is the photovoltaic interval deviation parameter of the i-th photovoltaic power station at the t-th time with a manually given value interval of (0,1).

[0085] Optionally, the sub-problem model building module further includes a wind power deviation determination module, which is used to:

[0086] The wind power output difference is calculated by subtracting the expected potential output of the wind farm from the maximum output of the wind farm. If the wind power output difference or the expected potential output of the wind farm is less than the set wind power ratio, the minimum value between the expected potential output of the wind farm and the wind power output difference is used as the maximum deviation between the potential output of the wind farm and the expected potential output; otherwise, it is determined by the wind power deviation calculation formula.

[0087] Optionally, the wind power setting ratio is calculated as follows:

[0088]

[0089] In the formula, is the wind power interval deviation parameter of the i-th wind farm at the t-th time with a set value interval of (0,1), is the standard deviation of the potential wind power output of the ith wind farm at time t.

[0090] Optionally, the wind power deviation calculation formula is as follows:

[0091]

[0092] In the formula, is the maximum deviation between the potential output of the wind farm and the expected value of the potential output, is the wind power interval deviation parameter of the i-th wind farm at the t-th time with a set value interval of (0,1), is the standard deviation of the potential wind power output of the ith wind farm at time t.

[0093] In another aspect, the present invention further provides a method for optimizing power balance, comprising:

[0094] Obtaining operating parameters of the power supply system;

[0095] The operating parameters of the power supply system are brought into a pre-built power balance optimization model, and the optimization model is solved to obtain the total system cost, the operating status of each thermal power unit, the output of each unit, and the potential output of wind power and photovoltaic power in the most extreme case;

[0096] Wherein, the power balance optimization model is constructed by using the above-mentioned method for constructing a power balance optimization model;

[0097] The power balance optimization model includes: a main problem model, a sub-problem model and an uncertainty set.

[0098] Optionally, the operating parameters of the power supply system are brought into a pre-built power balance optimization model, and the optimization model is solved to obtain the total system cost, the operating status of each thermal power unit, the output of each unit, and the potential output of wind power and photovoltaic power in the most extreme case, including:

[0099] Taking the dual of the inner minimization problem in the subproblem model and converting the subproblem into a mixed integer linear programming model, and forming an optimization problem with the mixed integer linear programming model;

[0100] The Benders decomposition method is used to solve the main problem model and the optimization problem, and the total system cost, the operating status of each thermal power unit, the output of each unit, and the potential output of wind power and photovoltaic power in the most extreme case are obtained;

[0101] Among them, the total system cost is the sum of the total system operating standby cost, the total system output cost, and the total system wind and solar power abandonment cost.

[0102] Optionally, the operating parameters of the power supply system include: maximum output, minimum output, shortest continuous shutdown time, operating reserve cost, output cost, demand response cost, wind abandonment cost, solar abandonment cost, positive reserve rate, negative reserve rate, monthly load upper limit, monthly load lower limit, expected value and standard deviation of potential wind power output of wind farms, and expected value and standard deviation of potential photovoltaic output of photovoltaic power stations.

[0103] In another aspect, the present invention further provides a power balance optimization system, comprising:

[0104] An acquisition module, used to acquire operating parameters of the power supply system;

[0105] A calculation module is used to bring the operating parameters of the power supply system into a pre-built power balance optimization model, solve the optimization model to obtain the total system cost, the operating status of each thermal power unit, the output of each unit, and the potential output of wind power and photovoltaic power in the most extreme case;

[0106] Wherein, the power balance optimization model is constructed by using the above-mentioned method for constructing a power balance optimization model;

[0107] The power balance optimization model includes: a main problem model, a sub-problem model and an uncertainty set.

[0108] Optionally, the computing module is specifically used for:

[0109] Taking the dual of the inner minimization problem in the subproblem model and converting the subproblem into a mixed integer linear programming model, and forming an optimization problem with the mixed integer linear programming model;

[0110] The Benders decomposition method is used to solve the main problem model and the optimization problem, and the total system cost, the operating status of each thermal power unit, the output of each unit, and the potential output of wind power and photovoltaic power in the most extreme case are obtained;

[0111] Among them, the total system cost is the sum of the total system operating standby cost, the total system output cost, and the total system wind and solar power abandonment cost.

[0112] Optionally, the acquisition module is specifically used to:

[0113] Obtain the maximum output, minimum output, shortest continuous shutdown time, operating reserve cost, output cost, demand response cost, wind abandonment cost, solar abandonment cost, positive reserve rate, negative reserve rate, monthly load upper limit, monthly load lower limit, expected value and standard deviation of potential wind power output of wind farms, and expected value and standard deviation of potential photovoltaic output of photovoltaic power stations for each type of unit.

[0114] In another aspect, the present application further provides a computing device, comprising: one or more processors;

[0115] a processor for executing one or more programs;

[0116] When the one or more programs are executed by the one or more processors, a method for constructing a power balance optimization model or a power balance optimization method as described above is implemented.

[0117] On the other hand, the present application also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed, it implements a method for constructing a power balance optimization model or a power balance optimization method as described above.

[0118] Compared with the prior art, the present invention has the following beneficial effects:

[0119] The present invention provides a method for constructing an electric power balance optimization model, including: taking the total operation and standby cost of the power supply system as the main problem optimization target, and constructing the main problem model with the unit operation state and the system operation and standby as constraints; taking the total output cost of the system under the most extreme scenario of the potential output of the wind farm and the photovoltaic power station as the optimization target of sub-problem 1, and taking the total wind and light abandonment cost of the system under the most extreme scenario of the potential output of the wind farm and the photovoltaic power station as the optimization target of sub-problem 2, and constructing the sub-problem model by the sum of the optimization target of sub-problem 1 and the optimization target of sub-problem 2, as well as the output constraint and the supply balance constraint; determining the uncertainty set to which the potential output of the wind farm and the photovoltaic power station belongs and the uncertainty set to which the potential output of the photovoltaic power station belongs respectively by the maximum deviation amount from the potential output expected value and the potential output of the wind farm and the photovoltaic power station and the 01 variable; constructing the electric power balance optimization model by the main problem model, the sub-problem model, the uncertainty set to which the potential output of the wind power station belongs and the uncertainty set to which the potential output of the photovoltaic power station belongs. The present invention realizes the control of the conservatism of the model optimization result by controlling the boundaries of the uncertainty set to which the potential output of the wind power station and the potential output of the photovoltaic power station belong.

[0120] The present invention provides a method for optimizing power balance, including: obtaining the operating parameters of a power supply system; bringing the operating parameters of the power supply system into a pre-constructed power balance optimization model, solving the optimization model to obtain the total system cost, the operating status of each thermal power unit, the output of each unit, and the potential output of wind power and photovoltaic power in the most extreme case; wherein the power balance optimization model is constructed using the above-mentioned method for constructing a power balance optimization model; the power balance optimization model includes: a main problem model, a sub-problem model, and an uncertainty set. The technical solution of the present invention meets the power demand of the system and the demand for new energy consumption as much as possible under extreme scenarios of wind power and photovoltaic potential output, thereby providing a decision-making reference for production planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0121] Figure 1 A flow chart of a method for constructing a power balance optimization model of the present invention;

[0122] Figure 2 A schematic diagram of a power supply and demand balance optimization model framework considering extreme scenarios of the present invention;

[0123] Figure 3 It is a schematic diagram of the curve of the total cost of the IEEE-RTS79 node system of the present invention changing with the deviation parameters of the wind farm and photovoltaic power station intervals. DETAILED DESCRIPTION

[0124] The purpose of the present invention is to provide an optimization model for balancing electricity supply and demand under extreme scenarios of wind power and photovoltaic output, aiming to comprehensively consider the requirements of system operation economy, safety, cleanliness, etc., and solve the above-mentioned problems in the prior art.

[0125] The present invention proposes a construction of a power balance optimization model, a balance optimization method and a system, which mainly consists of a main problem model MP, a sub-problem model SP, an uncertainty set W reflecting the uncertainty of wind power and photovoltaic output W and W S The model can be solved by master-slave iteration.

[0126] Embodiment 1:

[0127] A method for constructing a power balance optimization model, such as Figure 1 As shown, including:

[0128] S1: Taking the minimization of the total operating reserve cost of the power supply system as the main problem optimization objective, the main problem model is constructed with the unit operating status and system operating reserve as constraints;

[0129] S2: The optimization objective of sub-problem 1 is to minimize the total system output cost under the most extreme scenario of potential output of wind farms and photovoltaic power stations, and the optimization objective of sub-problem 2 is to minimize the total system wind and solar curtailment cost under the most extreme scenario of potential output of wind farms and photovoltaic power stations. The sub-problem model is constructed by the optimization objective of sub-problem 1, the optimization objective of sub-problem 2 and the constraint conditions.

[0130] S3: The uncertainty set to which the potential output of wind power and the uncertainty set to which the potential output of photovoltaic power stations belongs are determined by the maximum deviation between the potential output of wind farms and photovoltaic power stations and the expected value of potential output and the 01 variable respectively;

[0131] S4: constructing a power balance optimization model based on the main problem model, the sub-problem model, the uncertainty set to which the wind power potential output belongs, and the uncertainty set to which the photovoltaic potential output belongs.

[0132] Combination Figure 2 The present invention is specifically introduced:

[0133] In S1, the main problem optimization objective is to minimize the total operating reserve cost of the power supply system, and the main problem model is constructed with the unit operating status and system operating reserve as constraints, including:

[0134] A power supply and demand balance optimization model master problem model MP considering extreme scenarios is constructed.

[0135] The main problem model MP focuses on the system operation safety, and the specific steps of model construction are as follows.

[0136] (1) Construct the main problem model MP objective function as follows.

[0137]

[0138] From formula 1, it can be seen that the main problem model MP is a minimization optimization problem, Z F is the optimization target, which means the total system operation standby cost, N T is the number of thermal power units in the system; i is the unit subscript; T is the number of time (the minimum time unit here is usually a week, month, etc.); t is the time subscript (such as the tth week, tth month, etc.); c TBi t is the operating standby cost of the i-th thermal power unit at time t, which is determined according to the actual situation of the thermal power units in the system. The variable in formula 1 is x it , represents the operating status of the i-th thermal power unit at the t-th time, which is a 01 variable, 0 for operation and 1 for shutdown.

[0139] (2) Construct the main problem model MP constraint C F as follows.

[0140] C FIt is a set consisting of Formula 2 to Formula 4.

[0141]

[0142] Formula 2 is the continuity constraint of the unit operation status. it represents the operating status of the i-th thermal power unit at time t, x i,t-1 represents the operating status of the i-th thermal power unit at time t-1, Indicates the t+Dth i -1 time the operating status of the i-th thermal power unit, N T is the number of thermal power units in the system, T is the number of time, i is the unit subscript, D i is the shortest continuous shutdown time of the i-th thermal power unit.

[0143]

[0144] Formula 3 is the system operation positive reserve constraint. T is the number of thermal power units in the system, is the maximum output of the i-th thermal power unit at the t-th moment; x it Indicates the operating status of the i-th thermal power unit at time t; N H is the number of hydropower units in the system; i is the unit subscript; is the maximum output of the i-th hydropower unit at the t-th moment; N N is the number of nuclear power units in the system; is the maximum output of the i-th nuclear power unit at the t-th moment; N W is the number of wind farms in the system; is the maximum output of the i-th wind farm at the t-th moment; N S is the number of photovoltaic power stations in the system; is the maximum output of the ith photovoltaic power station at the tth moment; N C is the number of energy storage power stations in the system; is the maximum output of the i-th energy storage power station at time t; r t up is the system positive reserve rate at time t; is the upper limit of system load at the tth moment; T is the number of moments.

[0145]

[0146] Formula 4 is the negative reserve constraint for system operation. Where i is the unit index, N T is the number of thermal power units in the system, is the minimum output of the ith thermal power unit at time t; x it represents the operating status of the i-th thermal power unit at time t, N His the number of hydropower units in the system; is the minimum output of the i-th hydropower unit at the t-th moment; N N is the number of nuclear power units in the system; is the minimum output of the i-th nuclear power unit at time t; N W is the number of wind farms in the system; is the minimum output of the i-th wind farm at the t-th moment; N S is the number of photovoltaic power stations in the system; is the minimum output of the ith photovoltaic power station at the tth time; N C is the number of energy storage power stations in the system; is the minimum output of the i-th energy storage power station at time t; r t dn is the negative reserve rate of the system at time t; is the lower limit of system load at time t.

[0147] In S2, the optimization objective of subproblem 1 is to minimize the total system output cost under the most extreme scenario of potential output of wind farms and photovoltaic power stations, and the optimization objective of subproblem 2 is to minimize the total system wind and light abandonment cost under the most extreme scenario of potential output of wind farms and photovoltaic power stations. The subproblem model is constructed by the optimization objective of subproblem 1, the optimization objective of subproblem 2 and the constraints, including:

[0148] The optimization objective of sub-problem 1 is to minimize the total system output cost under the most extreme scenario of potential output of wind farms and photovoltaic power stations;

[0149] The optimization objective of sub-problem 2 is to minimize the total wind and solar curtailment cost of the system under the most extreme scenario of potential output of wind farms and photovoltaic power stations;

[0150] Set constraints for the optimization objective of sub-problem 1 and the optimization objective of sub-problem 2 respectively;

[0151] The sum of the optimization target of subproblem 1 and the optimization target of subproblem 2 in one operation cycle is used as the subproblem optimization target;

[0152] Constructing a sub-problem model based on the sub-problem optimization objectives and constraint conditions;

[0153] Among them, the constraints include: power balance constraints, demand response power constraints, hydropower unit output constraints, thermal power unit output constraints, nuclear power unit output constraints, energy storage power station output constraints, system peak regulation margin constraints, wind farm actual output constraints, photovoltaic power station actual output constraints, wind abandonment constraints, solar abandonment constraints, wind power potential output uncertainty constraints and photovoltaic potential output uncertainty constraints.

[0154] A sub-problem model SP of power supply and demand balance optimization model considering extreme scenarios is constructed.

[0155] The subproblem model SP is composed of subproblem models at each moment, wherein the subproblem model at the tth moment is composed of subproblem 1 model SP1 considering economic attributes and subproblem 2 model SP2 considering clean attributes. The specific steps are as follows.

[0156] (1) Construct the sub-problem 1 model SP1 at time t (hereinafter referred to as SP1 model).

[0157] The SP1 model consists of Equation 5 to Equation 18.

[0158]

[0159] Where, the SP1 model is a maximum-minimum nested optimization problem. is the optimization target, which means the total output cost of the system. are the output cost coefficient of the i-th hydropower unit, the i-th thermal power unit, the i-th nuclear power unit, the i-th wind farm, the i-th photovoltaic power station, the i-th energy storage power station and the i-th load demand response cost coefficient in the system, which are determined according to the actual situation of the system; the SP1 model variables are They represent the output of the ith hydropower unit at the t-th moment, the output of the ith thermal power unit at the t-th moment, the output of the ith nuclear power unit at the t-th moment, the output of the ith wind farm at the t-th moment, the abandoned wind power of the ith wind farm at the t-th moment, the output of the ith photovoltaic power station at the t-th moment, the abandoned light power of the ith photovoltaic power station at the t-th moment, the output of the ith energy storage power station at the t-th moment, the ith load demand response power at the t-th moment, the potential output of the ith wind farm at the t-th moment, and the potential output of the ith photovoltaic power station at the t-th moment. They are all continuous variables, and N H is the number of hydropower units in the system, N T is the number of thermal power units in the system, i is the unit subscript, N N is the number of nuclear power units in the system, N W is the number of wind farms in the system; N S is the number of photovoltaic power stations in the system, N C is the number of energy storage power stations in the system, N D is the number of loads in the system.

[0160] st

[0161] Power balance constraints:

[0162]

[0163] Where N H is the number of hydropower units in the system; d itThey represent the output of the i-th hydropower unit at the t-th time, the output of the i-th thermal power unit at the t-th time, the output of the i-th nuclear power unit at the t-th time, the output of the i-th wind farm at the t-th time, the output of the i-th photovoltaic power station at the t-th time, the output of the i-th energy storage power station at the t-th time, and the i-th load demand response power at the t-th time, all of which are continuous variables; is the upper limit of system load at time t.

[0164] Demand response power upper and lower limit constraints:

[0165]

[0166] Where, d it is the i-th load demand response power at the t-th moment; is the i-th load upper limit at time t; i is the load subscript;

[0167] The upper and lower limits of the output of hydropower units:

[0168]

[0169] In the formula, is the minimum output of the i-th hydropower unit at the t-th moment; is the output of the i-th hydropower unit at time t; is the maximum output of the i-th hydropower unit at the t-th time; i is the unit index.

[0170] The upper and lower limits of thermal power unit output:

[0171]

[0172] In the formula, is the minimum output of the i-th thermal power unit at the t-th moment; represents the estimated value of the operating status of the i-th thermal power unit at time t; The output of the i-th thermal power unit at time t; is the maximum output of the i-th thermal power unit at the t-th time; i is the unit index.

[0173] Nuclear power unit output upper and lower limit constraints:

[0174]

[0175] In the formula, is the minimum output of the i-th nuclear power unit at time t; is the output of the i-th nuclear power unit at time t; is the maximum output of the i-th nuclear power unit at the t-th time; i is the unit index.

[0176] The upper and lower limits of energy storage power station output:

[0177]

[0178] In the formula, is the minimum output of the i-th energy storage power station at time t; Output of the i-th energy storage power station at time t; is the maximum output of the i-th energy storage power station at the t-th moment; i is the unit index.

[0179] System peak load margin constraints:

[0180]

[0181] Where N T is the number of thermal power units in the system; is the maximum output of the i-th thermal power unit at the t-th moment; is the minimum output of the i-th thermal power unit at the t-th moment; N represents the estimated value of the operating status of the i-th thermal power unit at time t; C is the number of energy storage power stations in the system; is the maximum output of the i-th energy storage power station at time t; is the minimum output of the i-th energy storage power station at time t; N W is the number of wind farms in the system; is the output of the i-th wind farm at time t; N S is the number of photovoltaic power stations in the system; is the output of the i-th photovoltaic power station at the t-th time; i is the unit index.

[0182] The upper and lower limits of actual wind farm output are as follows:

[0183]

[0184] In the formula, represents the output of the i-th wind farm at time t; The potential output of the i-th wind farm at time t; i is the index of the unit.

[0185] The upper and lower limits of the actual output of photovoltaic power stations:

[0186]

[0187] In the formula, represents the output of the i-th photovoltaic power station at time t; It represents the potential output of the i-th photovoltaic power station at the t-th time; i is the unit index.

[0188] Wind curtailment constraints:

[0189]

[0190] In the formula, represents the output of the i-th wind farm at time t; represents the abandoned wind power of the i-th wind farm at time t; The potential output of the i-th wind farm at time t; i is the index of the unit.

[0191] Abandonment Constraints:

[0192]

[0193] In the formula, represents the output of the i-th photovoltaic power station at time t; represents the abandoned power of the i-th PV power station at time t; It represents the potential output of the i-th photovoltaic power station at the t-th time; i is the unit index.

[0194] Uncertainty constraints on wind power potential output:

[0195]

[0196] In the formula, The potential output of the i-th wind farm at time t; i is the unit index; W W is the uncertainty set to which the potential wind power output belongs.

[0197] Uncertainty constraints on photovoltaic potential output:

[0198]

[0199] In the formula, represents the potential output of the ith photovoltaic power station at time t; W S is the uncertainty set to which the PV potential output belongs; i is the unit subscript.

[0200] (2) Construct the sub-problem 2 model SP2 at time t (hereinafter referred to as SP2 model).

[0201] The objective function of the SP2 model is as follows:

[0202]

[0203] In the formula, is the optimization target, which means the total wind and solar power abandonment cost of the system. are the wind abandonment cost coefficient of the i-th wind farm and the photovoltaic abandonment cost coefficient of the i-th photovoltaic power station in the system, which are determined according to the actual situation of the system; represents the abandoned wind power of the i-th wind farm at time t; represents the abandoned power of the i-th PV power station at time t; dit , They represent the output of the ith hydropower unit at the t-th moment, the output of the ith thermal power unit at the t-th moment, the output of the ith nuclear power unit at the t-th moment, the output of the ith wind farm at the t-th moment, the abandoned wind power of the ith wind farm at the t-th moment, the output of the ith photovoltaic power station at the t-th moment, the abandoned light power of the ith photovoltaic power station at the t-th moment, the output of the ith energy storage power station at the t-th moment, the ith load demand response power at the t-th moment, the potential output of the ith wind farm at the t-th moment, and the potential output of the ith photovoltaic power station at the t-th moment. They are all continuous variables, and N W is the number of wind farms in the system; N S is the number of PV power stations in the system.

[0204] st

[0205] Power balance constraints:

[0206]

[0207] Where N H is the number of hydropower units in the system; d it They represent the output of the i-th hydropower unit at the t-th time, the output of the i-th thermal power unit at the t-th time, the output of the i-th nuclear power unit at the t-th time, the output of the i-th wind farm at the t-th time, the output of the i-th photovoltaic power station at the t-th time, the output of the i-th energy storage power station at the t-th time, and the i-th load demand response power at the t-th time, all of which are continuous variables; is the lower limit of system load at time t.

[0208] Demand response power upper and lower limit constraints:

[0209]

[0210] Where, d it is the i-th load demand response power at time t; is the i-th load lower limit at time t; i is the load subscript.

[0211] The upper and lower limits of the output of hydropower units:

[0212]

[0213] In the formula, is the minimum output of the i-th hydropower unit at the t-th moment; is the output of the i-th hydropower unit at time t; is the maximum output of the i-th hydropower unit at the t-th time; i is the unit index.

[0214] The upper and lower limits of thermal power unit output:

[0215]

[0216] In the formula, is the minimum output of the i-th thermal power unit at time t; represents the estimated value of the operating status of the i-th thermal power unit at time t; The output of the i-th thermal power unit at time t; is the maximum output of the i-th thermal power unit at the t-th time; i is the unit index.

[0217] Nuclear power unit output upper and lower limit constraints:

[0218]

[0219] In the formula, is the minimum output of the i-th nuclear power unit at time t; is the output of the i-th nuclear power unit at time t; is the maximum output of the i-th nuclear power unit at the t-th time; i is the unit index.

[0220] The upper and lower limits of energy storage power station output:

[0221]

[0222] In the formula, is the minimum output of the i-th energy storage power station at time t; Output of the i-th energy storage power station at time t; is the maximum output of the i-th energy storage power station at the t-th moment; i is the unit index.

[0223] System peak load margin constraints:

[0224]

[0225] Where N T is the number of thermal power units in the system; is the maximum output of the i-th thermal power unit at the t-th moment; is the minimum output of the i-th thermal power unit at time t; N represents the estimated value of the operating status of the i-th thermal power unit at time t; C is the number of energy storage power stations in the system; is the maximum output of the i-th energy storage power station at time t; is the minimum output of the i-th energy storage power station at time t; N W is the number of wind farms in the system; is the output of the i-th wind farm at time t; N S is the number of photovoltaic power stations in the system; is the output of the i-th photovoltaic power station at the t-th time; i is the unit index.

[0226] The upper and lower limits of actual wind farm output are as follows:

[0227]

[0228] In the formula, represents the output of the i-th wind farm at time t; The potential output of the i-th wind farm at time t; i is the index of the unit.

[0229] The upper and lower limits of the actual output of photovoltaic power stations:

[0230]

[0231] In the formula, represents the output of the i-th photovoltaic power station at time t; It represents the potential output of the i-th photovoltaic power station at the t-th time; i is the unit index.

[0232] Wind curtailment constraints:

[0233]

[0234] In the formula, represents the output of the i-th wind farm at time t; represents the abandoned wind power of the i-th wind farm at time t; The potential output of the i-th wind farm at time t; i is the index of the unit.

[0235] Abandonment Constraints:

[0236]

[0237] In the formula, represents the output of the i-th photovoltaic power station at time t; represents the abandoned power of the i-th PV power station at time t; It represents the potential output of the i-th photovoltaic power station at the t-th time; i is the unit index.

[0238] Uncertainty constraints on wind power potential output:

[0239]

[0240] In the formula, The potential output of the i-th wind farm at time t; i is the unit index; W W is the uncertainty set to which the potential wind power output belongs.

[0241] Uncertainty constraints on photovoltaic potential output:

[0242]

[0243] In the formula, represents the potential output of the ith photovoltaic power station at time t; W S is the uncertainty set to which the PV potential output belongs; i is the unit subscript.

[0244] The SP2 model is a maximum-minimum nested optimization problem. Formula 19 is the objective function of the SP2 model, and Formulas 20 to 32 are the constraints of the SP2 model.

[0245] (3) Based on the combination of SP1 model and SP2 model, the sub-problem model SP is obtained as follows.

[0246]

[0247] In formula 33, Z S is the SP objective function of the subproblem model, are the optimization target values ​​obtained by solving the SP1 model and the SP2 model respectively.

[0248] The maximum deviation between the potential output of the wind farm and the photovoltaic power station and the expected value of the potential output in S3 and the 01 variable respectively determine the uncertainty set to which the potential output of wind power belongs and the uncertainty set to which the potential output of photovoltaic power belongs, which specifically include:

[0249] Construct the uncertainty set W to which the potential output of wind power belongs W and the uncertainty set W to which the PV potential output belongs S .

[0250] Different from the traditional idea of ​​constructing a probabilistic model of wind power and photovoltaic output, the uncertainty set of wind power potential output and the uncertainty set of photovoltaic potential output proposed in the present invention do not involve the probability distribution of wind power and photovoltaic output, but focus on the boundaries of wind power and photovoltaic output by introducing 01 variables, that is, focus on extreme scenarios such as "maximum" and "minimum" wind power and photovoltaic output or the combination of various extreme scenarios at each moment. Therefore, no matter what probability distribution the wind power and photovoltaic output meet in the actual system, the uncertainty set of wind power potential output and the uncertainty set of photovoltaic potential output proposed in the present invention can enable the main problem model MP and the sub-problem model SP to find the optimal solution at the boundary of uncertainty factors. By controlling the boundaries of the uncertainty set of wind power potential output and the uncertainty set of photovoltaic potential output, the conservative control of the model optimization results can be achieved. Uncertainty set W of wind power potential output W and the uncertainty set W to which the PV potential output belongs S The details are as follows.

[0251]

[0252]

[0253] In Formula 34 and Formula 35, W W is the uncertainty set to which the potential output of wind power belongs, W S is the uncertainty set to which the photovoltaic potential output belongs, is the expected value of the potential wind power output of the i-th wind farm at time t; is the potential wind power output of the i-th wind farm at time t and Maximum deviation; It is a 01 variable. is the expected value of the potential photovoltaic output of the i-th photovoltaic power station at time t; is the potential output of the ith PV power station at time t and Maximum deviation; It is a 01 variable.

[0254] The calculation method is as follows: If formula 36 or formula 37 is satisfied, then use formula 38 for calculation; otherwise, use formula 39 for calculation. is the standard deviation of the potential wind power output of the i-th wind farm at time t, is the wind power interval deviation parameter of the i-th wind farm at the t-th time with a manually given value interval of (0,1). The specific value is determined according to the decision-making preference of the decision-maker. If the decision-maker wants to build a conservative model (i.e., considering the extreme scenario of poor wind power output and greater impact on the balance of power supply and demand), then The value should be close to 1. The closer it is to 1, the more conservative the model is and the worse the extreme scenarios considered.

[0255]

[0256] In the formula, is the expected value of the potential wind power output of the i-th wind farm at time t, is the wind power interval deviation parameter of the i-th wind farm at the t-th time with a set value interval of (0,1), is the standard deviation of the potential wind power output of the i-th wind farm at the t-th time.

[0257]

[0258] In the formula, is the maximum output of the i-th wind farm at time t, is the expected value of the potential wind power output of the i-th wind farm at time t, is the standard deviation of the potential wind power output of the i-th wind farm at time t, is the wind power interval deviation parameter of the i-th wind farm at the t-th time with a set value interval of (0,1).

[0259]

[0260] In the formula, is the maximum deviation between the potential output of the wind farm and the expected value of the potential output, is the expected value of the potential wind power output of the i-th wind farm at time t, is the maximum output of the i-th wind farm at the t-th moment.

[0261]

[0262] In the formula, is the maximum deviation between the potential output of the wind farm and the expected value of the potential output, is the wind power interval deviation parameter of the i-th wind farm at the t-th time with a set value interval of (0,1), is the standard deviation of the potential wind power output of the i-th wind farm at time t

[0263] The calculation method is as follows: If formula 40 or formula 41 is satisfied, then use formula 42 for calculation; otherwise, use formula 43 for calculation. is the standard deviation of the potential output of the ith PV power station at time t, is the photovoltaic interval deviation parameter of the i-th photovoltaic power station at the t-th time with a manually given value interval of (0,1). The specific value is determined according to the decision-making preference of the decision-maker. If the decision-maker wants to build a conservative model (i.e., considering the extreme scenario of poor photovoltaic output and greater impact on the balance of power supply and demand), then The value should be close to 1. The closer it is to 1, the more conservative the model is and the worse the extreme scenarios considered.

[0264]

[0265] In the formula, is the expected value of the potential photovoltaic output of the i-th photovoltaic power station at time t; is the standard deviation of the potential output of the ith PV power station at time t, It is the photovoltaic interval deviation parameter of the i-th photovoltaic power station at the t-th time with a manually given value interval of (0,1).

[0266]

[0267] In the formula, is the maximum output of the ith photovoltaic power station at time t; is the expected value of the potential photovoltaic output of the i-th photovoltaic power station at time t; is the standard deviation of the potential output of the ith PV power station at time t, It is the photovoltaic interval deviation parameter of the i-th photovoltaic power station at the t-th time with a manually given value interval of (0,1).

[0268]

[0269] In the formula, is the potential output of the ith PV power station at time t and Maximum deviation; is the expected value of the potential photovoltaic output of the i-th photovoltaic power station at time t; is the maximum output of the i-th photovoltaic power station at the t-th moment.

[0270]

[0271] In the formula, is the potential output of the ith PV power station at time t and Maximum deviation; is the standard deviation of the potential output of the ith PV power station at time t, It is the photovoltaic interval deviation parameter of the i-th photovoltaic power station at the t-th time with a manually given value interval of (0,1).

[0272] Embodiment 2:

[0273] In another aspect, the present invention further provides a system for constructing a power balance optimization model, comprising:

[0274] The main problem model building module is used to take the minimum total operation and standby cost of the power supply system as the main problem optimization target, and to build the main problem model with the unit operation status and system operation and standby as constraints;

[0275] The sub-problem model building module is used to minimize the total system output cost under the most extreme potential output scenario of wind farms and photovoltaic power stations as the optimization target of sub-problem 1, and minimize the total system wind and light abandonment cost under the most extreme potential output scenario of wind farms and photovoltaic power stations as the optimization target of sub-problem 2. The sub-problem model is constructed by the sum of the optimization target of sub-problem 1 and the optimization target of sub-problem 2, as well as the output constraint and the supply balance constraint;

[0276] A set determination module is used to determine the uncertainty set to which the potential output of wind power belongs and the uncertainty set to which the potential output of photovoltaic power station belongs, respectively, based on the maximum deviation between the potential output of wind farm and photovoltaic power station and the expected value of the potential output and the 01 variable;

[0277] The combination module is used to construct a power balance optimization model based on the main problem model, the sub-problem model, the uncertainty set to which the wind power potential output belongs, and the uncertainty set to which the photovoltaic potential output belongs.

[0278] Optionally, the main problem model building module is specifically used to:

[0279] The optimization objective is to minimize the total operating reserve cost of the power supply system;

[0280] The constraints are the continuity constraint of unit operation status, the positive reserve constraint of system operation, and the negative reserve constraint of system operation;

[0281] The main problem model is constructed by the optimization objective of the main problem and the continuity constraint of the unit operation state, the positive standby constraint of the system operation, and the negative standby constraint of the system operation.

[0282] Optionally, the optimization objective of the main problem is as follows:

[0283]

[0284] In the formula, Z F is the total system operation backup cost, N T is the number of thermal power units in the system; i is the unit subscript; T is the number of time; t is the time subscript; c TBit is the operating reserve cost of the i-th thermal power unit at time t, x it Indicates the operating status of the i-th thermal power unit at the t-th time.

[0285] Optionally, the sub-problem model building module is specifically used to:

[0286] The optimization objective of sub-problem 1 is to minimize the total system output cost under the most extreme scenario of potential output of wind farms and photovoltaic power stations;

[0287] The optimization objective of sub-problem 2 is to minimize the total wind and solar curtailment cost of the system under the most extreme scenario of potential output of wind farms and photovoltaic power stations;

[0288] Set constraints for the optimization objective of sub-problem 1 and the optimization objective of sub-problem 2 respectively;

[0289] The sum of the optimization target of subproblem 1 and the optimization target of subproblem 2 in one operation cycle is used as the subproblem optimization target;

[0290] Constructing a sub-problem model based on the sub-problem optimization objectives and constraint conditions;

[0291] Among them, the constraints include: power balance constraints, demand response power constraints, hydropower unit output constraints, thermal power unit output constraints, nuclear power unit output constraints, energy storage power station output constraints, system peak regulation margin constraints, wind farm actual output constraints, photovoltaic power station actual output constraints, wind abandonment constraints, solar abandonment constraints, wind power potential output uncertainty constraints and photovoltaic potential output uncertainty constraints.

[0292] Optionally, the optimization objective of sub-problem 1 is as follows:

[0293]

[0294] In the formula, is the total system output cost, They are the output cost coefficient of the i-th hydropower unit, the i-th thermal power unit, the i-th nuclear power unit, the i-th wind farm, the i-th photovoltaic power station, the i-th energy storage power station and the i-th load demand response cost coefficient in the system, and they are determined according to the actual situation of the system; d it , They represent the output of the i-th hydropower unit at the t-th time, the output of the i-th thermal power unit at the t-th time, the output of the i-th nuclear power unit at the t-th time, the output of the i-th wind farm at the t-th time, the abandoned wind power of the i-th wind farm at the t-th time, the output of the i-th photovoltaic power station at the t-th time, the abandoned light power of the i-th photovoltaic power station at the t-th time, the output of the i-th energy storage power station at the t-th time, the i-th load demand response power at the t-th time, the potential output of the i-th wind farm at the t-th time, and the potential output of the i-th photovoltaic power station at the t-th time. N H is the number of hydropower units in the system, N T is the number of thermal power units in the system, i is the unit subscript, N N is the number of nuclear power units in the system, N W is the number of wind farms in the system; N S is the number of photovoltaic power stations in the system, N C is the number of energy storage power stations in the system, N D is the number of loads in the system.

[0295] Optionally, the optimization objective of sub-problem 2 is as follows:

[0296]

[0297] In the formula, is the total wind and solar curtailment cost of the system, are the wind abandonment cost coefficient of the i-th wind farm and the photovoltaic abandonment cost coefficient of the i-th photovoltaic power station in the system, d it , They respectively represent the output of the i-th hydropower unit at the t-th moment, the output of the i-th thermal power unit at the t-th moment, the output of the i-th nuclear power unit at the t-th moment, the output of the i-th wind farm at the t-th moment, the wind power abandoned by the i-th wind farm at the t-th moment, the output of the i-th photovoltaic power station at the t-th moment, the solar power abandoned by the i-th photovoltaic power station at the t-th moment, the output of the i-th energy storage power station at the t-th moment, the i-th load demand response power at the t-th moment, the potential output of the i-th wind farm at the t-th moment, and the potential output of the i-th photovoltaic power station at the t-th moment.

[0298] Optionally, the sub-problem optimization objective is as follows:

[0299]

[0300] In the formula, Z S Optimize the objective for the subproblem, is the total wind and solar curtailment cost of the system, is the total output cost of the system, t represents the time, and T represents the operation cycle.

[0301] Optionally, the sub-problem model building module further includes a photovoltaic deviation determination module, which is used to:

[0302] The photovoltaic output difference is calculated by subtracting the expected potential output of the photovoltaic power station from the maximum output of the photovoltaic power station. If the photovoltaic output difference or the expected potential output of the photovoltaic power station is less than the photovoltaic set ratio, the minimum value of the expected potential output of the photovoltaic power station and the photovoltaic output difference is used as the maximum deviation between the potential output of the photovoltaic power station and the expected potential output; otherwise, it is determined by the photovoltaic deviation calculation formula.

[0303] Optionally, the photovoltaic setting ratio is calculated as follows:

[0304]

[0305] In the formula, is the standard deviation of the potential output of the ith PV power station at time t, It is the photovoltaic interval deviation parameter of the i-th photovoltaic power station at the t-th time with a manually given value interval of (0,1).

[0306] Optionally, the photovoltaic deviation calculation formula is as follows:

[0307]

[0308] In the formula, is the maximum deviation between the potential output of the photovoltaic power station and the expected potential output, is the standard deviation of the potential output of the ith PV power station at time t, It is the photovoltaic interval deviation parameter of the i-th photovoltaic power station at the t-th time with a manually given value interval of (0,1).

[0309] Optionally, the sub-problem model building module further includes a wind power deviation determination module, which is used to:

[0310] The wind power output difference is calculated by subtracting the expected potential output of the wind farm from the maximum output of the wind farm. If the wind power output difference or the expected potential output of the wind farm is less than the set wind power ratio, the minimum value between the expected potential output of the wind farm and the wind power output difference is used as the maximum deviation between the potential output of the wind farm and the expected potential output; otherwise, it is determined by the wind power deviation calculation formula.

[0311] Optionally, the wind power setting ratio is calculated as follows:

[0312]

[0313] In the formula, is the wind power interval deviation parameter of the i-th wind farm at the t-th time with a set value interval of (0,1), is the standard deviation of the potential wind power output of the ith wind farm at time t.

[0314] Optionally, the wind power deviation calculation formula is as follows:

[0315]

[0316] In the formula, is the maximum deviation between the potential output of the wind farm and the expected value of the potential output, is the wind power interval deviation parameter of the i-th wind farm at the t-th time with a set value interval of (0,1), is the standard deviation of the potential wind power output of the ith wind farm at time t.

[0317] Embodiment 3:

[0318] In another aspect, the present invention further provides a method for optimizing power balance, comprising:

[0319] Obtaining operating parameters of the power supply system;

[0320] The operating parameters of the power supply system are brought into a pre-built power balance optimization model, and the optimization model is solved to obtain the total system cost, the operating status of each thermal power unit, the output of each unit, and the potential output of wind power and photovoltaic power in the most extreme case;

[0321] Wherein, the power balance optimization model is constructed by using the above-mentioned method for constructing a power balance optimization model;

[0322] The power balance optimization model includes: a main problem model, a sub-problem model and an uncertainty set.

[0323] Optionally, the operating parameters of the power supply system are brought into a pre-built power balance optimization model, and the optimization model is solved to obtain the total system cost, the operating status of each thermal power unit, the output of each unit, and the potential output of wind power and photovoltaic power in the most extreme case, including:

[0324] Taking the dual of the inner minimization problem in the subproblem model and converting the subproblem into a mixed integer linear programming model, and forming an optimization problem with the mixed integer linear programming model;

[0325] The Benders decomposition method is used to solve the main problem model and the optimization problem, and the total system cost, the operating status of each thermal power unit, the output of each unit, and the potential output of wind power and photovoltaic power in the most extreme case are obtained;

[0326] Among them, the total system cost is the sum of the total system operating standby cost, the total system output cost, and the total system wind and solar power abandonment cost.

[0327] Optionally, the operating parameters of the power supply system include: maximum output, minimum output, shortest continuous shutdown time, operating reserve cost, output cost, demand response cost, wind abandonment cost, solar abandonment cost, positive reserve rate, negative reserve rate, monthly load upper limit, monthly load lower limit, expected value and standard deviation of potential wind power output of wind farms, and expected value and standard deviation of potential photovoltaic output of photovoltaic power stations.

[0328] Embodiment 4:

[0329] The present invention proposes an application scenario of an optimization model for power supply and demand balance considering extreme scenarios as follows. The IEEE-RTS79 node system (hereinafter referred to as the test system) is used as an example for explanation, and the research time scale is 12 months.

[0330] (1) Model input data:

[0331] ① Test the maximum output, minimum output, shortest continuous shutdown time, operation standby cost, output cost of each type of unit in the system, test the demand response cost of the system, test the wind abandonment cost and solar abandonment cost of the system. If the actual values ​​of the above data are difficult to obtain, they can be given manually within a reasonable range;

[0332] ② Test the positive and negative reserve rate of the system, which can be 5%;

[0333] ③ Test the upper and lower limits of the system load in each month;

[0334] ④ Test the expected value and standard deviation of the potential wind power output of the wind farm and the expected value and standard deviation of the potential photovoltaic output of the photovoltaic power station;

[0335] (2) Model construction: The main problem model MP and the sub-problem model SP are constructed using the methods given in steps 1 and 2 of the specific example of the technical solution, where the main problem model is a 01 planning model and the sub-problem model is a maximum-minimum nested optimization model;

[0336] (3) Uncertainty set construction: Manually set the wind power interval deviation parameters of the test system wind farm and the photovoltaic interval deviation parameters of the photovoltaic power station, and use the method given in step 3 of the specific example of the technical solution to construct the uncertainty set W to which the wind power potential output belongs W and the uncertainty set W to which the photovoltaic potential output belongs S ;

[0337] (4) Model solution:

[0338] ① Take the dual of the inner minimization problem in the subproblem model SP and transform the subproblem model SP into a mixed integer linear programming model;

[0339] ② The Benders decomposition method is used to solve the optimization problem composed of the main problem model MP and the sub-problem model SP, and the total cost of the test system (i.e. the sum of the total operating reserve cost of the system, the total output cost of the system, and the total wind and solar power abandonment cost of the system) is obtained, as well as the operating status of each thermal power unit in the test system, the output of each unit, and the potential output of wind power and photovoltaic power under the most extreme conditions.

[0340] (5) Result analysis:

[0341] The operating status of each thermal power unit and the output of each unit obtained by solving the solution are the operating modes recommended by the test system given by the model of the present invention. This operating mode can meet the power demand and new energy consumption demand of the test system as much as possible under the consideration of extreme scenarios of potential wind power and photovoltaic output (such as load peak and extremely small wind power / photovoltaic output, load valley and extremely large wind power / photovoltaic output, etc.), and can provide decision-making reference for the production plan arrangement of power grid companies.

[0342] Modify the wind power interval deviation parameters of the test system wind farm and the photovoltaic interval deviation parameters of the photovoltaic power station in the interval (0,1), and re-solve the model according to the method mentioned in "(4) Model Solution:" to obtain the total system cost under different interval deviation parameters. Assume that the interval deviation parameters of each wind farm and photovoltaic power station are the same, and gradually increase from 0 to 0.9, and draw the curve of the total system cost changing with the interval deviation parameters as shown in Figure 3As shown, the larger the interval deviation parameter is, the wider the boundary of the uncertainty set to which the potential wind power output belongs and the uncertainty set to which the potential photovoltaic output belongs is, the more extreme the wind power and photovoltaic output scenarios considered are, the greater the risk faced by the power supply and demand balance of the test system is, and the higher the corresponding total system cost is. The increase in the total system cost can be regarded as the additional price paid by the system to cope with extreme wind power / photovoltaic output scenarios. Therefore, the total system cost under different interval deviation parameter conditions given by the optimization model proposed in the present invention can provide decision-making references for decision makers of power grid companies on the overall balance between the economy, safety and cleanliness of the power grid.

[0343] Embodiment 5:

[0344] In another aspect, the present invention further provides a power balance optimization system, comprising:

[0345] An acquisition module, used to acquire operating parameters of the power supply system;

[0346] A calculation module is used to bring the operating parameters of the power supply system into a pre-built power balance optimization model, solve the optimization model to obtain the total system cost, the operating status of each thermal power unit, the output of each unit, and the potential output of wind power and photovoltaic power in the most extreme case;

[0347] Wherein, the power balance optimization model is constructed by using the above-mentioned method for constructing a power balance optimization model;

[0348] The power balance optimization model includes: a main problem model, a sub-problem model and an uncertainty set.

[0349] Optionally, the computing module is specifically used for:

[0350] Taking the dual of the inner minimization problem in the subproblem model and converting the subproblem into a mixed integer linear programming model, and forming an optimization problem with the mixed integer linear programming model;

[0351] The Benders decomposition method is used to solve the main problem model and the optimization problem, and the total system cost, the operating status of each thermal power unit, the output of each unit, and the potential output of wind power and photovoltaic power in the most extreme case are obtained;

[0352] Among them, the total system cost is the sum of the total system operating standby cost, the total system output cost, and the total system wind and solar power abandonment cost.

[0353] Optionally, the acquisition module is specifically used to:

[0354] Obtain the maximum output, minimum output, shortest continuous shutdown time, operating reserve cost, output cost, demand response cost, wind abandonment cost, solar abandonment cost, positive reserve rate, negative reserve rate, monthly load upper limit, monthly load lower limit, expected value and standard deviation of potential wind power output of wind farms, and expected value and standard deviation of potential photovoltaic output of photovoltaic power stations for each type of unit.

[0355] Embodiment 6:

[0356] Based on the same inventive concept, the present invention also provides a computer device, which includes a processor and a memory, the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions in a computer storage medium to implement a corresponding method flow or a corresponding function, so as to implement a method for constructing a power balance optimization model or a step of a power balance optimization method in the above embodiment.

[0357] Embodiment 7:

[0358] Based on the same inventive concept, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It is understandable that the computer-readable storage medium here may include both a built-in storage medium in a computer device and an extended storage medium supported by the computer device. The computer-readable storage medium provides a storage space that stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions may be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here may be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor may load and execute one or more instructions stored in the computer-readable storage medium to implement a method for constructing a power balance optimization model or a step of a power balance optimization method in the above-mentioned embodiment.

[0359] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented 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.

[0360] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0361] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0362] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0363] 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 in the scope of the claims of the present invention to be approved.

Claims

1. A method for constructing a power balance optimization model, characterized in that: include: Taking the minimization of the total operation and reserve cost of the power supply system as the optimization objective of the main problem, the main problem model is constructed with the unit operation status and system operation and reserve as constraints; The optimization objective of sub-problem 1 is to minimize the total system output cost under the most extreme scenario of potential output of wind farms and photovoltaic power stations, and the optimization objective of sub-problem 2 is to minimize the total system wind and solar curtailment cost under the most extreme scenario of potential output of wind farms and photovoltaic power stations. The sub-problem model is constructed by the optimization objective of sub-problem 1, the optimization objective of sub-problem 2 and the constraint conditions. The maximum deviation between the potential output of the wind farm and the expected value of the potential output of the photovoltaic power station and the uncertainty set to which the wind power potential output belongs and the uncertainty set to which the photovoltaic potential output belongs are determined respectively by the 01 variable; A power balance optimization model is constructed by the main problem model, the sub-problem model, the uncertainty set to which the potential output of wind power belongs, and the uncertainty set to which the potential output of photovoltaic power belongs.

2. The method according to claim 1, characterized in that The main problem model is constructed with the minimum total operation and standby cost of the power supply system as the optimization target and the unit operation status and system operation and standby as constraints, including: The optimization objective is to minimize the total operating reserve cost of the power supply system; The constraints are the continuity constraint of unit operation status, the positive reserve constraint of system operation, and the negative reserve constraint of system operation; The main problem model is constructed by the optimization objective of the main problem and the continuity constraint of the unit operation state, the positive standby constraint of the system operation, and the negative standby constraint of the system operation.

3. The method according to claim 2, characterized in that The optimization objective of the main problem is as follows: In the formula, Z F is the total system operation backup cost, N T is the number of thermal power units in the system; i is the unit subscript; T is the number of time; t is the time subscript; c TBit is the operating reserve cost of the i-th thermal power unit at time t, x it Indicates the operating status of the i-th thermal power unit at the t-th time.

4. The method according to claim 1, characterized in that The optimization objective of subproblem 1 is to minimize the total system output cost under the most extreme scenario of potential output of wind farms and photovoltaic power stations, and the optimization objective of subproblem 2 is to minimize the total system wind and light abandonment cost under the most extreme scenario of potential output of wind farms and photovoltaic power stations. The subproblem model is constructed by the optimization objective of subproblem 1, the optimization objective of subproblem 2 and the constraint conditions, including: The optimization objective of sub-problem 1 is to minimize the total system output cost under the most extreme scenario of potential output of wind farms and photovoltaic power stations; The optimization objective of sub-problem 2 is to minimize the total wind and solar curtailment cost of the system under the most extreme scenario of potential output of wind farms and photovoltaic power stations; Set constraints for the optimization objective of sub-problem 1 and the optimization objective of sub-problem 2 respectively; The sum of the optimization target of subproblem 1 and the optimization target of subproblem 2 in one operation cycle is used as the subproblem optimization target; Constructing a sub-problem model based on the sub-problem optimization objectives and constraint conditions; Among them, the constraints include: power balance constraints, demand response power constraints, hydropower unit output constraints, thermal power unit output constraints, nuclear power unit output constraints, energy storage power station output constraints, system peak regulation margin constraints, wind farm actual output constraints, photovoltaic power station actual output constraints, wind abandonment constraints, solar abandonment constraints, wind power potential output uncertainty constraints and photovoltaic potential output uncertainty constraints.

5. The method according to claim 4, characterized in that The optimization objective of sub-problem 1 is as follows: In the formula, is the total system output cost, are the output cost coefficient of the i-th hydropower unit, the i-th thermal power unit, the i-th nuclear power unit, the i-th wind farm, the i-th photovoltaic power station, the i-th energy storage power station and the i-th load demand response cost coefficient in the system respectively; They represent the output of the i-th hydropower unit at the t-th time, the output of the i-th thermal power unit at the t-th time, the output of the i-th nuclear power unit at the t-th time, the output of the i-th wind farm at the t-th time, the abandoned wind power of the i-th wind farm at the t-th time, the output of the i-th photovoltaic power station at the t-th time, the abandoned light power of the i-th photovoltaic power station at the t-th time, the output of the i-th energy storage power station at the t-th time, the i-th load demand response power at the t-th time, the potential output of the i-th wind farm at the t-th time, and the potential output of the i-th photovoltaic power station at the t-th time. N H is the number of hydropower units in the system, N T is the number of thermal power units in the system, i is the unit subscript, N N is the number of nuclear power units in the system, N W is the number of wind farms in the system; N S is the number of photovoltaic power stations in the system, N C is the number of energy storage power stations in the system, N D is the number of loads in the system.

6. The method according to claim 5, characterized in that The optimization objective of sub-problem 2 is as follows: In the formula, is the total wind and solar curtailment cost of the system, are the wind abandonment cost coefficient of the i-th wind farm and the light abandonment cost coefficient of the i-th photovoltaic power station in the system respectively.

7. The method according to claim 6, characterized in that The optimization objective of the sub-problem is as follows: In the formula, Z S Optimize the objective of the subproblem, t represents the time, and T represents the operation cycle.

8. The method according to claim 1, characterized in that The determination of the maximum deviation between the potential output of the wind farm and the expected value of the potential output includes: The wind power output difference is calculated by subtracting the expected potential output of the wind farm from the maximum output of the wind farm. If the wind power output difference or the expected potential output of the wind farm is less than the set wind power ratio, the minimum value between the expected potential output of the wind farm and the wind power output difference is used as the maximum deviation between the potential output of the wind farm and the expected potential output; otherwise, it is determined by the wind power deviation calculation formula.

9. The method according to claim 8, characterized in that The wind power setting ratio is calculated as follows: In the formula, is the wind power interval deviation parameter of the i-th wind farm at the t-th time with a set value interval of (0,1), is the standard deviation of the potential wind power output of the ith wind farm at time t.

10. The method according to claim 8, characterized in that The wind power deviation calculation formula is as follows: In the formula, is the maximum deviation between the potential output of the wind farm and the expected value of the potential output, is the wind power interval deviation parameter of the i-th wind farm at the t-th time with a set value interval of (0,1), is the standard deviation of the potential wind power output of the ith wind farm at time t.

11. The method according to claim 1, characterized in that The determination of the maximum deviation between the potential output of the photovoltaic power station and the expected value of the potential output includes: The photovoltaic output difference is calculated by subtracting the expected potential output of the photovoltaic power station from the maximum output of the photovoltaic power station. If the photovoltaic output difference or the expected potential output of the photovoltaic power station is less than the photovoltaic set ratio, the minimum value of the expected potential output of the photovoltaic power station and the photovoltaic output difference is used as the maximum deviation between the potential output of the photovoltaic power station and the expected potential output; otherwise, it is determined by the photovoltaic deviation calculation formula.

12. The method according to claim 11, characterized in that The photovoltaic setting ratio is calculated as follows: In the formula, is the standard deviation of the potential output of the ith PV power station at time t, It is the photovoltaic interval deviation parameter of the i-th photovoltaic power station at the t-th time with a manually given value interval of (0,1).

13. The method according to claim 11, characterized in that The photovoltaic deviation calculation formula is as follows: In the formula, is the maximum deviation between the potential output of the photovoltaic power station and the expected value of the potential output, is the standard deviation of the potential output of the ith PV power station at time t, It is the photovoltaic interval deviation parameter of the i-th photovoltaic power station at the t-th time with a manually given value interval of (0,1).

14. A system for constructing a power balance optimization model, characterized in that: include: The main problem model building module is used to take the minimum total operation and standby cost of the power supply system as the main problem optimization target, and to build the main problem model with the unit operation status and system operation and standby as constraints; The sub-problem model construction module is used to minimize the total system output cost under the most extreme scenario of potential output of wind farms and photovoltaic power stations as the optimization target of sub-problem 1, and minimize the total system wind and light abandonment cost under the most extreme scenario of potential output of wind farms and photovoltaic power stations as the optimization target of sub-problem 2. The sub-problem model is constructed by the optimization target of sub-problem 1, the optimization target of sub-problem 2 and the constraint conditions; A set determination module is used to determine the uncertainty set to which the potential output of wind power belongs and the uncertainty set to which the potential output of photovoltaic power station belongs, respectively, based on the maximum deviation between the potential output of wind farm and photovoltaic power station and the expected value of the potential output and the 01 variable; The combination module is used to construct a power balance optimization model based on the main problem model, the sub-problem model, the uncertainty set to which the wind power potential output belongs, and the uncertainty set to which the photovoltaic potential output belongs.

15. A method for optimizing power balance, characterized in that: include: Obtaining operating parameters of the power supply system; The operating parameters of the power supply system are brought into a pre-built power balance optimization model, and the optimization model is solved to obtain the total system cost, the operating status of each thermal power unit, the output of each unit, and the potential output of wind power and photovoltaic power in the most extreme case; Wherein, the power balance optimization model is constructed by using a method for constructing a power balance optimization model as described in any one of claims 1 to 13; The power balance optimization model includes: a main problem model, a sub-problem model and an uncertainty set.

16. The method according to claim 15, characterized in that The operating parameters of the power supply system are brought into a pre-built power balance optimization model, and the optimization model is solved to obtain the total system cost, the operating status of each thermal power unit, the output of each unit, and the potential output of wind power and photovoltaic power in the most extreme case, including: Taking the inner minimization problem in the subproblem model as dual and converting the subproblem into a mixed integer linear programming model; The Benders decomposition method is used to solve the optimization problem composed of the main problem model and the mixed integer linear programming model, and the total system cost, the operating status of each thermal power unit, the output of each unit, and the potential output of wind power and photovoltaic power in the most extreme case are obtained; Among them, the total system cost is the sum of the total system operating standby cost, the total system output cost, and the total system wind and solar power abandonment cost.

17. A power balance optimization system, characterized in that: include: An acquisition module, used to acquire operating parameters of the power supply system; A calculation module is used to bring the operating parameters of the power supply system into a pre-built power balance optimization model, solve the optimization model to obtain the total system cost, the operating status of each thermal power unit, the output of each unit, and the potential output of wind power and photovoltaic power in the most extreme case; Wherein, the power balance optimization model is constructed by using a method for constructing a power balance optimization model as described in any one of claims 1 to 13; The power balance optimization model includes: a main problem model, a sub-problem model and an uncertainty set.

18. A computer device, characterized in that: include: one or more processors; The processor is used to store one or more programs; When the one or more programs are executed by the one or more processors, a method for constructing a power balance optimization model as described in any one of claims 1 to 13 or a method for optimizing power supply and demand balance as described in claims 15 to 16 is implemented.

19. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed, it implements a method for constructing a power balance optimization model as described in any one of claims 1 to 13 or a method for optimizing power supply and demand balance as described in claims 15 to 16.

Images