Electric integrated energy system collaborative planning method and system oriented to operation flexibility

Abstract

The invention discloses an electrical integrated energy system collaborative planning method and system oriented to operation flexibility. The method comprises the steps of obtaining multiple types of typical scenes formed by clustering historical loads and wind power output data; based on an electric power system and a natural gas system, an electrical integrated energy system with multiple energy storage is constructed; inputting the data of the multiple types of typical scenes into the electrical integrated energy system, constructing a collaborative optimization scheduling model of the electrical integrated energy system, and solving to obtain each operation cost; a collaborative planning model of the electrical integrated energy system is constructed by considering various flexible resources of thermal power generating unit flexible transformation, gas generating unit construction and energy storage power station construction; and solving the collaborative planning model by taking the minimum annual total cost formed by each operation cost as a planning objective function and taking a common constraint condition and a flexibility resource characteristic constraint condition as constraint conditions to obtain an optimal collaborative planning scheme. The method not only meets the requirements of safety and reliability and improves the economical efficiency, but also considers the improvement of the operation flexibility.

Description

Technical Field

[0001] The present invention relates to the field of integrated energy systems, and particularly to a collaborative planning method and system for an electrical integrated energy system for operation flexibility. Background Art

[0002] The rapid development of gas turbines has deepened the coupling degree between the power and natural gas systems. The two influence and rely on each other, forming an integrated energy system with gas turbines as the coupling link. The electricity-gas integrated energy system is a multi-energy system coupling body with the power system as the main body and integrating various energy forms such as natural gas. It can realize the complementary utilization of various energies and the coordinated balance between multiple systems, promote the consumption of renewable energy, and effectively improve the energy utilization efficiency. Under the background of the coupling of electricity and natural gas, the operation between the two energy systems will affect each other. For example, the rapid ramp-up characteristics of gas turbines can better cope with the impact of the uncertainty of wind power output, but the change of their operating state will have a certain impact on the safe operation of the natural gas system. Therefore, the traditional method of independent operation and independent analysis of a single energy system is no longer applicable to the electricity-gas integrated energy system. It is urgent to seek a way to improve operation flexibility from the perspective of the integrated energy system, taking into account the physical operation constraints of the two and the interaction characteristics between different energy entities.

[0003] Energy system planning mainly refers to the optimal equipment combination, capacity, location, and investment time of the resources to be built during the engineering period. Traditional power system planning usually only considers the optimal configuration strategy of a single energy form (i.e., electric energy), without considering the interaction between different energies. Energy system planning is usually regarded as a long-term (decades-long) multi-stage decision-making problem. The goal of the planning is generally to minimize the cost while meeting the system reliability. Currently, most of the collaborative planning studies on electricity-gas integrated energy systems still focus on steady-state models. This is because in the planning problem, the optimization cycle time scale is often long. Even the slow-dynamic natural gas network will eventually reach a steady-state equilibrium state. Therefore, using the steady-state power flow model can already meet the requirements of planning accuracy. In most existing studies, for the electricity-gas integrated energy system, it is usually considered to coordinate the short-term scheduling and long-term expansion planning simultaneously.

[0004] However, most of the existing studies conduct collaborative planning on the power-gas coupling system with the goal of meeting safety, reliability, and improving economy. There is a lack of research on the collaborative planning method for the electricity-gas integrated energy system with the goal of improving flexibility. How to consider the impact of flexibility supply-demand balance and establish a flexibility quantification index system has become a hot issue that urgently needs to be solved. Summary of the Invention

[0005] The technical problem to be solved by the present invention is that most of the existing energy system planning methods perform collaborative planning on the power and natural gas coupling system with the goal of meeting safety, reliability and improving economy, lacking the research on the collaborative planning method of the electrical integrated energy system aiming at improving flexibility. The purpose of the present invention is to provide a collaborative planning method and system for the electrical integrated energy system oriented to operation flexibility. The present invention combines scenario reduction and flexibility resource modeling to obtain a collaborative planning scheme for the electrical integrated energy system aiming at improving flexibility, which not only meets safety, reliability and improves economy, but also considers the improvement of operation flexibility, and solves the problems of the existing methods.

[0006] The present invention is realized through the following technical solutions:

[0007] In the first aspect, the present invention provides a collaborative planning method for the electrical integrated energy system oriented to operation flexibility, and the method includes: obtaining multiple types of typical scenarios formed by clustering historical load and wind power output data;

[0008] Based on the power system and the natural gas system, constructing an electrical integrated energy system with multiple energy storages;

[0009] Inputting the data of multiple types of typical scenarios into the electrical integrated energy system, constructing a collaborative optimal scheduling model of the electrical integrated energy system, and solving to obtain various operation costs;

[0010] Considering various flexibility resources such as flexible transformation of thermal power units, construction of gas turbines and construction of energy storage power stations, constructing a collaborative planning model of the electrical integrated energy system;

[0011] Taking the minimum annual total cost formed by various operation costs as the planning objective function, and using the first constraint condition and the second constraint condition as constraint conditions, solving the collaborative planning model to obtain the optimal collaborative planning scheme;

[0012] Wherein: the first constraint condition is a general constraint condition, and the second constraint condition is a flexibility resource characteristic constraint condition.

[0013] Further, constructing a collaborative optimal scheduling model of the electrical integrated energy system, and solving to obtain various operation costs, including:

[0014] Taking the minimum total operation cost within the scheduling period of the electrical integrated energy system as the objective function, and using the reserve callability constraint under different typical scenarios as the constraint condition, constructing a collaborative optimal scheduling model of the electrical integrated energy system;

[0015] Using the alternating direction method of multipliers with adaptive penalty parameters to solve the collaborative optimal scheduling model to obtain various operation costs.

[0016] Furthermore, the total operating cost within the scheduling period includes the energy supply cost, the curtailment cost of wind power and load shedding, and the reserve capacity cost;

[0017] The energy supply cost includes the power generation cost of conventional generators, the natural gas production cost, the charge and discharge cost of energy storage power stations, and the interruption compensation cost of interruptible loads;

[0018] The curtailment cost of wind power and load shedding includes the first curtailment cost of wind power and load shedding and the second curtailment cost of wind power and load shedding. The first curtailment cost of wind power and load shedding refers to the cost of wind power curtailment and load loss without considering the deliverability check of reserve capacity, and the second curtailment cost of wind power and load shedding refers to the load loss cost considering the deliverability check of reserve capacity;

[0019] The reserve capacity cost includes the reserve capacity costs of generator sets (conventional and gas turbine units), energy storage power stations and interruptible loads, as well as the risk cost of insufficient reserve.

[0020] Furthermore, the reserve callability constraints include generator outage constraints, line outage constraints and natural gas pipeline constraints.

[0021] Furthermore, the annual total cost includes the investment cost and the annual operating cost;

[0022] The investment cost is the cost considering the initial one-time investment of the flexibility resources to be planned;

[0023] The annual operating cost is the total cost obtained by adding the annual power generation cost, the annual reserve cost, the annual wind power curtailment cost, the annual gas supply cost, the annual energy storage operation cost and the annual risk cost of insufficient reserve of the integrated electrical energy system after investing in different flexibility resources;

[0024] The constraint conditions of flexibility resource characteristics include the output and ramp rate constraints of thermal power units after flexibility transformation, the output and ramp rate constraints of gas turbine units, and the energy storage constraints.

[0025] In the second aspect, the present invention also provides a collaborative planning system for an integrated electrical energy system oriented to operational flexibility. The system includes: an acquisition unit for acquiring multiple types of typical scenarios formed by clustering historical load and wind power output data;

[0026] A first construction unit for constructing an integrated electrical energy system with multiple energy storages based on the power system and the natural gas system;

[0027] A second construction unit for inputting the data of multiple types of typical scenarios into the integrated electrical energy system, constructing a collaborative optimal scheduling model of the integrated electrical energy system, and solving to obtain various operating costs;

[0028] A third construction unit for constructing a collaborative planning model of the integrated electrical energy system considering various flexibility resources such as flexibility transformation of thermal power units, investment in gas turbine units and investment in energy storage power stations;

[0029] A collaborative planning and solving unit is used to solve the collaborative planning model with the minimum annual total cost formed by each operating cost as the planning objective function and the first constraint condition and the second constraint condition as the constraint conditions to obtain an optimal collaborative planning scheme. Among them, the first constraint condition is a general constraint condition, and the second constraint condition is a flexibility resource characteristic constraint condition.

[0030] Thirdly, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the above-mentioned collaborative planning method for an electrical integrated energy system oriented to operation flexibility is implemented.

[0031] Fourthly, the present invention also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned collaborative planning method for an electrical integrated energy system oriented to operation flexibility is implemented.

[0032] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0033] The collaborative planning method and system for an electrical integrated energy system oriented to operation flexibility of the present invention combines scenario reduction and flexibility resource modeling to obtain a collaborative planning scheme for the electrical integrated energy system with the goal of improving flexibility, which not only meets safety, reliability and improves economy, but also considers the improvement of operation flexibility and solves the problems of existing methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, form a part of this application, and do not limit the embodiments of the present invention. In the drawings:

[0035] Figure 1 is a schematic diagram of the technical route for collaborative planning of an electrical integrated energy system oriented to operation flexibility of the present invention;

[0036] Figure 2 is a flowchart of the collaborative planning method for an electrical integrated energy system oriented to operation flexibility of the present invention;

[0037] Figure 3 is a schematic diagram of the load clustering result of the present invention;

[0038] Figure 4 is a schematic diagram of the wind power clustering result of the present invention;

[0039] Figure 5 is a schematic diagram of the composition of the operating costs of each scheme on a typical day of the present invention;

[0040] Figure 6Schematic diagram of the standby capacity supply situation of the system of the present invention without considering any flexible resource systems;

[0041] Figure 7 Schematic diagram of the standby capacity supply situation of the system of the present invention after considering the flexibility transformation of thermal power units;

[0042] Figure 8 Schematic diagram of the standby capacity supply situation of the system of the present invention after considering the construction of gas turbine units;

[0043] Figure 9 Schematic diagram of the standby capacity supply situation of the system of the present invention after considering the construction of energy storage;

[0044] Figure 10 Block diagram of the collaborative planning system of the electrical integrated energy system for operation flexibility of the present invention. Detailed implementation manners

[0045] To make the objectives, technical solutions and advantages of the present invention more clear and understandable, the present invention will be further described in detail below in conjunction with embodiments and drawings. The illustrative embodiments of the present invention and their descriptions are only used to explain the present invention and are not intended to limit the present invention.

[0046] How to consider the influence of introducing the balance of flexible supply and demand and establish an index system for flexible quantification has become a hot issue that needs to be solved urgently. From the perspective of power supply balance, the flexibility of the power system is the self - ability to regulate and use various resources and still operate safely, efficiently, cleanly and economically under the changes and uncertain factors in the system. In addition, with the access of a high proportion of clean energy, in the short term, the advantage of the electricity - gas coupling in improving the overall flexibility of the system will become more obvious.

[0047] The research on the technical solution of the present invention regarding the flexibility of the power system mainly focuses on the accommodation issues of variable renewable energy generation, which can be divided into three categories: assessment, operation, and resource planning. By exploring and solving the characteristics of these three basic issues, it can be judged whether there will be insufficient accommodation problems in the current power system under the access of a high proportion of clean energy; whether it can meet the needs of improving power generation efficiency, reliability, and safety during operation; and finally, it can be judged whether new flexibility resources need to be configured to adapt to the typical operation scenarios of the expected high proportion of clean energy access. The core of the power system is the power and electricity balance, that is, determining the installed capacity and the additional capacity required in the planning year according to the load demand in the planning year to ensure the reliability of the system and the reasonable utilization hours of thermal power units. Traditional planning only considered the power and electricity balance and met the load growth demand by building new power plants, without considering the flexibility requirements brought by the large-scale access of clean energy. Flexibility planning is actually to study what technical measures should be adopted to plan and build a power system that can fully meet the flexibility requirements under the access of a high proportion of clean energy. Similarly, flexibility planning should mainly be analyzed from the perspectives of power source side planning, transmission grid planning, and distribution grid planning.

[0048] To realize the modeling of an electrical integrated energy system with multiple energy storages in the present invention, first, it is necessary to establish the models of two energy subsystems, namely the power system and the natural gas system, and establish the coupling element model; then establish the collaborative optimal scheduling model of the electrical integrated energy system considering multiple energy storages, and this model will take into account the impact of the deliverability of reserve capacity on the safe operation of the system. Among them, the power system model includes the generation shift distribution factor model based on DC power flow, the energy storage device model, the interruptible load model, the wind turbine output model, and the operation constraints of the power system; the natural gas system model includes the steady-state model of the gas flow in the natural gas pipeline and the piecewise linearization model of the gas flow increment. The advantages and disadvantages of the planning scheme need to be simulated through operation at the system level. The formulation of the collaborative planning scheme of the electrical integrated energy system must be based on a reasonable operation scheduling scheme. On the basis of the collaborative optimal scheduling model of the electrical integrated energy system, an extended collaborative planning model of the electrical integrated energy system combining the two stages of investment decision-making and operation simulation is proposed. It is mainly considered from the following four aspects: scenario reduction, flexibility resource modeling, formation of the planning scheme, and evaluation of the economy and flexibility of the planning scheme. The specific technical route is as Figure 1 shown.

[0049] Example 1

[0050] As Figure 2 shown, the collaborative planning method of the electrical integrated energy system for operation flexibility of the present invention includes:

[0051] Step 1: Obtain multiple typical scenarios formed by clustering historical load and wind power output data;

[0052] Step 2: Based on the power system and the natural gas system, construct an electrical integrated energy system with multiple energy storages;

[0053] Step 3: Input the data of multiple typical scenarios into the electrical integrated energy system, construct a collaborative optimization scheduling model for the electrical integrated energy system, and solve to obtain the operating costs;

[0054] Step 4: Consider various flexibility resources such as the flexibility transformation of thermal power units, the construction of gas turbines, and the construction of energy storage power stations, and construct a collaborative planning model for the electrical integrated energy system;

[0055] Step 5: Take the minimum annual total cost formed by the operating costs as the planning objective function, and use the first constraint condition and the second constraint condition as the constraint conditions to solve the collaborative planning model to obtain the optimal collaborative planning scheme; where: the first constraint condition is the general constraint condition, and the second constraint condition is the flexibility resource characteristic constraint condition.

[0056] In this embodiment, the relevant model of the power system in Step 2 is as follows:

[0057] (1) Power network model

[0058] The power grid in the electrical integrated energy system with multiple energy storages is usually a distribution network, which meets the conditions for using the DC power flow to replace the AC power flow. Therefore, the DC power flow model can be used to calculate the power grid power flow.

[0059] To make the power flow calculation in the model more convenient, the generation shift distribution factor is used to solve the power grid power flow. In the power system, the Generation Shift Distribution Factors (GSDF) is defined as the change in the active power flow of a branch caused by a unit increase in the active power output of a generator, and the DC power flow calculation is used. According to the DC power flow model, the GSDF of generator node j to branch m is:

[0060] where, ΔP m is the change in the active power of branch m. ΔP Gj is the change in the active power of the generator at node i. l and k are the head and tail nodes of branch m respectively. X lj and X kj are the elements in the l-th row and j-th column and the k-th row and j-th column of the reactance matrix respectively. x m is the reactance of branch m. The active power flow of branch m can be described as:

[0061] where, n - 1 is the number of generator nodes except the slack node. P m is the active power flow of branch m. is the initial active power flow of branch m.

[0062] GSDF constructs the relationship between the active power flow of branches and the change value of the active power output of nodal generators. If the load nodes are regarded as negative generator nodes, then GSDF can reflect the relationship between the nodal power generation and the active power flow of branches in the form of all variables. When the system load changes, GSDF can also be used to quickly determine the active power flow of branches.

[0063] (2) Energy storage device model

[0064] The access of energy storage devices in the power system can suppress the system fluctuations caused by the intermittency and reverse peak - shaving characteristics of wind power output, thereby achieving peak shaving and valley filling and improving the flexibility of system operation.

[0065] This invention uses an energy storage power station as the energy storage device. The charge - discharge model of the energy storage power station is as follows:

[0066]

[0067] where, E s,t is the electricity stored in the energy storage power station at time t (kWh), μ loss is the self - energy loss rate of the energy storage power station, is the charging efficiency of the energy storage power station, Δt is the charge - discharge time interval (h), is the discharging efficiency of the energy storage power station, Cap E is the capacity of the energy storage power station, γ ch is the maximum charging rate, γ dc is the maximum discharging rate, ω s,t and ω r,t are binary variables indicating the charging and discharging states of the energy storage power station. When ω s,t = 1, it means charging is in progress.

[0068] (3) Interruptible load model

[0069] When the system power generation is insufficient, the interruptible load can be regarded as virtual standby power generation capacity, and the system energy supply gap can be reduced by cutting off the interruptible load; when there is a surplus in system power generation, the interruptible load can also be regarded as system upward reserve, and the balance between system power generation and demand can be maintained by connecting the interruptible load. Let k represent the total number of nodes connected with interruptible loads in the system. The power generation capacity and upward reserve capacity provided by the interruptible load in the system can be expressed as:

[0070] The power generation and reserve capacity provided by the interruptible load and the switching time need to meet the following constraints:

[0071]

[0072] Among them, and are the maximum capacity and minimum capacity of the interruptible load connected to the i-th node, respectively. u k,t is a 0-1 variable representing the working state of the interruptible load. and represent the minimum on-time and minimum off-time limits of the interruptible load, respectively. and represent the cumulative on-time and cumulative off-time of the interruptible load before time t, respectively.

[0073] (4) Wind turbine output model

[0074] As a distributed generation device in the integrated power-gas energy system, wind power has the advantages of cleanness and environmental protection. However, its intermittency and uncertainty increase the scheduling difficulty for the safe and stable operation of the system. The output power of a wind farm is closely related to the wind speed, and their relationship can be expressed by the following piecewise function:

[0075]

[0076] Among them, is the wind turbine output power at node i. v i is the wind speed at node i where the wind turbine is located. and are the cut-in and cut-out wind speeds of the wind turbine, respectively. v rated is the rated wind speed of the wind turbine. is the total installed wind power capacity at node i. The specific wind turbine parameters are related to the specifications of the wind turbine.

[0077] (5) Power system operation constraints

[0078] The power system mainly consists of conventional generator sets, gas generator sets, wind turbine sets, transmission lines, power loads, energy storage power stations, interruptible loads, etc. When the power system is operating, it needs to meet the following constraint conditions:

[0079] 1) System power balance constraint:

[0080]

[0081] Among them, P i,t is the total power generation of the generator sets in the system, including conventional generator sets and gas turbine sets; P w,t is the power generation of the wind farms connected to the system; P s,t is the discharge power provided by the energy storage power station in the system; L d,e,t is the total load of the system; is the system load shedding amount; The dispatching power provided for the system interruptible load, N E 、N w 、N S 、N e 、N int respectively represent the number of nodes connecting generators, wind farms, energy storage power stations, loads, and interruptible loads in the system.

[0082] 2) Generator ramp rate constraint: P i,t -r i ≤P i,t+1 ≤P i,t +r i , where P i,t is the power generation power of unit i at time t, P i,t+1 is the power generation power of unit i at time t + 1, and r i is the ramp rate of unit i.

[0083] 3) Generator reserve capacity constraint: where, is the upward reserve capacity provided by unit i,

[0084] is the downward reserve capacity provided by unit i, is the maximum power generation capacity of unit i.

[0085] 4) Generator reserve response time constraint: where T r is the reserve response time requirement of the unit.

[0086] 5) Wind farm output constraint: where P w,t is the actual value of wind power output at time t, is the predicted value of wind power output at time t.

[0087] 6) Line transmission capacity constraint:

[0088]

[0089] where, represents the maximum power transmission limit of line m, and K is the power distribution coefficient, which can be derived from the generation shift distribution factor GSDF:

[0090] 7) Energy storage device charge and discharge power constraint: where, is the maximum charging power of the energy storage power station. is the maximum discharging power of the energy storage power station. ω s,tand ω r,t are 0-1 variables indicating the charging and discharging states of the energy storage device. When the value is 1, it means the energy storage power station is charging or discharging, and when the value is 0, it means it is not working.

[0091] 8) Constraint on the remaining energy of the energy storage device: where E s,t is the remaining energy of the energy storage power station at time t. and are the maximum and minimum energy storage capacities of the energy storage power station respectively. When a complete scheduling period ends, the energy storage E s,T in the system will be set to the initial energy storage E s,0 .

[0092] 9) Constraint on the reserve capacity of the energy storage: where and represent the upward reserve capacity and downward reserve capacity provided by the energy storage power station respectively. and represent the charging and discharging efficiencies of the energy storage power station respectively.

[0093] 10) Constraint on interruptible load

[0094] When modeling the interruptible load in the system, the constraints of the interruptible load scheduling capacity, upward reserve capacity, minimum on-time, and minimum off-time need to be considered.

[0095] 11) Constraint on reserve capacity demand

[0096] Reserve is an important ancillary service. The system reserve capacity should be able to withstand the power fluctuations caused by wind power, load uncertainty, and system component failures. In this paper, the total reserve capacity is provided jointly by non-gas units, gas units, interruptible loads, and energy storage power stations to ensure the reliability of system operation. The reserve capacity demand of the system to cope with wind power and load fluctuations is considered as a certain percentage of its predicted value. The interruption of the maximum installed capacity of the system is considered the most serious N-1 contingency. Therefore, the reserve capacity to cope with unexpected situations is set to the maximum installed capacity of the generator. The total upward and downward reserve constraints in the system are as follows:

[0097]

[0098] where β d and β w are the reserve demand coefficients of the load and wind energy. represents the maximum installed capacity of the generator set.

[0099] In this embodiment, the relevant model of the natural gas system in step 2 is as follows:

[0100] (1) Natural gas network model

[0101] The gas flow rate F in the natural gas pipeline mn has the following relationship with the node gas pressure π:

[0102]

[0103] where C mn is a constant, specifically depending on the characteristics of the pipeline (such as length, diameter, and temperature, etc.).

[0104] The natural gas network also needs to satisfy the gas output constraints of gas wells and the node gas pressure constraints:

[0105]

[0106] where and respectively represent the upper and lower limits of the gas output of gas wells, and respectively represent the upper and lower limits of the node gas pressure.

[0107] The node gas flow balance equation of the natural gas network is:

[0108]

[0109] In the natural gas network, the pipeline gas flow constraints, gas source gas supply constraints, node gas pressure constraint equations, and node gas flow balance constraint equations are mainly considered. Fming, Fmax g are respectively the lower and upper limits of the gas supply of the gas source; π m,t is the gas pressure of node m at time t; and are respectively the lower and upper limit values of the gas pressure, C mn is the comprehensive pipeline parameter; and are respectively the node - gas source correlation matrix, node - load correlation matrix, node - pipeline correlation matrix, and node - gas turbine correlation matrix; F g,t , L d,g,t , F c,t , F mn,t , are respectively the gas supply of the gas source, gas load demand, lost gas load, pipeline gas flow, and gas consumption vector of the gas turbine at time t.

[0110] (2) Gas turbine model

[0111] The relationship between the natural gas consumption and the power generation of the gas turbine is:

[0112]

[0113] Among them, α j is the thermal conversion efficiency coefficient of the gas turbine, which is related to the unit itself; HHV is the fixed higher calorific value of natural gas, taking 1.026 MBtu / kcf.

[0114] The characteristics of the rapid response of the gas unit to system changes enable it to provide better system reserve services. The system upward reserve and downward reserve are respectively:

[0115]

[0116] The relationship between the natural gas consumption and the power generation of the gas turbine is:

[0117]

[0118] Among them, α j is the thermal conversion efficiency coefficient of the gas turbine, which is related to the unit itself; HHV is the fixed higher calorific value of natural gas, taking 1.026 MBtu / kc.

[0119] The characteristics of the rapid response of the gas unit to system changes enable it to provide better system reserve services. The system upward reserve and downward reserve are respectively:

[0120]

[0121] In this embodiment, in step 3, a collaborative optimization scheduling model of the electrical integrated energy system is constructed and solved to obtain various operating costs, including:

[0122] Taking the minimum total operating cost within the scheduling period of the electrical integrated energy system as the objective function and the reserve availability constraints under different typical scenarios as the constraint conditions, a collaborative optimization scheduling model of the electrical integrated energy system is constructed;

[0123] The alternating direction method of multipliers with an adaptive penalty parameter is used to solve the collaborative optimization scheduling model to obtain various operating costs.

[0124] Specifically, assuming that the power generation cost of wind power is 0, the total operating cost within the scheduling period includes the energy supply cost, the curtailment and load shedding cost, and the reserve capacity cost;

[0125] (1) Energy supply cost C1: including the power generation cost of conventional generators, the natural gas production cost, the charge and discharge cost of energy storage power stations, and the interruption compensation cost of interruptible loads;

[0126] (2) Curtailment and load shedding cost C2: Considering environmental protection policies, fines need to be paid for the curtailed wind power that cannot be absorbed. Assuming that the load may not be provided at 100% and load shedding may occur, the curtailment and load shedding compensation costs are included in the total operating cost to maximize the absorption of wind power and reduce load losses. The curtailment and load shedding costs consist of two parts, including the first curtailment and load shedding cost C 21 and the second curtailment and load shedding cost C 22 . The first curtailment and load shedding cost refers to the curtailment and load shedding costs without considering the deliverability check of the reserve capacity, and the second curtailment and load shedding cost refers to the load shedding cost considering the deliverability check of the reserve capacity. In the present invention, the wind power, load demand fluctuations, and component failures are used as the actual operation volatility scenarios to check whether the reserve capacity can be effectively called to suppress the fluctuations.

[0127] (3) Reserve capacity cost C3: It includes the reserve capacity costs of generator sets (conventional and gas turbine units), energy storage power stations, and interruptible loads, as well as the risk cost of insufficient reserve. Considering that the reserve capacity demand may not be fully met during the scheduling plan formulation process, based on the idea of flexible modeling, the present invention introduces factors and to relax the upward and downward adjustment reserve capacity constraints at time period t, and constructs a risk model of insufficient reserve to quantify the reserve shortage in each time period.

[0128] Specifically, with the minimum total operating cost within the scheduling period of the electrical integrated energy system as the objective function, the objective function is as follows:

[0129]

[0130] Among them, C i and P i,t respectively represent the cost coefficient and power generation of non-gas turbine units; C g and F g,t are respectively the cost coefficient of natural gas supply and gas production; C s is the unit charge-discharge cost of the energy storage power station, and P s,t represents the charging or discharging power of the energy storage power station; and are the unit power interruption compensation cost of the interruptible load and the scheduling power of the interruptible load; C w represents the unit fine cost of curtailment; and are respectively the cost coefficients of upward and downward reserve, and are respectively the upward capacity and reserve capacity provided by the reserve source r, and the reserve source r may be generator reserve or energy storage power station or interruptible load; and Denote the cost coefficients for upward and downward reserves of the gas turbine, and represent the amount of natural gas reserved for the gas turbine to provide upward and downward reserve capacities, N G denote the number of gas units, and are the penalty cost parameters for the shortage of upward and downward reserve capacities per unit respectively.

[0131] Specifically, to ensure the security and reliability of the dispatching of the integrated electricity-gas energy system, this model takes wind power, load demand fluctuations, and component failures as the actual operation volatility scenarios to check whether the reserve capacity can be effectively called to suppress the fluctuations. Component failures are divided into generator set failures and line failures, and the component failures are combined with the predicted demand fluctuations to construct the reserve checking scenarios. The reserve callability constraints under different typical scenarios are as follows:

[0132] (1) Generator outage constraint

[0133] Sudden N-1 generator outage failures and renewable power fluctuations can be solved by dispatching the reserve capacity. The power balance is maintained through the planned reserve capacity. At this time, the transmission limits of the lines are as follows:

[0134]

[0135] Among them, is the amount of lost load at time t under the checking scenario τ; G is the generator failure number; are the call amounts of the reserve capacities of the generator sets, energy storage power stations, and interruptible loads under the current checking scenario respectively.

[0136] (2) Line interruption constraint

[0137] The model proposed in the present invention simulates the impact of line interruptions on the reserve capacity allocation. To reduce the computational burden, some lines are selected as the fault set, denoted by L as the line that fails within the dispatching range, to check the transferability of the reserve capacity.

[0138] The active power shortage caused by wind power fluctuations can also be solved by dispatching the system reserve capacity.

[0139]

[0140] Among them, L is the line failure number; KL is the power distribution coefficient considering the failure of line L, are the call amounts of the reserve capacities of the generator sets, energy storage power stations, and interruptible loads under the current checking scenario respectively.

[0141] (3) Natural gas pipeline constraint

[0142] Gas turbines can also provide backup services in the power system. However, when the gas consumption of gas turbines increases during operation, the natural gas pipeline may become blocked, making it impossible to meet the gas demand of gas turbines. Therefore, it is necessary to verify whether the gas consumption of gas turbines can be met by the gas pipeline network when there are additional backup supply requirements. In the present invention, the natural gas demand for the gas turbine to provide reserve capacity is regarded as a load in the natural gas network, and natural gas network constraints are added to ensure that the fluctuating demand of the gas turbine is effectively met. The constraints are expressed as follows:

[0143]

[0144] Specifically, the solution of the collaborative optimal scheduling model of the integrated electrical energy system is as follows:

[0145] The present invention uses the alternating direction method of multipliers with adaptive penalty parameters (ADMM-SAP) to construct a solution algorithm for the collaborative optimal scheduling model of the integrated electrical energy system. When the collaborative optimal scheduling model of the integrated electrical energy system takes the minimum scheduling operation cost as the objective function, that is, to find the minimum value of the sum of the power system scheduling operation cost and the natural gas system scheduling operation cost.

[0146] A. Model reset

[0147] The objective function of the collaborative optimal scheduling model of the integrated electrical energy system can be decomposed into two sub-objective functions of the power system and the natural gas system. The optimal scheduling problem of the power system can be represented by sub-problem 1, and the optimal scheduling problem of the natural gas system can be represented by sub-problem 2.

[0148]

[0149] Sub-problem 1: Objective function minf e , which consists of the power network element model and the gas turbine (as a generator) and obeys the power system operation constraints.

[0150] Sub-problem 2: Objective function minf g , which consists of the natural gas network element model and the gas turbine (as a natural gas load) and obeys the natural gas system operation constraints.

[0151] Since the gas turbine model is included in both sub-problem 1 and sub-problem 2, and both obey the operation constraints, the gas turbine variables and can be used as boundary variables coupling the power and natural gas systems, and and The optimal solution results within the two energy subsystems should be consistent:

[0152]

[0153] wherein and represent the optimal results of the boundary variables in the power system, and represent the optimal results of the boundary variables in the natural gas system.

[0154] B. ADMM-based model solution strategy

[0155] To facilitate the implementation of the ADMM algorithm, auxiliary variables and are introduced. The key idea of using ADMM to coordinate the optimization problems of the power and natural gas systems is to relax the coupling constraints and add penalty terms to the objective functions of the two sub-problems.

[0156] After adding penalty terms, the objective functions for solving the power system and the natural gas system are shown as follows respectively.

[0157]

[0158] where γ j,t and ρ represent the Lagrange multiplier and the penalty parameter respectively.

[0159] To solve the collaborative optimization scheduling model of the integrated electrical energy system using the ADMM algorithm, it is specifically carried out according to the following steps:

[0160] Step 1: Variable initialization. Set the iteration index n = 1. Set the original and dual convergence thresholds ε p and ε d . Initialize the gas consumption of the gas turbine The gas reserves for the gas turbine to provide upward and downward reserve capacities are and the Lagrange multiplier λ and the penalty parameter ρ.

[0161] Step 2: Solve the optimal scheduling result of the power system sub-problem 1. According to and the initial values, obtain the optimal solution results and

[0162] Step 3: Update the auxiliary variables. Let share the boundary variables with the natural gas system and

[0163] Step 4: Solve the optimal scheduling result of the natural gas system sub-problem 2. According to and the initial values, obtain the optimal solution results of the current natural gas system and

[0164] Step 5: Update the auxiliary variables. Let share the boundary variables with the power system and information.

[0165] Step 6: Calculate the original residuals and dual residuals of the boundary variables and respectively.

[0166] Step 7: Check for convergence. If the maximum residual satisfies the constraint condition or n > N, terminate the iterative process and output the solution; otherwise, go to Step 8. If the current iteration number n is greater than the maximum iteration limit N, consider this solution process as not converging.

[0167] Step 8: Update the Lagrange multiplier, let n = n + 1, and go back to Step 2 to repeat the iterative process.

[0168]

[0169] The penalty parameter ρ is a fixed constant in the standard ADMM algorithm. However, it is worth noting that the choice of the penalty parameter ρ has a certain impact on the convergence speed of the algorithm in practice. However, for different system parameters and constraint conditions, the ρ that can achieve the fastest convergence in the calculation is different, and it is also difficult to find the most suitable penalty parameter value at one time during the solution.

[0170] Therefore, in order to improve the convergence speed and reduce the dependence of the convergence performance of the algorithm on the initial setting value of the penalty parameter, this paper uses the ADMM algorithm with an adaptive penalty parameter (ADMM-SAP) improved based on the standard ADMM algorithm to solve the proposed model. The penalty parameter update formula is:

[0171]

[0172] where is the penalty parameter for the (n + 1)-th iteration, is the penalty parameter for the n-th iteration, is the original residual for the n-th iteration, is the dual residual for the n-th iteration.

[0173] At the same time, in order to prevent the algorithm from converging due to an overly large penalty parameter, the present invention sets a maximum penalty parameter value ρ max :

[0174] If then

[0175] C, Sub - problem Solving Strategy

[0176] According to the distributed solution idea of the ADMM algorithm, the originally complex collaborative optimal scheduling of the integrated electrical energy system is decomposed into sub - problems of two subsystems, namely the power system and the natural gas system, for separate optimal solutions, and the alternating optimization between the two subsystems is realized through the way of coupling variable parameter transfer. The pseudo - codes of the solution processes of the two subsystems are respectively shown in Table 1 and Table 2.

[0177] Table 1 Power System Optimal Solution Process

[0178]

[0179] Table 2 Natural Gas System Optimal Solution Process

[0180]

[0181] In this embodiment, the multiple types of typical scenarios obtained in step 1 are as follows:

[0182] To ensure the consistency of the planned investment cost and the total cost during the operation stage over the time span, typical scenarios are selected from the annual time - series data of wind and electricity loads, and the daily operation costs of each typical scenario are optimized and solved. The annual operation cost can be obtained by adding the products of the daily operation cost of each typical day, the probability of occurrence of each typical scenario, and the number of days of occurrence. The k - means clustering algorithm is used to cluster the historical load and wind power output data of 365 days in a certain year into a total of 8 types of typical scenarios, as Figure 3 and Figure 4 shown.

[0183] In this embodiment, the collaborative planning model of the integrated electrical energy system in step 4 is as follows:

[0184] Considering various flexibility resources such as the flexibility transformation of thermal power units, the construction of gas - fired units, and the construction of energy storage power stations, a collaborative planning model of the integrated electrical energy system considering multiple types of flexibility resources participating is constructed; with the minimum annual total cost composed of the investment cost and the annual operation cost as the planning objective, an operational research model for the planning is established. The investment cost considers the initial one - time investment cost of the flexibility resources to be planned; the annual operation cost considers the total cost after adding the annual power generation cost, the annual reserve cost, the annual wind curtailment cost, the annual gas supply cost, the annual energy storage operation cost, and the annual reserve shortage risk cost of the system after investing in different flexibility resources, as follows.

[0185] (1) Planning objective function:

[0186] minC = C inv +C op

[0187] where C inv represents the annual investment cost; Cop Represents the annual operating cost.

[0188] The annual investment cost is expressed as:

[0189]

[0190] Among them, the annual investment cost consists of the flexibility retrofit cost of thermal power units, the investment cost of installing energy storage on the source side, and the construction cost of gas turbine units. In the formula, c is the investment cost per unit capacity; x is a 0-1 variable indicating whether to invest; λ is the capital recovery factor for converting a one-time investment into an annual investment fee; ω is a 0-1 variable indicating whether to choose to invest in this type of flexible resource.

[0191] The annual operating cost consists of five parts and is expressed as:

[0192] C op = CY1 + CY2 + CY3 + CY4 + CY5

[0193]

[0194] Among them, the annual operating cost includes:

[0195] 1) The cost of unit and wind power output CY1. Ω cf Represents the set of thermal power units, f i (P i,t ) represents the power generation cost of thermal power units, P i,t represents the output of thermal power units; Ω w represents the set of wind farms, C w represents the unit cost of wind power generation, represents the predicted value of wind power generation, represents the curtailment volume.

[0196] 2) The cost of specific resource output CY2. ω gf is a 0-1 variable indicating whether to choose a gas turbine unit. If the gas turbine unit is selected, it is 1; if not, it is 0. C g,t represents the unit price of gas supply from gas wells, G g,t represents the gas supply volume from gas wells; ω es is a 0-1 variable indicating whether to choose energy storage on the source side. If the source-side energy storage is selected, it is 1; if not, it is 0. C s,t represents the unit operation price of energy storage, including the costs of charging and discharging, |P s,t | represents the charge and discharge power of energy storage.

[0197] 3) The standby supply cost CY3. and represent the unit price and supply volume of standby capacity supply respectively, including upward and downward regulation standby.

[0198] 4) Risk cost of insufficient reserve CY4. and represent the unit price of insufficient reserve risk and the amount of reserve shortage respectively, including the upper reserve shortage and the lower reserve shortage.

[0199] 5) Curtailment cost CY5. and represent the unit curtailment cost and the amount of curtailment respectively.

[0200] In addition, M in each item represents the set of typical scenarios of electric load and wind power output in a year; D m represents the number of days included in the m-th typical scenario. This corresponds to the clustering result obtained by scenario clustering in 2.3.1.

[0201] (2) The first constraint condition and the second constraint condition

[0202] 1) The first constraint condition: Ordinary constraint condition

[0203] a. Output and ramping constraint of conventional thermal power units:

[0204]

[0205] where P i,t is the planned power generation; and are the reserve capacities required for the down-ramping and up-ramping of the thermal power unit respectively; r i max is the maximum value of the ramping rate of the thermal power unit; P i min and P i max are the minimum output and the maximum output of the thermal power unit.

[0206] b. Wind power output constraint:

[0207]

[0208] The actual wind power output P w,t should be between 0 and the wind power prediction value .

[0209] c. Node power balance and transmission capacity limit:

[0210]

[0211] where P i,t , P j,t , P w,t , P s,t are the power generations of thermal power units, gas turbines, wind farms and energy storage respectively; is the power consumption of the power load; T l is the power transfer distribution factor matrix based on the DC power flow, and the matrix corresponding to each component is different. ef l max represents the maximum transmission capacity of the line.

[0212] d. Natural gas system constraints:

[0213]

[0214]

[0215] The above equations represent the steady-state gas flow constraints based on the Weymouth theory. and are the maximum and minimum gas supply volumes of the gas well. In the gas network node gas pressure constraints, and represent the minimum and maximum limits of the node gas pressure. For the directional relationship and numerical conversion relationship between the pipeline gas flow F mn and the node gas pressure, C mn is a constant, specifically depending on the characteristics of the pipeline, F mn is the pipeline gas flow rate, and π is the node gas pressure. In the node gas flow balance constraint, F g,t is the natural gas output of the gas well, is the gas-saving load, F mn,t is the gas flow rate in pipeline mn, is the gas consumption of the node gas turbine unit.

[0216] 2) Second constraint condition: Flexibility resource characteristic constraint condition

[0217] a. Output and ramp rate constraints of thermal power units after flexibility transformation:

[0218]

[0219] When the flexibility transformation of the thermal power unit is completed, the minimum output of the thermal power unit changes to and the maximum ramp rate changes to is a 0-1 variable indicating whether the thermal power unit undergoes flexibility transformation. If the thermal power unit undergoes flexibility transformation, it is 1; if not, it is 0. is the ramp rate of the thermal power unit after transformation.

[0220] b. Output and ramp rate constraints of gas turbine units:

[0221]

[0222] Among them is the gas consumption of the gas turbine unit; HHV is the high heating value; αj is the thermoelectric ratio. is a 0-1 variable indicating whether the gas turbine unit is built. If the gas turbine unit is built, it is 1; if not, it is 0. and are the reserve capacities required for the down-ramp and up-ramp of the gas turbine unit respectively, is the maximum value of the ramp rate of the gas turbine unit.

[0223] c. Energy storage constraint

[0224]

[0225] where is the capacity value of the s-th energy storage battery pack at time t; and are the charging coefficient and discharging coefficient respectively. is a 0-1 variable indicating whether the source-side energy storage is built. If the source-side energy storage is built, it is 1; if not, it is 0. In addition, the upper reserve and lower reserve capacities of the energy storage also have certain limitations.

[0226] In specific implementation, based on the IEEE-24 bus system and the 12-bus power-gas integrated energy system, a collaborative optimal dispatch model of the power-gas integrated energy system considering multiple types of flexibility measures was simulated. Then, through different examples, the impacts of energy storage, reserve capacity deliverability, and price parameters on the collaborative optimal dispatch of the power-gas integrated energy system were compared and analyzed, and the effectiveness of the ADMM algorithm with adaptive penalty parameters proposed in this application was analyzed.

[0227] In this embodiment, the IEEE-24 bus power system includes 10 thermal power units G1-G10, 4 wind farms W1-W4, and 4 candidate energy storage devices EES1-EES4. The power system and the natural gas system are interconnected by four candidate gas turbine units GasG1-GasG4. The 12-bus natural gas system contains three gas sources N1-N3, 10 natural gas pipelines, and 4 natural gas loads. Table 3 shows the installed capacities and installation locations of the candidate flexibility resources for planning. In addition, specific flexibility retrofit measures for thermal power units are also included, which can also be regarded as technical solutions to improve the flexibility of the power system.

[0228] The collaborative planning model of the electrical integrated energy system considering multiple types of flexibility resources was simulated and calculated. The optimization model was transformed into a mixed-integer linear programming model, and the final planning results are shown in Table 3.

[0229] Table 3 Planning Results

[0230]

[0231] Compare the specific advantages of the planning schemes in terms of economy and flexibility. First, analyze the annual total cost of the planning. The flexible planning strategies adopted by Schemes 2 / 3 / 4 can all reduce the annual total cost, which is reduced from the original 267.65 million yuan to 171.05 million yuan, 166.20 million yuan, and 167.49 million yuan respectively. As can be seen from the following table, although the investment and construction cost of the gas turbine unit in Scheme 3 is the highest, its operating cost after investment and construction is much lower than that of Schemes 2 and 4. This is mainly because the reduction of gas price enables the system to make full use of the electricity converted from natural gas to supply the electrical load, thus obtaining the best annual total economic effect.

[0232] Table 4 Cost Comparison of Different Planning Schemes

[0233]

[0234] Next, analyze the operating cost of each scheme under a typical day. As Figure 5 shown, and analyze it in combination with the wind curtailment situation during the operation process. By comparing Schemes 2 / 3 / 4, although Scheme 3 has added the gas supply cost of the new natural gas source (yellow bar chart), under Scheme 3, the system makes full use of the advantage of low wind power generation cost and reduces the output of high-cost units on the basis of sufficient energy supply, thus making the daily operating total cost the lowest and the economy the best. The standby supply to a certain extent reflects the system's ability to cope with the uncertainties of wind power, load, and photovoltaic output. Compared with Scheme 1, Schemes 2 / 3 / 4 have all eliminated the risk cost of insufficient standby, but under the condition of meeting sufficient standby, the standby supply cost of Scheme 3 is less (orange bar chart), which benefits from the cheaper standby supply capacity of the gas turbine unit.

[0235] From the perspective of standby scheduling ( Figures 6 - 9 ), the specific analysis of the 24-hour daily standby of each planning scheme is as follows. By retrofitting the flexibility of conventional units, investing in and constructing gas turbine units, and investing in and constructing energy storage power stations, sufficient standby capacity can be provided to meet the standby demand of the system at each time period (no standby shortage phenomenon appears). Schemes 3 / 4 can reduce the standby pressure of conventional units (i.e., the blue part decreases) by investing in different flexible resources (gas turbine units / energy storage).

[0236] In fact, insufficient downward flexibility is an important reason for wind curtailment and photovoltaic curtailment. Under the background of this project, the system gives priority to considering the impact of downward flexibility on the clean energy consumption capacity. By comparing Schemes 1, 2, and 4, it can be seen that compared with thermal power units, the gas turbine unit has a larger downward regulation range, and the consumption advantage in dealing with the scenario of high proportion of clean energy access is more obvious.

[0237] (1) Flexibility Assessment

[0238] Based on the basic characteristics of the flexibility of the aforementioned power system, a deterministic assessment of the overall flexibility of the system planning results will be carried out from the perspective of the power balance of flexibility supply and demand. The flexibility assessment results of different planning schemes are specifically shown in Table 5 as follows:

[0239] Table 5 Comparison of Flexibility Assessments for Each Scheme

[0240]

[0241]

[0242] Taking one hour (60 min) as the time scale for operation simulation, each scheme will be comprehensively evaluated from the perspectives of flexibility, wind power accommodation level economy, and carbon emissions.

[0243] In the context of high proportion of clean energy access, the phenomenon of wind and light curtailment is mainly directly related to the downward flexibility of the system, which will be taken as the main analysis point here. It can be seen that whether it is #transformation, #gas, or #energy storage, after adding different types of flexibility resources to the planning, the overall downward flexibility adequacy of the system has been improved, increasing from 2.7683 MW / min to 2.7744 MW / min, 2.7776 MW / min, and 2.7774 MW / min respectively; correspondingly, the downward flexibility deficiency of the three planning schemes has also decreased, and even been completely eliminated in the #energy storage scheme; from the perspective of the downward flexibility adequacy rate, different schemes have increased from 87.60% in the initial state to 95.87%, 97.03%, and 100% respectively, indicating that the duration of the risk of insufficient system flexibility has been significantly shortened, which shows that by planning different flexibility resources, the risk brought by insufficient flexibility can be greatly reduced and the overall flexibility can be improved.

[0244] Next, compare the advantages and disadvantages of the three planning schemes in different aspects. It can be clearly seen from the evaluation results that, whether it is for promoting wind power consumption, reducing costs or reducing carbon emissions, if the convenience of on-site completion of the #transformation technology is not considered, #gas is the best choice under various conditions. From the perspective of promoting wind power consumption, compared with coal-fired units, gas units have a lower minimum technical output and a higher ramp rate, which means they have a wider adjustable range and a faster response speed to adapt to a large amount of wind power output and the wind power fluctuations at adjacent times. The economic advantages of #gas are already known from the results analyzed in the previous section. From an environmental protection perspective, the annual total carbon emissions of the system after #gas planning have decreased significantly, by 18.93% and 18.75% respectively compared with the #transformation and #energy storage planning schemes. The reason is that the introduction of gas units with a lower carbon emission intensity per unit power output has changed the power supply structure of the system, sharing the pressure of the original system relying solely on coal-fired units for power generation to a certain extent, and ultimately reducing the total carbon emissions. However, this situation has not been improved in #transformation and #energy storage, because the main carbon emission source of the system is still coal-fired units with a higher carbon emission intensity at this time.

[0245] However, the #gas scheme also has certain limitations, mainly reflected in the risk of downward regulation flexibility. From theoretical analysis, it can be known that downward regulation flexibility is directly related to the wind curtailment rate, that is, the larger the index value of downward regulation flexibility adequacy, the less wind curtailment. However, from the results in the table, it can be seen that although #gas has the lowest wind curtailment rate, there is a situation of insufficient downward regulation flexibility (the inadequacy is not equal to 0 and the adequacy rate is not equal to 100%), indicating that the introduction of #gas may lead to the risk of insufficient downward regulation flexibility supply in the system. #gas will promote more wind power consumption at the cost of this risk. A similar situation also occurs in #transformation. The above phenomenon shows that it may be difficult to ensure the flexible operation of the system under the premise of safety only relying on the inherent technical characteristics of flexible power sources. Considering the safe operation and risk elimination of all planning scenarios, #energy storage is a better choice at this time - with a low cost and wind curtailment rate similar to #gas, but without the risk of insufficient flexibility.

[0246] Example 2

[0247] As Figure 10 shown, the difference between this embodiment and Embodiment 1 is that this embodiment further provides a collaborative planning system for an electrical integrated energy system for operation flexibility, which corresponds one-to-one with the collaborative planning method for an electrical integrated energy system for operation flexibility in Embodiment 1; the system includes:

[0248] An acquisition unit, configured to acquire multiple types of typical scenarios formed by clustering historical load and wind power output data;

[0249] The first construction unit is used to construct an electrical integrated energy system with multiple energy storages based on the power system and the natural gas system;

[0250] The second construction unit is used to input data of multiple typical scenarios into the electrical integrated energy system, construct a collaborative optimization scheduling model of the electrical integrated energy system, and solve to obtain various operating costs;

[0251] The third construction unit is used to construct a collaborative planning model of the electrical integrated energy system by considering various flexibility resources such as the flexibility transformation of thermal power units, the construction of gas turbines, and the construction of energy storage power stations;

[0252] The collaborative planning and solving unit is used to take the minimum annual total cost formed by various operating costs as the planning objective function, and take the first constraint condition and the second constraint condition as constraint conditions to solve the collaborative planning model to obtain the optimal collaborative planning scheme; where: the first constraint condition is a general constraint condition, and the second constraint condition is a flexibility resource characteristic constraint condition.

[0253] As a further implementation, the second construction unit includes:

[0254] The first sub-unit is used to construct a collaborative optimization scheduling model of the electrical integrated energy system with the minimum total operating cost within the scheduling period of the electrical integrated energy system as the objective function and the reserve callability constraint under different typical scenarios as the constraint condition;

[0255] The second sub-unit is used to solve the collaborative optimization scheduling model by using the alternating direction multiplier method with an adaptive penalty parameter to obtain various operating costs.

[0256] Among them, the execution process of each unit can be carried out according to the process steps of the collaborative planning method of the electrical integrated energy system for operation flexibility in Embodiment 1, and will not be elaborated one by one in this embodiment.

[0257] At the same time, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the above-mentioned collaborative planning method of the electrical integrated energy system for operation flexibility is implemented.

[0258] At the same time, the present invention also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned collaborative planning method of the electrical integrated energy system for operation flexibility is implemented.

[0259] The specific embodiments described above further elaborate on the purpose, technical solutions, and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A collaborative planning method for an electrical integrated energy system oriented to operational flexibility, characterized in that: The method includes: Obtain multiple typical scenarios formed by clustering historical load and wind power output data; Based on the power system and natural gas system, build an electrical integrated energy system with multiple energy storage; Inputting the data of the multiple typical scenarios into the electrical integrated energy system, constructing a collaborative optimization scheduling model of the electrical integrated energy system, and solving various operating costs; Considering the flexibility transformation of thermal power units, the construction of gas units and the construction of energy storage power stations, a collaborative planning model for the electrical integrated energy system is constructed; Taking the minimum annual total cost formed by various operating costs as the planning objective function, and taking the first constraint condition and the second constraint condition as the constraint condition, the collaborative planning model is solved to obtain the optimal collaborative planning solution; Among them: the first constraint condition is a common constraint condition, and the second constraint condition is a flexibility resource characteristic constraint condition.

2. The method for collaborative planning of an electrical integrated energy system for operational flexibility according to claim 1, characterized in that: Construct a collaborative optimization dispatch model for the electrical integrated energy system and solve various operating costs, including: Taking the minimum total operating cost within the dispatch period of the electric integrated energy system as the objective function and the reserve availability constraints under different typical scenarios as the constraint conditions, a collaborative optimization dispatch model of the electric integrated energy system is constructed; The collaborative optimization scheduling model is solved by using an alternating direction multiplier method with adaptive penalty parameters to obtain various operating costs.

3. The method for collaborative planning of an electrical integrated energy system for operational flexibility according to claim 2 is characterized in that: The total operating cost during the dispatching period includes energy supply cost, wind abandonment and load reduction cost, and reserve capacity cost; The energy supply cost includes the power generation cost of the generator, the natural gas production cost, the charging and discharging cost of the energy storage power station and the interruption compensation cost of the interruptible load; The wind abandonment and load reduction costs include the first wind abandonment and load reduction costs and the second wind abandonment and load reduction costs. The first wind abandonment and load reduction costs refer to the wind abandonment and load loss costs without considering the reserve capacity deliverability verification, and the second wind abandonment and load reduction costs refer to the load loss costs considering the reserve capacity deliverability verification. The backup capacity cost includes a first backup capacity cost and a second backup capacity cost. The first backup capacity cost refers to the backup capacity cost of the generator set, the energy storage power station and the interruptible load, and the second backup capacity cost refers to the backup shortage risk cost.

4. The method for collaborative planning of an electrical integrated energy system for operational flexibility according to claim 2, characterized in that: The reserve availability constraints include generator shutdown constraints, line interruption constraints and natural gas pipeline constraints.

5. The method for collaborative planning of an electrical integrated energy system for operational flexibility according to claim 2, characterized in that: The penalty parameter update formula in the alternating direction multiplier method of the adaptive penalty parameter is: In the formula, is the penalty parameter for the n+1th iteration, is the penalty parameter for the nth iteration, is the original residual of the nth iteration, is the dual residual of the nth iteration.

6. The method for collaborative planning of an electrical integrated energy system for operational flexibility according to claim 1, characterized in that: The total annual cost includes investment cost and annual operating cost; The investment cost is the cost of the initial one-time investment in the flexibility resources to be planned; The annual operating cost is the total cost of the electrical integrated energy system after considering the investment in different flexibility resources, including the annual power generation cost, annual reserve cost, annual wind abandonment cost, annual gas supply cost, annual energy storage operating cost and annual reserve shortage risk cost. The flexibility resource characteristic constraints include the output and ramp constraints of thermal power units after flexibility transformation, the output and ramp constraints of gas units, and energy storage constraints.

7. The electrical integrated energy system collaborative planning system for operational flexibility is characterized by: The system includes: An acquisition unit, used to acquire multiple typical scenarios formed by clustering historical load and wind power output data; The first construction unit is used to construct an electrical integrated energy system with multiple energy storages based on an electric power system and a natural gas system; The second construction unit is used to input the data of the multiple types of typical scenarios into the electrical integrated energy system, construct a collaborative optimization scheduling model of the electrical integrated energy system, and solve for various operating costs; The third construction unit is used to consider the flexibility transformation of thermal power units, the construction of gas units and the construction of energy storage power stations, and to build a collaborative planning model for the electrical integrated energy system; The collaborative planning solving unit is used to solve the collaborative planning model with the minimum annual total cost formed by various operating costs as the planning objective function and the first constraint condition and the second constraint condition as the constraint condition to obtain the optimal collaborative planning solution; wherein: the first constraint condition is a common constraint condition and the second constraint condition is a flexibility resource characteristic constraint condition.

8. The electrical integrated energy system collaborative planning system for operational flexibility according to claim 7 is characterized in that: The second building block comprises: The first subunit is used to construct a collaborative optimization scheduling model for the electric integrated energy system by taking the minimum total operating cost within the scheduling period of the electric integrated energy system as the objective function and the backup availability constraints under different typical scenarios as the constraint conditions; The second subunit is used to solve the collaborative optimization scheduling model by adopting the alternating direction multiplier method of adaptive penalty parameters to obtain various operating costs.

9. An electronic 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, it implements the collaborative planning method of the electrical integrated energy system for operational flexibility as described in any one of claims 1 to 6.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for collaborative planning of an electrical integrated energy system for operational flexibility as described in any one of claims 1 to 6 is implemented.

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