Method, device, equipment and medium for evaluating dispatching capability of integrated energy system

Abstract

The present invention relates to the technical field of power system optimization and dispatching, and in particular to a method, device, equipment and medium for evaluating the dispatching capacity of an integrated energy system, the method comprising: establishing a robust aggregation model of an integrated energy system including conventional generator sets, renewable energy generator sets and battery energy storage; determining the maximum and minimum power requirements of the power system according to the robust aggregation model; taking the maximization of the feasible domain of integrated energy aggregation in the day-ahead dispatching stage as the objective function, taking the output power constraints and ramp constraints of conventional generator sets, the output power constraints of renewable energy generator sets, the charging and discharging power constraints of the battery energy storage system and the system power balance constraints as constraints; solving the robust aggregation model with the objective function and the constraints to obtain the dispatching capacity evaluation result. In the present invention, the adjustable power domain of the integrated energy system can be obtained, the system flexibility can be improved, the amount of wind and solar power abandonment can be reduced, and the goal of economic dispatching of the power system can be achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system optimization and dispatching, and in particular to a method, device, equipment and medium for evaluating the dispatching capability of an integrated energy system. Background Art

[0002] In recent years, a large number of distributed resources have appeared in the power grid system. Their randomness, volatility and over-dispersion have increased the complexity of the system and the difficulty of regulation, thus posing a challenge to the safe and reliable operation of the power grid. Considering that the power system directly dispatches and manages these heterogeneous, decentralized, and diverse random power sources and flexible resources, it not only fails to bring high economic benefits to both parties, but also creates many technical difficulties in stable operation. Therefore, an effective distributed energy management method is urgently needed.

[0003] Resource aggregation has no specific constraints on the geographical location and operating characteristics of distributed energy, and provides a new distributed energy management method with high flexibility and adaptability for the power system. The integrated energy system can aggregate various decentralized source-load-storage resources to form resource clusters and conduct unified scheduling, thereby effectively exerting the combined force of decentralized resources such as distributed power sources and energy storage systems, and improving the flexibility, safety and economy of power grid regulation.

[0004] The effective aggregation of distributed resources is the key technology to achieve efficient operation of integrated energy systems. Aggregation refers to the behavior of aggregating distributed energy in the same region and across regions for the purpose of pursuing economic benefits and grid interaction. This aggregation is information aggregation rather than physical aggregation in the traditional sense. As a bridge between the grid management system and distributed energy, the integrated energy system needs to aggregate the operating characteristics of distributed energy, such as operating status, operating cost and operating power, and then participate in the power market or grid dispatch together with traditional power plants.

[0005] In the prior art, scholars have conducted relevant research on the optimization and scheduling of integrated energy systems with resource aggregation, but there are few studies that consider the adjustable capacity of integrated energy systems. Therefore, how to study the adjustable capacity of integrated energy systems is an urgent problem that technicians in this field need to solve.

[0006] The information disclosed in this background technology section is only intended to deepen the understanding of the overall background technology of the present invention, and should not be regarded as acknowledging or suggesting in any form that the information constitutes the prior art already known to those skilled in the art. Summary of the invention

[0007] The present invention provides a method, device, equipment and medium for evaluating the dispatching capability of an integrated energy system, thereby effectively solving the problems in the background technology.

[0008] In order to achieve the above object, the technical solution adopted by the present invention is: a method for evaluating the dispatching capacity of an integrated energy system, comprising the following steps:

[0009] Establish a robust aggregation model of an integrated energy system including conventional generators, renewable energy generators, and battery energy storage;

[0010] Determining the maximum and minimum power demands of the power system according to the robust aggregation model;

[0011] The objective function is to maximize the feasible domain of comprehensive energy aggregation in the day-ahead dispatching stage, and the constraints are the output power constraints and ramp constraints of conventional generator sets, the output power constraints of renewable energy units, the charging and discharging power constraints of the battery energy storage system, and the system power balance constraints.

[0012] The robust aggregation model is solved using the objective function and constraint conditions to obtain a scheduling capability evaluation result.

[0013] Furthermore, the objective function includes:

[0014] ;

[0015] ;

[0016] , ;

[0017] In the formula, represents the maximum feasible domain of comprehensive energy aggregation, , Represents the weight coefficient of the upper and lower limits of power at time t, , represents the maximum and minimum power of the robust aggregation model at time t, represents the set of scheduling periods, represents the system power consumption at time t, Represents uncertain set constraints , represents the slack variable of the energy balance constraint at time t, Represents a collection of conventional generator sets, Represents the set of renewable energy units, represents the collection of combined heat and power units, Represents a collection of gas boilers, represents the collection of absorption chillers, Represents the electric boiler set, Represents a collection of chillers, represents the first-stage decision variable, represents the second-stage decision variable, represents the output power of device i at time t, represents the kth combination factor of the ith CHP, represents the charging power of the ith battery at time t, represents the discharge power of the ith battery at time t, Represents the energy stored in the i-th battery at time t.

[0018] Furthermore, the uncertain set constraints include:

[0019] ;

[0020] In the formula, represents the minimum total power emitted by the system during period t, Represents the maximum total power emitted by the system during period t.

[0021] Furthermore, the conventional generator set output power constraint and ramp constraint include:

[0022] ;

[0023] In the formula, represents the lower limit of the output of the i-th unit, represents the output upper limit of the i-th unit, represents the ramp-down rate of the i-th unit, Represents the ramp-up rate of the i-th thermal power unit.

[0024] Furthermore, the conventional generator set output power constraint and ramp constraint also include representing the operating area of ​​each cogeneration power plant as a convex combination of extreme feature points, using a convex combination of polygonal feasible region vertices to describe the operating area of ​​the cogeneration unit CHP, and obtaining the cogeneration unit operation constraint:

[0025] ;

[0026] ;

[0027] In the formula, represents the lower limit of the output of the i-th unit, represents the lower limit of the output of the i-th unit, represents the kth combination factor of the i-th CHP, The extreme value set of the operable area of ​​the i-th CHP, represents the kth extreme value of power generation within the operable area of ​​the i-th CHP, The heat output of the i-th CHP in period t is: The kth extreme value of thermal power generation in the operable area of ​​the i-th CHP.

[0028] Furthermore, the renewable energy unit output power constraint includes:

[0029] ;

[0030] In the formula, represents the output power of device i at time t, Represents the predicted output value for the day before.

[0031] Furthermore, the battery energy storage system charging and discharging power constraints include:

[0032] ;

[0033]

[0034] ;

[0035] In the formula, represents the minimum energy stored in the ith battery, represents the maximum energy stored in the ith battery, represents the maximum charging power of the ith battery, The maximum discharge power of the i-th battery, The charging efficiency of the i-th battery, The discharge efficiency of the ith battery, The i-th battery loss rate.

[0036] Furthermore, the system power balance constraint includes:

[0037] ;

[0038] ;

[0039] ;

[0040] In the formula, represents the power load predicted on the day before, represents the thermal load predicted on the day before, Represents the cooling load predicted for the day ahead.

[0041] Further, solving the robust aggregation model comprises the following steps:

[0042] First, ignore the influence of uncertain variables in the main problem and solve it as a deterministic optimization problem;

[0043] Using the column and constraint generation algorithm, on the basis of solving the deterministic optimization problem, we continuously add harsh scenarios and corresponding sub-problem decision variables and constraints to continuously improve the upper and lower bounds of the objective function until the algorithm converges and obtains the scheduling capacity evaluation result.

[0044] The present invention also includes a device for evaluating the dispatching capability of an integrated energy system, using the above method, the device comprising:

[0045] Modeling unit, to establish a robust aggregation model of the integrated energy system including conventional generators, renewable energy generators, and battery energy storage;

[0046] A demand unit, used for determining the maximum and minimum power demands of the power system according to the robust aggregation model;

[0047] The target constraint unit is used to maximize the feasible domain of comprehensive energy aggregation in the day-ahead scheduling phase as the objective function, and to use the output power constraint and ramp constraint of conventional generator sets, the output power constraint of renewable energy units, the charging and discharging power constraint of the battery energy storage system, and the system power balance constraint as constraint conditions;

[0048] A solving unit is used to solve the robust aggregation model with the objective function and constraint conditions to obtain a scheduling capability evaluation result.

[0049] The present invention also includes a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method described above is implemented.

[0050] The present invention also includes a storage medium on which a computer program is stored. When the computer program is executed by a processor, the method described above is implemented.

[0051] The beneficial effects of the present invention are as follows: by establishing a robust aggregation model of an integrated energy system including conventional generator sets, renewable energy generator sets and battery energy storage, the maximum and minimum power demands of the system are determined, and the maximization of the feasible domain of integrated energy aggregation in the day-ahead scheduling stage is used as the objective function, and its robustness is ensured, with the output power constraints and ramp constraints of conventional generator sets, the output power constraints of renewable energy generator sets, the charging and discharging power constraints of the battery energy storage system and the system power balance constraints as constraints. An integrated energy system dispatching capability evaluation model is established, and finally solving the model can obtain the adjustable power domain of the integrated energy system, improve system flexibility, reduce the amount of wind and solar power abandonment, and achieve the goal of economic dispatch of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0053] Figure 1 is a flow chart of the method in Example 1;

[0054] Figure 2 It is a structural schematic diagram of the device in Example 1;

[0055] Figure 3 is a flow chart of the method in Example 2;

[0056] Figure 4 It is a structural schematic diagram of the computer device of the present invention. DETAILED DESCRIPTION

[0057] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0058] Embodiment 1:

[0059] like Figure 1 As shown: A method for evaluating the dispatching capability of an integrated energy system comprises the following steps:

[0060] Establish a robust aggregation model of an integrated energy system including conventional generators, renewable energy generators, and battery energy storage;

[0061] Determine the maximum and minimum power demand of the power system based on the robust aggregation model;

[0062] The objective function is to maximize the feasible domain of comprehensive energy aggregation in the day-ahead dispatching stage, and the constraints are the output power constraints and ramp constraints of conventional generator sets, the output power constraints of renewable energy units, the charging and discharging power constraints of the battery energy storage system, and the system power balance constraints.

[0063] The robust aggregation model is solved with objective function and constraints to obtain the dispatch capability evaluation result.

[0064] By establishing a robust aggregation model of an integrated energy system containing conventional generators, renewable energy generators and battery energy storage, the maximum and minimum power demands of the system are determined, and the objective function is to maximize the feasible domain of integrated energy aggregation in the day-ahead dispatching stage, and to ensure its robustness, with the output power constraints and ramp constraints of conventional generators, the output power constraints of renewable energy generators, the charging and discharging power constraints of the battery energy storage system and the system power balance constraints as constraints. An integrated energy system dispatching capability evaluation model is established, and finally solving the model can obtain the adjustable power domain of the integrated energy system, improve system flexibility, reduce the amount of wind and solar power abandonment, and achieve the goal of economic dispatching of the power system.

[0065] In this embodiment, the objective function includes:

[0066] ;

[0067] ;

[0068] , ;

[0069] In the formula, represents the maximum feasible domain of comprehensive energy aggregation, , Represents the weight coefficient of the upper and lower limits of power at time t, , represents the maximum and minimum power of the robust aggregation model at time t, represents the set of scheduling periods, represents the system power consumption at time t, Represents uncertain set constraints , represents the slack variable of the energy balance constraint at time t, Represents a collection of conventional generator sets, Represents the set of renewable energy units, represents the collection of combined heat and power units, Represents a collection of gas boilers, represents the collection of absorption chillers, Represents the electric boiler set, Represents a collection of chillers, represents the first-stage decision variable, represents the second-stage decision variable, represents the output power of device i at time t, represents the kth combination factor of the ith CHP, represents the charging power of the ith battery at time t, represents the discharge power of the ith battery at time t, Represents the energy stored in the i-th battery at time t.

[0070] Among them, the uncertain set constraints include:

[0071] ;

[0072] In the formula, represents the minimum total power emitted by the system during period t, Represents the maximum total power emitted by the system during period t.

[0073] Conventional generator set output power constraints and ramp constraints include:

[0074] ;

[0075] In the formula, represents the lower limit of the output of the i-th unit, represents the output upper limit of the i-th unit, represents the ramp-down rate of the i-th unit, Represents the ramp-up rate of the i-th thermal power unit.

[0076] In this embodiment, the conventional generator set output power constraint and ramp constraint also include representing the operating area of ​​each cogeneration power plant as a convex combination of extreme feature points, using a convex combination of polygonal feasible region vertices to describe the operating area of ​​the cogeneration unit CHP, and obtaining the cogeneration unit operation constraint:

[0077] ;

[0078] ;

[0079] In the formula, represents the lower limit of the output of the i-th unit, represents the lower limit of the output of the i-th unit, represents the kth combination factor of the i-th CHP, The extreme value set of the operable area of ​​the i-th CHP, represents the kth extreme value of power generation within the operable area of ​​the i-th CHP, The heat output of the i-th CHP in period t is: The kth extreme value of thermal power generation in the operable area of ​​the i-th CHP.

[0080] The output power constraints of renewable energy units include:

[0081] ;

[0082] In the formula, represents the output power of device i at time t, Represents the predicted output value for the day before.

[0083] Among them, the charging and discharging power constraints of the battery energy storage system include:

[0084] ;

[0085]

[0086] ;

[0087] In the formula, represents the minimum energy stored in the ith battery, represents the maximum energy stored in the ith battery, represents the maximum charging power of the ith battery, The maximum discharge power of the i-th battery, The charging efficiency of the i-th battery, The discharge efficiency of the ith battery, The i-th battery loss rate.

[0088] As a preference of the above embodiment, the system power balance constraint includes:

[0089] ;

[0090] ;

[0091] ;

[0092] In the formula, represents the power load predicted on the day before, represents the thermal load predicted on the day before, Represents the cooling load predicted for the day ahead.

[0093] Solving the robust aggregation model includes the following steps:

[0094] First, ignore the influence of uncertain variables in the main problem and solve it as a deterministic optimization problem;

[0095] Using the column and constraint generation algorithm, on the basis of solving the deterministic optimization problem, we continuously add harsh scenarios and corresponding sub-problem decision variables and constraints to continuously improve the upper and lower bounds of the objective function until the algorithm converges and obtains the scheduling capacity evaluation result.

[0096] like Figure 2 As shown, this embodiment also includes a device for evaluating the dispatching capability of an integrated energy system, using the above method, the device includes:

[0097] Modeling unit, to establish a robust aggregation model of the integrated energy system including conventional generators, renewable energy generators, and battery energy storage;

[0098] A demand unit, used to determine the maximum and minimum power demands of the power system based on a robust aggregation model;

[0099] The target constraint unit is used to maximize the feasible domain of comprehensive energy aggregation in the day-ahead scheduling phase as the objective function, and to use the output power constraint and ramp constraint of conventional generator sets, the output power constraint of renewable energy units, the charging and discharging power constraint of the battery energy storage system, and the system power balance constraint as constraint conditions;

[0100] The solving unit is used to solve the robust aggregation model with the objective function and constraint conditions to obtain the dispatch capability evaluation result.

[0101] Embodiment 2:

[0102] The embodiment of the present invention provides a method for evaluating the adjustable capacity of an integrated energy system. Figure 3 , Figure 3 : is a flow chart of an optimization scheduling method for an integrated energy system provided in an embodiment of the present invention. The optimization scheduling method may include:

[0103] S110. Establish a robust aggregation model of an integrated energy system including conventional power generation units, renewable energy units and battery energy storage.

[0104] In the embodiment of the present invention, battery energy storage is connected to the power grid, and conventional generator sets and renewable energy generator sets are coordinated to participate in system scheduling, so that the integrated energy system can give full play to the operating characteristics of battery energy storage and achieve complementary advantages in the scheduling process. Among them, battery energy storage is a commonly used energy storage device at home and abroad. Battery energy storage uses an energy storage inverter system with power electronics technology as the core to connect to the grid. It has stable output and can maintain high-precision operation for a long time. It has fast charging and discharging capabilities and can achieve rapid response to system power requirements.

[0105] S120. Determine the maximum and minimum power requirements of the system based on the adjustable capacity evaluation model of the integrated energy system.

[0106] In an embodiment of the present invention, after establishing a robust aggregation model of an integrated energy system including conventional power generating units, renewable energy generating units and battery energy storage, the maximum and minimum power requirements of the system can be determined.

[0107] S130. Taking the maximization of the aggregated feasible domain of the integrated energy system in the day-ahead dispatch stage as the objective function, determine the maximum and minimum power demands of the system and ensure its robustness, with the output power constraints and ramp constraints of conventional generator sets, the output power constraints of renewable energy units, the charging and discharging power constraints of the battery energy storage system and the system power balance constraints as constraints.

[0108] S140. Solve the robust aggregation model of the integrated energy system and obtain the evaluation results.

[0109] In the embodiment of the present invention, under the premise of satisfying the constraints established in the above steps, the objective function of the robust aggregation model of the integrated energy system is solved, and finally an evaluation result of the system can be obtained.

[0110] From the above, it can be seen that the method for evaluating the adjustable capacity of an integrated energy system provided by an embodiment of the present invention determines the maximum and minimum power demands of the system by establishing a robust aggregation model of an integrated energy system including conventional generator sets, renewable energy generator sets and battery energy storage, takes maximizing the feasible domain of integrated energy aggregation in the day-ahead scheduling stage as the objective function, and ensures its robustness, and takes the output power constraints and ramp constraints of conventional generator sets, the output power constraints of renewable energy generators, the charging and discharging power constraints of the battery energy storage system, and the system power balance constraints as constraints.

[0111] Specifically, in the above embodiment, the objective function expression of the robust aggregation model is:

[0112] ;

[0113] ;

[0114] , ;

[0115] in, represents the maximum feasible domain of comprehensive energy aggregation, , Represents the weight coefficient of the upper and lower limits of power at time t, , represents the maximum and minimum power of the aggregation model at time t, represents the set of scheduling periods, represents the system power consumption at time t, Represents uncertain set constraints , represents the slack variable of the energy balance constraint at time t, Represents a collection of conventional generator sets, Represents the set of renewable energy units, represents the collection of combined heat and power units, Represents a collection of gas boilers, represents the collection of absorption chillers, Represents the electric boiler set, Represents a collection of chillers, represents the first-stage decision variable, represents the second-stage decision variable, represents the output power of device i at time t, represents the kth combination factor of the ith CHP, represents the charging power of the ith battery at time t, represents the discharge power of the ith battery at time t, Represents the energy stored in the i-th battery at time t.

[0116] In the above embodiment, the expression of variable constraint is:

[0117] ;

[0118] The expression of the operating constraints of conventional generator sets is:

[0119] ;

[0120] in, represents the lower limit of the output of the i-th unit, represents the output upper limit of the i-th unit, represents the ramp-down rate of the i-th unit, Represents the ramp-up rate of the i-th thermal power unit;

[0121] The expression of the uncertain set constraint is:

[0122] ;

[0123] in, represents the minimum total power emitted by the system during period t, Represents the maximum total power emitted by the system during period t;

[0124] The expression of the operation constraint of the cogeneration unit is:

[0125] ;

[0126] ;

[0127] in, represents the lower limit of the output of the i-th unit, represents the lower limit of the output of the i-th unit, represents the kth combination factor of the i-th CHP, The extreme value set of the operable area of ​​the i-th CHP, represents the kth extreme value of power generation within the operable area of ​​the i-th CHP, The heat output of the i-th CHP in period t is: The kth extreme value of thermal power generation within the operable area of ​​the i-th CHP;

[0128] Preferably, the expression of renewable energy unit constraint is:

[0129] ;

[0130] in, Represents the predicted output value of the day before;

[0131] The expression of the battery energy storage system constraint is:

[0132] ;

[0133] ;

[0134] ;

[0135] in, represents the minimum energy stored in the ith battery, represents the maximum energy stored in the ith battery, represents the maximum charging power of the ith battery, The maximum discharge power of the i-th battery, The charging efficiency of the i-th battery, The discharge efficiency of the ith battery, The i-th battery loss rate;

[0136] The expression of node power balance constraint is:

[0137] ;

[0138] ;

[0139] ;

[0140] in, represents the power load predicted on the day before, represents the thermal load predicted on the day before, represents the cooling load predicted on the day before;

[0141] In the above embodiment, the robust aggregation model of the integrated energy system is solved to obtain the evaluation results, including:

[0142] In the process of solving the C&CG algorithm, the influence of uncertain variables is not considered in the main problem at first, and it is solved as a deterministic optimization. However, the worst scenario of uncertain variables will definitely affect the decision of the main problem. The C&CG algorithm is based on the deterministic optimization solution, and continuously adds relatively bad scenarios and corresponding sub-problem decision variables and constraints, so that the upper and lower bounds of the objective function are continuously improved until the algorithm converges, ensuring the robustness of the final solution and obtaining the scheduling capacity evaluation result.

[0143] In specific implementation, solvers such as Cplex and Gurobi can be used for solving. For example, the CPLEX12.6 solver can be used to solve the robust aggregation model of the integrated energy system to obtain the evaluation results of the system.

[0144] See also Figure 4 A computer device 400 provided in an embodiment of the present application includes: a processor 410 and a memory 420, wherein the memory 420 stores a computer program executable by the processor 410, and when the computer program is executed by the processor 410, the above method is executed.

[0145] The embodiment of the present application further provides a storage medium 430 on which a computer program is stored. When the computer program is run by the processor 410, the above method is executed.

[0146] Among them, the storage medium 430 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable red-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, disk or optical disk.

[0147] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. "Multiple" means two or more, unless otherwise clearly and specifically defined.

[0148] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0149] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

[0150] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention belong.

[0151] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0152] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0153] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0154] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present invention. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A method for evaluating the dispatching capability of an integrated energy system, characterized in that: The steps include: Establish a robust aggregation model of an integrated energy system including conventional generators, renewable energy generators, and battery energy storage; Determining the maximum and minimum power demands of the power system according to the robust aggregation model; The objective function is to maximize the feasible domain of comprehensive energy aggregation in the day-ahead dispatching stage, and the constraints are the output power constraints and ramp constraints of conventional generator sets, the output power constraints of renewable energy units, the charging and discharging power constraints of the battery energy storage system, and the system power balance constraints. Solving the robust aggregation model with the objective function and constraint conditions to obtain a dispatch capability evaluation result; The objective function includes: S D =S G ∪S W ∪S CHP ∪S GB ∪S AC ∪S EB ∪S EC ; y=[p t，min ,p t，min ], In the formula, F represents the maximum feasible domain of comprehensive energy aggregation, Represents the power upper and lower limit weight coefficient at time t, p t，max 、p t，min represents the maximum and minimum power of the robust aggregation model at time t, ψ represents the scheduling period set, and p t，IES represents the system power consumption at time t, and Ω represents the uncertainty set constraint represents the slack variable of the energy balance constraint at time t, S G represents the set of conventional generators, S W Represents the set of renewable energy units, S CHP represents the set of CHP units, S GB Represents the gas boiler set, S AC represents the absorption chiller set, S EB Represents the electric boiler set, S EC represents the set of chillers, y represents the first-stage decision variables, and x represents the second-stage decision variables. represents the output power of device i at time t, represents the kth combination factor of the ith CHP, represents the charging power of the ith battery at time t, represents the discharge power of the ith battery at time t, Represents the energy stored in the i-th battery at time t.

2. The method for evaluating the dispatching capability of an integrated energy system according to claim 1, characterized in that: The uncertain set constraints include: In the formula, e t，min represents the minimum total power emitted by the system during period t, e t，max Represents the maximum total power emitted by the system during period t.

3. The method for evaluating the dispatching capability of an integrated energy system according to claim 1, characterized in that: The conventional generator set output power constraint and ramp constraint include: In the formula, p G，i，min represents the lower limit of the output of the i-th unit, p G，i，max represents the output upper limit of the i-th unit, r G，i，d represents the ramp-down rate of the i-th unit, r G，i，u Represents the ramp-up rate of the i-th thermal power unit.

4. The method for evaluating the dispatching capability of an integrated energy system according to claim 2, characterized in that: The output power constraints and ramp constraints of conventional generator sets also include representing the operating area of ​​each CHP power plant as a convex combination of extreme feature points, using a convex combination of polygonal feasible region vertices to describe the operating area of ​​the CHP unit, and obtaining the CHP unit operating constraints: p i，min ≤p t ≤p i，max ; In the formula, p i，min represents the lower limit of the output of the i-th unit, p i，max represents the lower limit of the output of the i-th unit, represents the kth combination factor of the i-th CHP, EP i The extreme value set of the operable area of ​​the i-th CHP, P i k represents the kth extreme value of power generation within the operable area of ​​the i-th CHP, The heat output of the i-th CHP in period t is: The kth extreme value of thermal power generation in the operable area of ​​the i-th CHP.

5. The method for evaluating the dispatching capability of an integrated energy system according to claim 1, characterized in that: The output power constraints of the renewable energy units include: In the formula, represents the output power of device i at time t, Represents the predicted output value for the day before.

6. The method for evaluating the dispatching capability of an integrated energy system according to claim 1, characterized in that: The battery energy storage system charging and discharging power constraints include: In the formula, E i，min represents the minimum energy stored in the ith battery, E i，max represents the maximum energy stored in the ith battery, P c ，i，max represents the maximum charging power of the ith battery, P dc，i，max The maximum discharge power of the i-th battery, η c，i The charging efficiency of the ith battery, η dc，i The discharge efficiency of the ith battery, s i The i-th battery loss rate.

7. The method for evaluating the dispatching capability of an integrated energy system according to claim 1, characterized in that: The system power balance constraints include: In the formula, represents the power load predicted on the day before, represents the thermal load predicted on the day before, Represents the cooling load predicted for the day ahead.

8. The method for evaluating the dispatching capability of an integrated energy system according to claim 1, characterized in that: The solving of the robust aggregation model comprises the following steps: First, ignore the influence of uncertain variables in the main problem and solve it as a deterministic optimization problem; Using the column and constraint generation algorithm, on the basis of solving the deterministic optimization problem, we continuously add harsh scenarios and corresponding sub-problem decision variables and constraints to continuously improve the upper and lower bounds of the objective function until the algorithm converges and obtains the scheduling capacity evaluation result.

9. A device for evaluating the dispatching capability of an integrated energy system, characterized in that: Using the method according to any one of claims 1 to 8, the device comprises: Modeling unit, to establish a robust aggregation model of the integrated energy system including conventional generators, renewable energy generators, and battery energy storage; A demand unit, used for determining the maximum and minimum power demands of the power system according to the robust aggregation model; The target constraint unit is used to maximize the feasible domain of comprehensive energy aggregation in the day-ahead scheduling phase as the objective function, and to use the output power constraint and ramp constraint of conventional generator sets, the output power constraint of renewable energy units, the charging and discharging power constraint of the battery energy storage system, and the system power balance constraint as constraint conditions; A solving unit is used to solve the robust aggregation model with the objective function and constraint conditions to obtain a scheduling capability evaluation result.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 8 is implemented.

11. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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