WO2020118734A1 - 一种分布式储能调度方法和装置 - Google Patents

一种分布式储能调度方法和装置 Download PDF

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WO2020118734A1
WO2020118734A1 PCT/CN2018/121392 CN2018121392W WO2020118734A1 WO 2020118734 A1 WO2020118734 A1 WO 2020118734A1 CN 2018121392 W CN2018121392 W CN 2018121392W WO 2020118734 A1 WO2020118734 A1 WO 2020118734A1
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energy storage
distributed energy
traffic
power
parameters
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PCT/CN2018/121392
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English (en)
French (fr)
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李相俊
甘伟
马力
刘汉民
贾学翠
董文琦
岳巍澎
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国网新源张家口风光储示范电站有限公司
中国电力科学研究院有限公司
国家电网有限公司
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Publication of WO2020118734A1 publication Critical patent/WO2020118734A1/zh

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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
    • H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers

Definitions

  • the invention relates to the technical field of electrical engineering, in particular to a distributed energy storage scheduling method and device.
  • distributed energy storage scheduling generally considers its functions of peak shaving, voltage regulation, and promotion of new energy consumption. Based on a given charging load curve, the role of distributed energy storage scheduling in reducing charging load is determined. The simplification ignores the spatial transferability of electric vehicle charging demand. Its distributed energy storage scheduling strategy will make the operating cost of the power system higher. At the same time, it underestimates the ability of the distributed energy storage grid to accept the charging load, which has a great impact With the access of large-scale charging load, the acceptance capacity of charging load is poor.
  • the present invention provides a distributed energy storage scheduling method and device for obtaining scheduling parameters; solving pre-built distributed energy storage Scheduling model to obtain distributed energy storage scheduling strategy; distributed energy storage scheduling model includes thermal power unit operating costs and additional commuting time costs, scheduling parameters include transportation network parameters, traffic demand parameters, power equipment parameters and power grid parameters.
  • the present invention considers the coupling effect of the traffic flow distribution method and the distributed energy storage scheduling method, which reduces the impact of the charging load on the power system through the spatial transfer of the traffic flow, reduces the total operating cost of the power system and the transportation network, and improves the charging Load capacity.
  • the present invention provides a distributed energy storage scheduling method, including:
  • the distributed energy storage scheduling model includes the operating cost of the thermal power unit and the cost of additional commuting time;
  • the scheduling parameters include traffic network parameters, traffic demand parameters, power equipment parameters, and power grid parameters.
  • the traffic network parameters include traffic capacity of traffic sections
  • the traffic demand parameters include the traffic demand of the traffic load at various times and the cost coefficient of commuting time;
  • the power equipment parameters include thermal power unit parameters, wind power unit parameters, distributed energy storage parameters, and active power of the power load;
  • the grid parameters include the total number of periods, the transmission capacity of the power line, the reserve capacity adjusted upward by the power grid, and the reserve capacity adjusted downward by the power grid.
  • the thermal power unit parameters include the number of thermal power units, the upper output limit, the lower output limit, the maximum uphill climbing rate, the maximum downhill climbing rate, and the coal consumption curve parameters;
  • the wind turbine parameters include the maximum wind curtailment rate and the predicted power generation
  • the distributed energy storage parameters include charging efficiency, discharging efficiency, power capacity, energy capacity, and initial energy.
  • the construction of the distributed energy storage scheduling model includes:
  • the construction of the traffic flow distribution model includes:
  • the constraint conditions include traffic flow balance constraints, alternative path constraints, traffic section flow distribution constraints, and traffic section capacity constraints.
  • the construction of the power system dispatching model includes:
  • the constraint conditions include thermal power unit constraints, wind power unit constraints, distributed energy storage constraints and power grid constraints.
  • thermal power unit constraints are as follows:
  • the distributed energy storage constraint is as follows:
  • Is the discharge power of distributed energy storage k in period t Is the charging power of distributed energy storage k in period t, Is the active power of distributed energy storage k in period t; Is the power capacity of distributed energy storage k;
  • U kt is the charge and discharge state of distributed energy storage k during t period, when charging state, U kt takes 0, when discharging state, U kt takes 1;
  • E kt is distributed during t period Residual energy of energy storage k, E k,t-1 is the residual energy of distributed energy k during t-1 period, E k0 is the initial energy of distributed energy storage k, E kT is the distributed energy of distributed energy k during T period Remaining energy Is the energy capacity of distributed energy storage k in period t, ⁇ c is the charging efficiency of distributed energy storage k, and ⁇ d is the discharge efficiency of distributed energy storage k;
  • Is the active power flowing into node p during period t Is the active power flowing out of node p during time t, Is the active power of the electrical load at node p in period t, Is the lower output limit of thermal power unit i, Is the upper output limit of the thermal power unit i;
  • x a, t is the traffic flow assigned to the traffic section a at time t;
  • ⁇ is the conversion coefficient of the traffic flow and the charging load;
  • Adjust the reserve capacity upwards for the power grid at time t The power grid adjusts the reserve capacity downward for period t;
  • S l is the transmission capacity of power line l, and P lt is the transmission power of power line l for period t.
  • the objective function of the distributed energy storage scheduling model is as follows:
  • F is the sum of the operating cost of the thermal power unit and the extra commuting time cost;
  • the operating cost of the thermal power unit Is the actual commuting time cost, Is the minimum commuting time cost;
  • T is the total number of time periods, N g is the number of thermal power units, w is the commuting time cost coefficient, ⁇ a is the traffic road set, and ⁇ d is the traffic load set,
  • Is the commute time of traffic segment a, t d,1 is the commute time of the shortest path corresponding to traffic load d, x a,t is the traffic flow assigned to traffic segment a during t period, q d,t is traffic load d during t period Traffic demand.
  • the solution to the pre-built distributed energy storage scheduling model to obtain a distributed energy storage scheduling strategy includes:
  • the active power of the distributed energy storage is obtained.
  • the present invention provides a distributed energy storage scheduling device, including:
  • Acquisition module for acquiring scheduling parameters
  • Solving module used to solve the pre-built distributed energy storage scheduling model to get the distributed energy storage scheduling strategy
  • the distributed energy storage scheduling model includes the operating cost of the thermal power unit and the cost of additional commuting time;
  • the scheduling parameters include traffic network parameters, traffic demand parameters, power equipment parameters, and power grid parameters.
  • the present invention also provides a computer storage medium that stores computer-executable instructions that are used to execute a distributed energy storage scheduling method.
  • the present invention also provides an electronic device, including: at least one processor, at least one memory, and computer program instructions stored in the memory, which are implemented when the computer program instructions are executed by the processor Described method.
  • the electronic device further includes: at least one communication interface for acquiring traffic network parameters, traffic demand parameters, power equipment parameters, and power grid parameters.
  • the electronic device is a device where a controller, a PC or a control platform is located.
  • the scheduling parameters are obtained; the pre-built distributed energy storage scheduling model is solved to obtain the distributed energy storage scheduling strategy; the distributed energy storage scheduling model includes the operating cost of the thermal power unit and additional commuting time Cost and scheduling parameters include transportation network parameters, transportation demand parameters, power equipment parameters and power grid parameters, which reduces the total operating cost of the power system and the transportation network and improves the acceptance capacity of the charging load;
  • the distributed energy storage scheduling device includes an obtaining module and a solving module.
  • the obtaining module is used to obtain scheduling parameters;
  • the solving module is used to solve a pre-built distributed energy storage scheduling model to obtain a distributed energy storage scheduling strategy;
  • the distributed energy storage scheduling model includes thermal power unit operating costs and additional commuting time costs.
  • the scheduling parameters include transportation network parameters, traffic demand parameters, power equipment parameters, and grid parameters, which reduces the total operating costs of the power system and the transportation network and improves charging. Load acceptance capacity;
  • the invention aims at a coupling system composed of a transportation network and an electric power system.
  • a linear and easy-to-solve distributed energy storage scheduling model is established, and the solution is guaranteed by calling the solver to ensure the distributed storage The accuracy of the scheduling model solution;
  • the present invention considers the coupling effect of the traffic flow distribution method and the distributed energy storage scheduling method, and reduces the impact of the charging load on the power system through the spatial transfer of the traffic flow, providing a theoretical basis for the large-scale access of the charging load.
  • FIG. 1 is a flowchart of a distributed energy storage scheduling method in an embodiment of the present invention.
  • Embodiment 1 of the present invention provides a distributed energy storage scheduling method.
  • the specific flowchart is shown in FIG. 1, and the specific process is as follows:
  • the above distributed energy storage scheduling model includes the operating costs of thermal power units and the cost of additional commuting time;
  • Dispatch parameters include traffic network parameters, traffic demand parameters, power equipment parameters, and power grid parameters.
  • the traffic network parameters include the traffic capacity of the traffic section
  • the traffic demand parameters include the traffic demand and the cost coefficient of commuting time in various periods;
  • Power equipment parameters include thermal power unit parameters, wind power unit parameters, distributed energy storage parameters, and active power of electrical loads;
  • the grid parameters include the total number of time periods, the transmission capacity of the power line, the reserve capacity adjusted upward by the power grid and the reserve capacity adjusted downward by the power grid.
  • the thermal power unit parameters include the number of thermal power units, the upper output limit, the lower output limit, the maximum ascent rate, the maximum ascent rate and the coal consumption curve parameters;
  • the parameters of the wind turbine include the maximum wind curtailment rate and the predicted generating power
  • the distributed energy storage parameters include charging efficiency, discharging efficiency, power capacity, energy capacity and initial energy.
  • the construction of distributed energy storage scheduling model includes:
  • the construction of traffic flow distribution model includes:
  • the constraint conditions include traffic flow balance constraints, alternative path constraints, traffic section flow distribution constraints, and traffic section capacity constraints.
  • the first objective function is as follows:
  • the traffic flow balance constraint is as follows:
  • f d, c, t represents the traffic flow allocated on the alternative path c under the traffic load d at time t
  • q d, t is the traffic demand of the traffic load d at time t
  • the traffic flow distribution constraint of the traffic section is as follows:
  • x a,t is the traffic flow assigned to the traffic segment a at time t
  • a d,c,a is the association relationship between the alternative path c and the traffic segment a under the traffic load d
  • the alternative path c is associated with the traffic segment a
  • a d,c,a takes 1
  • the alternative path c is not related to the traffic segment a
  • a d,c,a takes 0.
  • the construction of power system dispatching model includes:
  • the constraint conditions include thermal power unit constraints, wind power unit constraints, distributed energy storage constraints and grid constraints.
  • the second objective function is as follows:
  • F 1 is the operating cost of the thermal power unit
  • T is the total time period
  • N g is the number of thermal power units
  • It is the operating cost of the ith thermal power unit in period t.
  • thermal power unit constraints are as follows:
  • Ug it represents the on-off state of the thermal power unit i in period t.
  • Ug When the thermal power unit i is turned on, Ug it takes 1; when the thermal power unit i is turned off, Ug it takes 0; Is the power generation cost when the thermal power unit i is at the minimum technical output;
  • cg im represents the unit power coal consumption cost of the thermal power unit i at the mth section of the coal consumption curve; Is the power generated by the thermal power unit i at the mth section of the coal consumption curve at time t;
  • P G,it is the active power of the thermal power unit i at time t, and P G,it-1 is the active power of the thermal power unit i at time t-1;
  • Is the lower output limit of thermal power unit i Is the upper limit of the output of thermal power unit i in the mth section of the coal consumption curve;
  • Is the maximum downhill climbing rate of thermal power unit i Is the maximum climbing speed of thermal power unit i;
  • the distributed energy storage constraint is as follows:
  • Is the discharge power of distributed energy storage k in period t Is the charging power of distributed energy storage k in period t, Is the active power of distributed energy storage k in period t; Is the power capacity of distributed energy storage k;
  • U kt is the charge and discharge state of distributed energy storage k during t period, when charging state, U kt takes 0, when discharging state, U kt takes 1;
  • E kt is distributed during t period Residual energy of energy storage k, E k,t-1 is the residual energy of distributed energy k during t-1 period, E k0 is the initial energy of distributed energy storage k, E kT is the distributed energy of distributed energy k during T period Remaining energy Is the energy capacity of distributed energy storage k in period t, ⁇ c is the charging efficiency of distributed energy storage k, and ⁇ d is the discharge efficiency of distributed energy storage k;
  • Grid constraints mainly include power system power balance constraints, rotation reserve constraints, and line transmission capacity limits.
  • Rotary reserve is the sum of the maximum output of all operating units minus the current system load and losses. Standby is an important measure to ensure the reliable power supply of the system, to prevent the system from a serious load shortage when one or several units fail, resulting in a sudden drop in system frequency and failure.
  • additional rotation reserves need to be configured to deal with wind power fluctuations. Therefore, in order to ensure the safe operation of the system, when arranging the start and stop plan of the unit, it should be considered to arrange sufficient unit operation to meet the needs of system backup. Changes in wind power output and energy storage system charging and discharging may cause relatively large changes in network power flow.
  • system grids need to be considered when deciding on unit output and wide area energy storage system charging and discharging plans.
  • Policy support wind farms are often connected to the grid faster than conventional power sources and supporting transmission grids, making power systems often limit the grid’s ability to accept wind power due to limited conventional power regulation support or insufficient transmission line transmission capacity, so The system grid topology and the upper limit of transmission thermal stability must be considered.
  • Is the active power flowing into node p during period t Is the active power flowing out of node p during time t, Is the active power of the electrical load at node p in period t, Is the lower output limit of thermal power unit i, Is the upper output limit of the thermal power unit i;
  • x a, t is the traffic flow assigned to the traffic section a at time t;
  • ⁇ is the conversion coefficient of the traffic flow and the charging load;
  • Adjust the reserve capacity upwards for the power grid at time t The power grid adjusts the reserve capacity downward for period t;
  • S l is the transmission capacity of power line l, and P lt is the transmission power of power line l for period t.
  • the above distributed energy storage scheduling model includes:
  • F is the sum of the operating cost of the thermal power unit and the extra commuting time cost
  • Wardrop In the modeling of urban traffic scheduling model, the traffic demand of each O-D pair needs to be allocated to the actual road network. In the traffic flow distribution, it is necessary to satisfy the Wardrop first principle or Wardrop second principle.
  • Wardrop's first-principle principle is also known as the user balance principle. Under this principle, after considering traffic congestion, the commuting time of the alternative paths that actually allocate power flow will be equal. That is, any passenger cannot find a faster route by changing his route choice.
  • the second principle of Wardrop is also called the optimal principle, that is, assuming a dispatch center, the commuting time of the system will reach the minimum under the flow distribution of the dispatch center.
  • the constraints include traffic network constraints and power system constraints;
  • Traffic network constraints include traffic flow balance constraints, alternative path constraints, traffic segment flow distribution constraints and traffic segment capacity constraints;
  • Power system constraints include thermal power unit constraints, wind power unit constraints, distributed energy storage constraints and grid constraints.
  • a pre-built distributed energy storage scheduling model is solved to obtain a distributed energy storage scheduling strategy, which specifically takes transportation network parameters, traffic demand parameters, power equipment parameters, and power grid parameters as input items, based on a simulation computing platform, and
  • the solution tool is called to solve the distributed energy storage scheduling model to obtain the active power of the distributed energy storage (ie ).
  • Embodiment 2 of the present invention also provides a distributed energy storage scheduling device, including an acquisition module and a solution module.
  • a distributed energy storage scheduling device including an acquisition module and a solution module.
  • Acquisition module for acquiring scheduling parameters
  • Solving module used to solve the pre-built distributed energy storage scheduling model to get the distributed energy storage scheduling strategy
  • the above distributed energy storage scheduling model includes the operating costs of thermal power units and the cost of additional commuting time;
  • Dispatch parameters include traffic network parameters, traffic demand parameters, power equipment parameters and power grid parameters;
  • the traffic network parameters include the traffic capacity of the traffic section
  • the traffic demand parameters include the traffic demand and the cost coefficient of commuting time in various periods;
  • Power equipment parameters include thermal power unit parameters, wind power unit parameters, distributed energy storage parameters, and active power of electrical loads;
  • the grid parameters include the total number of time periods, the transmission capacity of the power line, the reserve capacity adjusted upward by the power grid and the reserve capacity adjusted downward by the power grid.
  • the thermal power unit parameters include the number of thermal power units, the upper output limit, the lower output limit, the maximum ascent rate, the maximum ascent rate and the coal consumption curve parameters;
  • the parameters of the wind turbine include the maximum wind curtailment rate and the predicted generating power
  • the distributed energy storage parameters include charging efficiency, discharging efficiency, power capacity, energy capacity and initial energy.
  • the apparatus provided in Embodiment 2 of the present invention further includes a modeling module.
  • the modeling module includes:
  • the first solving unit is used to solve the pre-constructed traffic flow distribution model based on the traffic capacity of the traffic section to obtain extra commuting time cost;
  • the second solving unit is used to solve the pre-built power system scheduling model to obtain the operating cost of the thermal power unit;
  • the modeling unit is used to build a distributed energy storage scheduling model with the minimum sum of the operating cost of the thermal power unit and the additional commuting time cost as the constraint, and to modify the power balance of the power system considering the charging load.
  • the first solving unit is specifically used for:
  • the constraint conditions include traffic flow balance constraints, alternative path constraints, traffic section flow distribution constraints, and traffic section capacity constraints.
  • the first objective function is as follows:
  • the traffic flow balance constraint is as follows:
  • f d, c, t represents the traffic flow allocated on the alternative path c under the traffic load d at time t
  • q d, t is the traffic demand of the traffic load d at time t
  • the traffic flow distribution constraint of the traffic section is as follows:
  • x a,t is the traffic flow assigned to the traffic segment a at time t
  • a d,c,a is the association relationship between the alternative path c and the traffic segment a under the traffic load d
  • the alternative path c is associated with the traffic segment a
  • a d,c,a takes 1
  • the alternative path c traffic section a is not related
  • a d,c,a takes 0.
  • the second solving unit is specifically used for:
  • the constraint conditions include thermal power unit constraints, wind power unit constraints, distributed energy storage constraints and power grid constraints.
  • the second objective function is as follows:
  • F 1 is the operating cost of the thermal power unit
  • T is the total time period
  • N g is the number of thermal power units
  • It is the operating cost of the ith thermal power unit in period t.
  • the thermal power unit constraints are as follows:
  • Ug it represents the on-off state of the thermal power unit i in period t.
  • Ug When the thermal power unit i is turned on, Ug it takes 1; when the thermal power unit i is turned off, Ug it takes 0; Is the power generation cost when the thermal power unit i is at the minimum technical output;
  • cg im represents the unit power coal consumption cost of the thermal power unit i at the mth section of the coal consumption curve; Is the power generated by the thermal power unit i at the mth section of the coal consumption curve at time t;
  • P G,it is the active power of the thermal power unit i at time t, and P G,it-1 is the active power of the thermal power unit i at time t-1;
  • Is the lower output limit of thermal power unit i Is the upper limit of the output of thermal power unit i in the mth section of the coal consumption curve;
  • Is the maximum downhill climbing rate of thermal power unit i Is the maximum climbing speed of thermal power unit i;
  • the distributed energy storage constraint is as follows:
  • Is the discharge power of distributed energy storage k in period t Is the charging power of distributed energy storage k in period t, Is the active power of distributed energy storage k in period t; Is the power capacity of distributed energy storage k;
  • U kt is the charge and discharge state of distributed energy storage k during t period, when charging state, U kt takes 0, when discharging state, U kt takes 1;
  • E kt is distributed during t period Residual energy of energy storage k, E k,t-1 is the residual energy of distributed energy k during t-1 period, E k0 is the initial energy of distributed energy storage k, E kT is the distributed energy of distributed energy k during T period Remaining energy Is the energy capacity of distributed energy storage k in period t, ⁇ c is the charging efficiency of distributed energy storage k, and ⁇ d is the discharge efficiency of distributed energy storage k;
  • Is the active power flowing into node p during period t Is the active power flowing out of node p during time t, Is the active power of the electrical load at node p in period t, Is the lower output limit of thermal power unit i, Is the upper output limit of the thermal power unit i;
  • x a, t is the traffic flow assigned to the traffic section a at time t;
  • ⁇ is the conversion coefficient of the traffic flow and the charging load;
  • Adjust the reserve capacity upwards for the power grid at time t The power grid adjusts the reserve capacity downward for period t;
  • S l is the transmission capacity of power line l, and P lt is the transmission power of power line l for period t.
  • the distributed energy storage scheduling model includes:
  • F is the sum of the operating cost of the thermal power unit and the extra commuting time cost
  • the above solution module takes the transportation network parameters, transportation demand parameters, power equipment parameters and power grid parameters as input items, based on the simulation calculation platform, and calls the solution tool to solve the distributed energy storage scheduling model to obtain the distributed energy storage active power.
  • Embodiment 3 of the present invention provides a computer storage medium in which computer executable instructions are stored, and the computer executable instructions are used to execute the distributed energy storage scheduling method of Embodiment 1 above.
  • Embodiment 4 of the present invention provides an electronic device.
  • the electronic device includes: at least one processor, at least one memory, and computer program instructions stored in the memory, and the embodiment is implemented when the computer program instructions are executed by the processor 1 Method provided.
  • the above electronic device further includes: at least one communication interface for obtaining traffic network parameters, traffic demand parameters, power equipment parameters, and power grid parameters.
  • the above electronic device is a device where a controller, a PC or a control platform is located.
  • a controller a PC or a control platform
  • each part of the device described above is divided into various modules or units in terms of function and described separately.
  • the functions of each module or unit can be implemented in one or more software or hardware when implementing this application.
  • the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer usable program code.
  • computer usable storage media including but not limited to disk storage, CD-ROM, optical storage, etc.
  • These computer program instructions may also be stored in a computer-readable memory that can guide a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction device, the instructions
  • the device implements the functions specified in one block or multiple blocks of the flowchart one flow or multiple flows and/or block diagrams.
  • These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operating steps are performed on the computer or other programmable device to produce computer-implemented processing, which is executed on the computer or other programmable device
  • the instructions provide steps for implementing the functions specified in one block or multiple blocks of the flowchart one flow or multiple flows and/or block diagrams.

Abstract

一种分布式储能调度方法和装置，获取调度参数（S101）；求解预先构建的分布式储能调度模型，得到分布式储能调度策略（S102）；分布式储能调度模型包括火电机组运行费用和额外通勤时间成本，调度参数包括交通网参数、交通需求参数、电力设备参数和电网参数。通过引入交通潮流与充电负荷的转换系数，建立线性、易于快速求解的分布式储能调度模型，并通过调用求解器求解保证了分布式储能调度模型求解的精确度；考虑交通潮流分配方式与分布式储能调度方式的耦合影响，通过交通潮流在空间上的转移减缓充电负荷对电力系统的冲击，降低了电力系统与交通网的总运行成本，提升了充电负荷的接纳能力，为充电负荷的大规模接入提供理论基础。

Description

一种分布式储能调度方法和装置 技术领域
本发明涉及电气工程技术领域，具体涉及一种分布式储能调度方法和装置。
背景技术
随着电动汽车销量的逐年递增，由2013年的4万台增长到2017年的153万台，电动汽车充电在电力负荷中占据越来越重要的位置。但由于交通出行的潮汐特性，电动汽车充电负荷峰谷差距大，给电力系统为大规模充电汽车供电带来了挑战。一方面，较高的峰谷差率对电力系统调峰能力、网络传输能力提出了更高的要求；另一方面，当电力系统网络传输能力受限时，电动汽车需改变其充电点以适应电力系统的要求，从而改变交通网中潮流分配方式，交通通勤时间成本可能增加。
大规模储能系统可对电网负荷“削峰填谷”，实现部分负荷的时空平移，减少电网等效负荷峰谷差，进而松弛电网向下调峰瓶颈。通过在电网中引入分布式储能系统并对其合理调度，既有电网将有能力接纳更大规模充电负荷。
现有技术中分布式储能调度一般考虑其调峰、调压、促进新能源消纳等作用，基于给定的充电负荷曲线确定分布式储能调度在减少充电负荷中的作用，对充电负荷的简化忽略了电动汽车充电需求在空间上的可转移性，其分布式储能调度策略将使得电力系统运行成本偏高，同时低估了含分布式储能电网对充电负荷的接纳能力，影响大规模充电负荷的接入，充电负荷的接纳能力较差。
发明内容
为了克服上述现有技术中电力系统运行成本偏高和充电负荷的接纳能力较差的不足，本发明提供一种分布式储能调度方法和装置，获取调度参数；求解预先构建的分布式储能调度模型，得到分布式储能调度策略；分布式储能调度模型包括火电机组运行费用和额外通勤时间成本，调度参数包括交通网参数、交通需求参数、电力设备参数和电网参数。本发明考虑交通潮流分配方式与分布式储能调度方式的耦合影响，通过交通潮流在空间上的转移减缓充电负荷对电力系统的冲击，降低了电力系统与交通网的总运行成本，提升了充电负荷的接纳能力。
为了实现上述发明目的，本发明采取如下技术方案：
一方面，本发明提供一种分布式储能调度方法，包括：
获取调度参数；
求解预先构建的分布式储能调度模型，得到分布式储能调度策略；
所述分布式储能调度模型包括火电机组运行费用和额外通勤时间成本；
所述调度参数包括交通网参数、交通需求参数、电力设备参数和电网参数。
所述交通网参数包括交通路段的通行容量；
所述交通需求参数包括交通负荷在各个时段的通行需求和通勤时间成本系数；
所述电力设备参数包括火电机组参数、风电机组参数、分布式储能参数以及电力负荷的有功功率；
所述电网参数包括总时段数、电力线路输送容量、电网向上调节备用容量和电网向下调节备用容量。
所述火电机组参数包括火电机组的数量、出力上限、出力下限、最大上爬坡速率、最大下爬坡速率和煤耗曲线的参数；
所述风电机组参数包括最大弃风率和预测发电功率；
所述分布式储能参数包括充电效率、放电效率、功率容量、能量容量和初始能量。
所述分布式储能调度模型的构建包括：
对预先构建的基于交通路段的通行容量的交通潮流分配模型进行求解，得到额外通勤时间成本；
对预先构建的电力系统调度模型进行求解，得到火电机组运行费用；
以所述火电机组运行费用和额外通勤时间成本之和最小为目标，以修正考虑充电负荷的电力系统功率平衡为约束，构建分布式储能调度模型。
所述交通潮流分配模型的构建，包括：
以实际通勤时间成本、最小通勤时间成本确定的通勤时间成本为目标构建所述交通潮流分配模型的第一目标函数；
设定约束条件；
其中所述约束条件包括交通潮流平衡约束、备选路径约束、交通路段潮流分配约束和交通路段通行容量约束。
所述电力系统调度模型的构建，包括：
构建所述电力系统调度模型的第二目标函数；
设定约束条件；
其中，所述约束条件包括火电机组约束、风电机组约束、分布式储能约束和电网约束。
所述火电机组约束如下式：
Figure PCTCN2018121392-appb-000001
Figure PCTCN2018121392-appb-000002
Figure PCTCN2018121392-appb-000003
Figure PCTCN2018121392-appb-000004
式中，
Figure PCTCN2018121392-appb-000005
为t时段第i台火电机组的运行费用；Ug it表示t时段火电机组i的开关状态，火电机组i开机时，Ug it取1，火电机组i关机时，Ug it取0；
Figure PCTCN2018121392-appb-000006
为火电机组i处于最小技术出力时的发电费用；cg im表示火电机组i在煤耗曲线第m分段时的单位功率煤耗成本；
Figure PCTCN2018121392-appb-000007
为t时段火电机组i在煤耗曲线第m分段的发电功率；P G,it为t时段火电机组i的有功功率，P G，it-1为t-1时段火电机组i的有功功率；
Figure PCTCN2018121392-appb-000008
为火电机组i的出力下限，
Figure PCTCN2018121392-appb-000009
为火电机组i在煤耗曲线第m分段的出力上限；
Figure PCTCN2018121392-appb-000010
为火电机组i的最大下爬坡速率，
Figure PCTCN2018121392-appb-000011
为火电机组i的最大上爬坡速率；
所述分布式储能约束如下式：
Figure PCTCN2018121392-appb-000012
Figure PCTCN2018121392-appb-000013
Figure PCTCN2018121392-appb-000014
Figure PCTCN2018121392-appb-000015
Figure PCTCN2018121392-appb-000016
E kT＝E k0
式中，
Figure PCTCN2018121392-appb-000017
为t时段分布式储能k的放电功率，
Figure PCTCN2018121392-appb-000018
为t时段分布式储能k的充电功率，
Figure PCTCN2018121392-appb-000019
为t时段分布式储能k的有功功率；
Figure PCTCN2018121392-appb-000020
为分布式储能k的功率容量；U kt为t时段分布式储能k的充放电状态，充电状态时，U kt取0，放电状态时，U kt取1；E kt为t时段分布式储能k的剩余能量，E k,t-1为t-1时段分布式储能k的剩余能量，E k0为分布式储能k的初始能量，E kT为T时段分布式储能k的剩余能量；
Figure PCTCN2018121392-appb-000021
为t时段分布式储能k的能量容量，η c为分布式储能 k的充电效率，η d为分布式储能k的放电效率；
所述电网约束如下式：
Figure PCTCN2018121392-appb-000022
Figure PCTCN2018121392-appb-000023
Figure PCTCN2018121392-appb-000024
P lt≤S l
式中，
Figure PCTCN2018121392-appb-000025
为t时段流入节点p的有功功率，
Figure PCTCN2018121392-appb-000026
为t时段流出节点p的有功功率，
Figure PCTCN2018121392-appb-000027
为t时段节点p处电力负荷的有功功率，
Figure PCTCN2018121392-appb-000028
为火电机组i的出力下限，
Figure PCTCN2018121392-appb-000029
为火电机组i的出力上限；x a,t为t时段分配至交通路段a的交通潮流；η为交通潮流与充电负荷的转换系数；
Figure PCTCN2018121392-appb-000030
为t时段电网向上调节备用容量，
Figure PCTCN2018121392-appb-000031
为t时段电网向下调节备用容量；S l为电力线路l的输送容量，P lt为t时段电力线路l的输送功率。
所述分布式储能调度模型的目标函数如下式：
Figure PCTCN2018121392-appb-000032
式中，F为火电机组运行费用和额外通勤时间成本之和；
Figure PCTCN2018121392-appb-000033
为火电机组运行费用，
Figure PCTCN2018121392-appb-000034
为实际通勤时间成本，
Figure PCTCN2018121392-appb-000035
为最小通勤时间成本；T为总时段数，N g为火电机组的数量，w为通勤时间成本系数，Ω a为交通路段集合，Ω d为交通负荷集合，
Figure PCTCN2018121392-appb-000036
为交通路段a的通勤时间，t d,1为交通负荷d对应的最短路径的通勤时间，x a,t为t时段分配至交通路段a的交通潮流，q d,t为t时段交通负荷d的通行需求。
所述求解预先构建的分布式储能调度模型，得到分布式储能调度策略，包括：
将所述交通网参数、交通需求参数、电力设备参数和电网参数作为输入项，基于仿真计算平台，并调用求解工具对分布式储能调度模型进行求解，得到分布式储能的有功功率。
另一方面，本发明提供一种分布式储能调度装置，包括：
获取模块，用于获取调度参数；
求解模块，用于求解预先构建的分布式储能调度模型，得到分布式储能调度策略；
所述分布式储能调度模型包括火电机组运行费用和额外通勤时间成本；
所述调度参数包括交通网参数、交通需求参数、电力设备参数和电网参数。
再一方面，本发明还提供一种计算机存储介质，所述计算机存储介质中存储有计算机可执行指令，所述计算机可执行指令用于执行分布式储能调度方法。
再一方面，本发明还提供一种电子设备，包括：至少一个处理器、至少一个存储器以及存储在所述存储器中的计算机程序指令，当所述计算机程序指令被所述处理器执行时实现所述的方法。
所述电子设备还包括：用于获取交通网参数、交通需求参数、电力设备参数和电网参数的至少一个通信接口。
所述电子设备为控制器、PC机或控制平台所在设备。
与最接近的现有技术相比，本发明提供的技术方案具有以下有益效果：
本发明提供的分布式储能调度方法中，获取调度参数；求解预先构建的分布式储能调度模型，得到分布式储能调度策略；分布式储能调度模型包括火电机组运行费用和额外通勤时间成本，调度参数包括交通网参数、交通需求参数、电力设备参数和电网参数，降低了电力系统与交通网的总运行成本，提升了充电负荷的接纳能力；
本发明提供的分布式储能调度装置包括获取模块和求解模块，获取模块，用于获取调度参数；求解模块，用于求解预先构建的分布式储能调度模型，得到分布式储能调度策略；分布式储能调度模型包括火电机组运行费用和额外通勤时间成本，调度参数包括交通网参数、交通需求参数、电力设备参数和电网参数，降低了电力系统与交通网的总运行成本，提升了充电负荷的接纳能力；
本发明针对交通网、电力系统所构成的耦合系统，通过引入交通潮流与充电负荷的转换系数，建立线性、易于快速求解的分布式储能调度模型，并通过调用求解器求解保证了分布式储能调度模型求解的精确度；
本发明考虑交通潮流分配方式与分布式储能调度方式的耦合影响，通过交通潮流在空间上的转移减缓充电负荷对电力系统的冲击，为充电负荷的大规模接入提供理论基础。
附图说明
图1是本发明实施例中分布式储能调度方法流程图。
具体实施方式
下面结合附图对本发明作进一步详细说明。
实施例1
本发明实施例1提供了一种分布式储能调度方法，具体流程图如图1所示，具体过程如下：
S101：获取调度参数；
S102：求解预先构建的分布式储能调度模型，得到分布式储能调度策略；
上述分布式储能调度模型包括火电机组运行费用和额外通勤时间成本；
调度参数包括交通网参数、交通需求参数、电力设备参数和电网参数。
交通网参数包括交通路段的通行容量；
交通需求参数包括交通负荷在各个时段的通行需求和通勤时间成本系数；
电力设备参数包括火电机组参数、风电机组参数、分布式储能参数以及电力负荷的有功功率；
电网参数包括总时段数、电力线路输送容量、电网向上调节备用容量和电网向下调节备用容量。
火电机组参数包括火电机组的数量、出力上限、出力下限、最大上爬坡速率、最大下爬坡速率和煤耗曲线的参数；
风电机组参数包括最大弃风率和预测发电功率；
分布式储能参数包括充电效率、放电效率、功率容量、能量容量和初始能量。
分布式储能调度模型的构建包括：
对预先构建的基于交通路段的通行容量的交通潮流分配模型进行求解，得到额外通勤时间成本；
对预先构建的电力系统调度模型进行求解，得到火电机组运行费用；
以所述火电机组运行费用和额外通勤时间成本之和最小为目标，以修正考虑充电负荷的电力系统功率平衡为约束，构建分布式储能调度模型。
交通潮流分配模型的构建，包括：
以实际通勤时间成本、最小通勤时间成本确定的通勤时间成本为目标构建所述交通潮流分配模型的第一目标函数；
设定约束条件；
其中所述约束条件包括交通潮流平衡约束、备选路径约束、交通路段潮流分配约束和交通路段通行容量约束。
所述第一目标函数如下式：
Figure PCTCN2018121392-appb-000037
式中，F 2为额外通勤时间成本，
Figure PCTCN2018121392-appb-000038
为实际通勤时间成本，
Figure PCTCN2018121392-appb-000039
为最小通勤时间成本；w为通勤时间成本系数，Ω a为交通路段集合，
Figure PCTCN2018121392-appb-000040
为交通路段a的通勤时间，Ω d为交通负荷集合，t d,1为交通负荷d对应的最短路径的通勤时间。所述交通潮流平衡约束如下式：
Figure PCTCN2018121392-appb-000041
式中，f d,c,t表示t时段交通负荷d下备选路径c上分配的交通潮流，q d,t为t时段交通负荷d的通行需求；
所述备选路径约束如下式：
f d,c,t≥0
所述交通路段潮流分配约束如下式：
Figure PCTCN2018121392-appb-000042
式中，x a,t为t时段分配至交通路段a的交通潮流，A d,c,a为交通负荷d下备选路径c与交通路段a的关联关系，备选路径c交通路段a关联，A d,c,a取1，备选路径c交通路段a不关联，A d,c,a取0。
电力系统调度模型的构建，包括：
构建电力系统调度模型的第二目标函数；
设定约束条件；
其中，约束条件包括火电机组约束、风电机组约束、分布式储能约束和电网约束。
所述第二目标函数如下式：
Figure PCTCN2018121392-appb-000043
式中，F 1为火电机组运行费用，T为总时段数，N g为火电机组的数量，
Figure PCTCN2018121392-appb-000044
为t时段第i台火电机组的运行费用。
所述火电机组约束如下式：
Figure PCTCN2018121392-appb-000045
Figure PCTCN2018121392-appb-000046
Figure PCTCN2018121392-appb-000047
Figure PCTCN2018121392-appb-000048
式中，Ug it表示t时段火电机组i的开关状态，火电机组i开机时，Ug it取1，火电机组i关机时，Ug it取0；
Figure PCTCN2018121392-appb-000049
为火电机组i处于最小技术出力时的发电费用；cg im表示火电机组i在煤耗曲线第m分段时的单位功率煤耗成本；
Figure PCTCN2018121392-appb-000050
为t时段火电机组i在煤耗曲线第m分段的发电功率；P G,it为t时段火电机组i的有功功率，P G，it-1为t-1时段火电机组i的有功功率；
Figure PCTCN2018121392-appb-000051
为火电机组i的出力下限，
Figure PCTCN2018121392-appb-000052
为火电机组i在煤耗曲线第m分段的出力上限；
Figure PCTCN2018121392-appb-000053
为火电机组i的最大下爬坡速率，
Figure PCTCN2018121392-appb-000054
为火电机组i的最大上爬坡速率；
所述分布式储能约束如下式：
Figure PCTCN2018121392-appb-000055
Figure PCTCN2018121392-appb-000056
Figure PCTCN2018121392-appb-000057
Figure PCTCN2018121392-appb-000058
Figure PCTCN2018121392-appb-000059
E kT＝E k0
式中，
Figure PCTCN2018121392-appb-000060
为t时段分布式储能k的放电功率，
Figure PCTCN2018121392-appb-000061
为t时段分布式储能k的充电功率，
Figure PCTCN2018121392-appb-000062
为t时段分布式储能k的有功功率；
Figure PCTCN2018121392-appb-000063
为分布式储能k的功率容量；U kt为t时段分布式储能k的充放电状态，充电状态时，U kt取0，放电状态时，U kt取1；E kt为t时段分布式储能k的剩余能量，E k,t-1为t-1时段分布式储能k的剩余能量，E k0为分布式储能k的初始能量，E kT为T时段分布式储能k的剩余能量；
Figure PCTCN2018121392-appb-000064
为t时段分布式储能k的能量容量，η c为分布式储能k的充电效率，η d为分布式储能k的放电效率；
电网约束主要包括功率系统功率平衡约束、旋转备用约束以及线路传输容量限值，旋转备用是将所有运行机组的最大出力之和减去当前系统的负荷和损耗。备用是为了保证系统可靠供电的一项重要措施，防止当出现一台机组或几台机组故障时，系统出现严重的负荷缺额从而导致系统频率急剧下降而发生故障。在含风电电力系统中因为风电出力波动和预测误差，需要配置额外的旋转备用来应对风电波动。因此，为了保证系统的安全运行，在安排机组启停计划时，应考虑安排足够机组运行，以满足系统备用的需要。风电出力变化、储能系统充放电可能导致网络潮流发生比较大的变化，所以在决策机组出力和广域储能系统充放电计划的时候需要考虑系统网架，另外由于新能源发展过程中因为国家政策扶持，风电场的并网速度往往快于常规电源以及配套传输网架的建设速度，使得电力系统往往因为常规电源调节支援能力有限或者输电线路输送能力不足而限制电网对风电的接纳能力，所以必须考虑到系统网架拓扑和传输热稳定上限。
具体的电网约束如下式：
Figure PCTCN2018121392-appb-000065
Figure PCTCN2018121392-appb-000066
Figure PCTCN2018121392-appb-000067
P lt≤S l
式中，
Figure PCTCN2018121392-appb-000068
为t时段流入节点p的有功功率，
Figure PCTCN2018121392-appb-000069
为t时段流出节点p的有功功率，
Figure PCTCN2018121392-appb-000070
为t时段节点p处电力负荷的有功功率，
Figure PCTCN2018121392-appb-000071
为火电机组i的出力下限，
Figure PCTCN2018121392-appb-000072
为火电机组i的出力上限；x a,t为t时段分配至交通路段a的交通潮流；η为交通潮流与充电负荷的转换系数；
Figure PCTCN2018121392-appb-000073
为t时段电网向上调节备用容量，
Figure PCTCN2018121392-appb-000074
为t时段电网向下调节备用容量；S l为电力线路l的输送容量，P lt为t时段电力线路l的输送功率。
上述分布式储能调度模型包括：
(1)目标函数如下式：
Figure PCTCN2018121392-appb-000075
式中，F为火电机组运行费用和额外通勤时间成本之和；
(2)约束条件：
在城市交通调度模型的建模中，每个O-D对的交通需求需要被分配实际的路网中。在交通潮流分配中，需要满足Wardrop第一性原则或者Wardrop第二性原则。Wardrop第一性原则又被称为用户均衡原则，在该原则下，考虑交通拥塞后，实际分配潮流的备选路径通勤时间将相等。即任意乘客不能通过改变自己的路径选择找到更快的路径。Wardrop第二性原则又被称为最优原则，即假定一个调度中心，在调度中心的潮流分配下系统的通勤时间将达到最小。在本文中，为便于交通模型与电气模型的耦合后的求解，采用的是基于Wardrop第二性原则的交通潮流分配模型。于是，约束条件包括交通网约束和电力系统约束；
交通网约束包括交通潮流平衡约束、备选路径约束、交通路段潮流分配约束和交通路段通行容量约束；
电力系统约束包括火电机组约束、风电机组约束、分布式储能约束和电网约束。
Figure PCTCN2018121392-appb-000076
f d,c,t≥0
Figure PCTCN2018121392-appb-000077
Figure PCTCN2018121392-appb-000078
Figure PCTCN2018121392-appb-000079
Figure PCTCN2018121392-appb-000080
Figure PCTCN2018121392-appb-000081
Figure PCTCN2018121392-appb-000082
Figure PCTCN2018121392-appb-000083
Figure PCTCN2018121392-appb-000084
Figure PCTCN2018121392-appb-000085
Figure PCTCN2018121392-appb-000086
E kT＝E k0
Figure PCTCN2018121392-appb-000087
Figure PCTCN2018121392-appb-000088
Figure PCTCN2018121392-appb-000089
P lt≤S l。
上述S102中，求解预先构建的分布式储能调度模型，得到分布式储能调度策略，具体是将交通网参数、交通需求参数、电力设备参数和电网参数作为输入项，基于仿真计算平台，并调用求解工具对分布式储能调度模型进行求解，得到分布式储能的有功功率(即
Figure PCTCN2018121392-appb-000090
)。
实施例2
基于同一发明构思，本发明实施例2还提供一种分布式储能调度装置，包括获取模块和求解模块，下面对上述几个模块的功能进行详细说明：
获取模块，用于获取调度参数；
求解模块，用于求解预先构建的分布式储能调度模型，得到分布式储能调度策略；
上述分布式储能调度模型包括火电机组运行费用和额外通勤时间成本；
调度参数包括交通网参数、交通需求参数、电力设备参数和电网参数；
交通网参数包括交通路段的通行容量；
交通需求参数包括交通负荷在各个时段的通行需求和通勤时间成本系数；
电力设备参数包括火电机组参数、风电机组参数、分布式储能参数以及电力负荷的有功功率；
电网参数包括总时段数、电力线路输送容量、电网向上调节备用容量和电网向下调节备用容量。
火电机组参数包括火电机组的数量、出力上限、出力下限、最大上爬坡速率、最大下爬坡速率和煤耗曲线的参数；
风电机组参数包括最大弃风率和预测发电功率；
分布式储能参数包括充电效率、放电效率、功率容量、能量容量和初始能量。
本发明实施例2提供的装置还包括建模模块，建模模块包括：
第一求解单元，用于对预先构建的基于交通路段的通行容量的交通潮流分配模型进行求解，得到额外通勤时间成本；
第二求解单元，用于对预先构建的电力系统调度模型进行求解，得到火电机组运行费用；
建模单元，用于以所述火电机组运行费用和额外通勤时间成本之和最小为目标，以修正考虑充电负荷的电力系统功率平衡为约束，构建分布式储能调度模型。
第一求解单元具体用于：
以实际通勤时间成本、最小通勤时间成本确定的通勤时间成本为目标构建所述交通潮流分配模型的第一目标函数；
设定约束条件；
其中所述约束条件包括交通潮流平衡约束、备选路径约束、交通路段潮流分配约束和交通路段通行容量约束。
第一目标函数如下式：
Figure PCTCN2018121392-appb-000091
式中，F 2为额外通勤时间成本，
Figure PCTCN2018121392-appb-000092
为实际通勤时间成本，
Figure PCTCN2018121392-appb-000093
为最小通勤时间成本；w为通勤时间成本系数，Ω a为交通路段集合，
Figure PCTCN2018121392-appb-000094
为交通路段a的通勤时间，Ω d为交通负荷集合，t d,1为交通负荷d对应的最短路径的通勤时间。交通潮流平衡约束如下式：
Figure PCTCN2018121392-appb-000095
式中，f d,c,t表示t时段交通负荷d下备选路径c上分配的交通潮流，q d,t为t时段交通负荷d的通行需求；
所述备选路径约束如下式：
f d,c,t≥0
所述交通路段潮流分配约束如下式：
Figure PCTCN2018121392-appb-000096
式中，x a,t为t时段分配至交通路段a的交通潮流，A d,c,a为交通负荷d下备选路径c与交通路段a的关联关系，备选路径c交通路段a关联，A d,c,a取1，备选路径c交通路段a不关联，A d,c,a取0。
第二求解单元具体用于：
构建所述电力系统调度模型的第二目标函数；
设定约束条件；
其中，所述约束条件包括火电机组约束、风电机组约束、分布式储能约束和电网约束。
第二目标函数如下式：
Figure PCTCN2018121392-appb-000097
式中，F 1为火电机组运行费用，T为总时段数，N g为火电机组的数量，
Figure PCTCN2018121392-appb-000098
为t时段第i台火电机组的运行费用。火电机组约束如下式：
Figure PCTCN2018121392-appb-000099
Figure PCTCN2018121392-appb-000100
Figure PCTCN2018121392-appb-000101
Figure PCTCN2018121392-appb-000102
式中，Ug it表示t时段火电机组i的开关状态，火电机组i开机时，Ug it取1，火电机组i关机时，Ug it取0；
Figure PCTCN2018121392-appb-000103
为火电机组i处于最小技术出力时的发电费用；cg im表示火电机组i在煤耗曲线第m分段时的单位功率煤耗成本；
Figure PCTCN2018121392-appb-000104
为t时段火电机组i在煤耗曲线第m分段的发电功率；P G,it为t时段火电机组i的有功功率，P G，it-1为t-1时段火电机组i的有功功率；
Figure PCTCN2018121392-appb-000105
为火电机组i的出力下限，
Figure PCTCN2018121392-appb-000106
为火电机组i在煤耗曲线第m分段的出力上限；
Figure PCTCN2018121392-appb-000107
为火电机组i的最大下爬坡速率，
Figure PCTCN2018121392-appb-000108
为火电机组i的最大上爬坡速率；
所述分布式储能约束如下式：
Figure PCTCN2018121392-appb-000109
Figure PCTCN2018121392-appb-000110
Figure PCTCN2018121392-appb-000111
Figure PCTCN2018121392-appb-000112
Figure PCTCN2018121392-appb-000113
E kT＝E k0
式中，
Figure PCTCN2018121392-appb-000114
为t时段分布式储能k的放电功率，
Figure PCTCN2018121392-appb-000115
为t时段分布式储能k的充电功率，
Figure PCTCN2018121392-appb-000116
为t时段分布式储能k的有功功率；
Figure PCTCN2018121392-appb-000117
为分布式储能k的功率容量；U kt为t时段分布式储 能k的充放电状态，充电状态时，U kt取0，放电状态时，U kt取1；E kt为t时段分布式储能k的剩余能量，E k,t-1为t-1时段分布式储能k的剩余能量，E k0为分布式储能k的初始能量，E kT为T时段分布式储能k的剩余能量；
Figure PCTCN2018121392-appb-000118
为t时段分布式储能k的能量容量，η c为分布式储能k的充电效率，η d为分布式储能k的放电效率；
所述电网约束如下式：
Figure PCTCN2018121392-appb-000119
Figure PCTCN2018121392-appb-000120
Figure PCTCN2018121392-appb-000121
P lt≤S l
式中，
Figure PCTCN2018121392-appb-000122
为t时段流入节点p的有功功率，
Figure PCTCN2018121392-appb-000123
为t时段流出节点p的有功功率，
Figure PCTCN2018121392-appb-000124
为t时段节点p处电力负荷的有功功率，
Figure PCTCN2018121392-appb-000125
为火电机组i的出力下限，
Figure PCTCN2018121392-appb-000126
为火电机组i的出力上限；x a,t为t时段分配至交通路段a的交通潮流；η为交通潮流与充电负荷的转换系数；
Figure PCTCN2018121392-appb-000127
为t时段电网向上调节备用容量，
Figure PCTCN2018121392-appb-000128
为t时段电网向下调节备用容量；S l为电力线路l的输送容量，P lt为t时段电力线路l的输送功率。
分布式储能调度模型包括：
如下式的目标函数：
Figure PCTCN2018121392-appb-000129
式中，F为火电机组运行费用和额外通勤时间成本之和；
如下式的约束条件：
Figure PCTCN2018121392-appb-000130
f d,c,t≥0
Figure PCTCN2018121392-appb-000131
Figure PCTCN2018121392-appb-000132
Figure PCTCN2018121392-appb-000133
Figure PCTCN2018121392-appb-000134
Figure PCTCN2018121392-appb-000135
Figure PCTCN2018121392-appb-000136
Figure PCTCN2018121392-appb-000137
Figure PCTCN2018121392-appb-000138
Figure PCTCN2018121392-appb-000139
Figure PCTCN2018121392-appb-000140
E kT＝E k0
Figure PCTCN2018121392-appb-000141
Figure PCTCN2018121392-appb-000142
Figure PCTCN2018121392-appb-000143
P lt≤S l。
上述求解模块将交通网参数、交通需求参数、电力设备参数和电网参数作为输入项，基于仿真计算平台，并调用求解工具对分布式储能调度模型进行求解，得到分布式储能的有功功率。
实施例3
本发明实施例3提供一种计算机存储介质，计算机存储介质中存储有计算机可执行指令，计算机可执行指令用于执行上述实施例1的分布式储能调度方法。
实施例4
本发明实施例4提供一种电子设备，该电子设备包括：至少一个处理器、至少一个存储器以及存储在上述存储器中的计算机程序指令，当上述计算机程序指令被所述处理器执行时实现实施例1提供的方法。
上述电子设备还包括：用于获取交通网参数、交通需求参数、电力设备参数和电网参数的至少一个通信接口。
上述电子设备为控制器、PC机或控制平台所在设备。为了描述的方便，以上所述装置的 各部分以功能分为各种模块或单元分别描述。当然，在实施本申请时可以把各模块或单元的功能在同一个或多个软件或硬件中实现。
本领域内的技术人员应明白，本申请的实施例可提供为方法、系统、或计算机程序产品。因此，本申请可采用完全硬件实施例、完全软件实施例、或结合软件和硬件方面的实施例的形式。而且，本申请可采用在一个或多个其中包含有计算机可用程序代码的计算机可用存储介质(包括但不限于磁盘存储器、CD-ROM、光学存储器等)上实施的计算机程序产品的形式。
本申请是参照根据本申请实施例的方法、设备(系统)、和计算机程序产品的流程图和/或方框图来描述的。应理解可由计算机程序指令实现流程图和/或方框图中的每一流程和/或方框、以及流程图和/或方框图中的流程和/或方框的结合。可提供这些计算机程序指令到通用计算机、专用计算机、嵌入式处理机或其他可编程数据处理设备的处理器以产生一个机器，使得通过计算机或其他可编程数据处理设备的处理器执行的指令产生用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的装置。
这些计算机程序指令也可存储在能引导计算机或其他可编程数据处理设备以特定方式工作的计算机可读存储器中，使得存储在该计算机可读存储器中的指令产生包括指令装置的制造品，该指令装置实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能。
这些计算机程序指令也可装载到计算机或其他可编程数据处理设备上，使得在计算机或其他可编程设备上执行一系列操作步骤以产生计算机实现的处理，从而在计算机或其他可编程设备上执行的指令提供用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的步骤。
最后应当说明的是：以上实施例仅用以说明本发明的技术方案而非对其限制，所属领域的普通技术人员参照上述实施例依然可以对本发明的具体实施方式进行修改或者等同替换，这些未脱离本发明精神和范围的任何修改或者等同替换，均在申请待批的本发明的权利要求保护范围之内。

Claims (14)

  1. 一种分布式储能调度方法，其特征在于，包括：
    获取调度参数；
    求解预先构建的分布式储能调度模型，得到分布式储能调度策略；
    所述分布式储能调度模型包括火电机组运行费用和额外通勤时间成本；
    所述调度参数包括交通网参数、交通需求参数、电力设备参数和电网参数。
  2. 根据权利要求1所述的分布式储能调度方法，其特征在于，所述交通网参数包括交通路段的通行容量；
    所述交通需求参数包括交通负荷在各个时段的通行需求和通勤时间成本系数；
    所述电力设备参数包括火电机组参数、风电机组参数、分布式储能参数以及电力负荷的有功功率；
    所述电网参数包括总时段数、电力线路输送容量、电网向上调节备用容量和电网向下调节备用容量。
  3. 根据权利要求2所述的分布式储能调度方法，其特征在于，所述火电机组参数包括火电机组的数量、出力上限、出力下限、最大上爬坡速率、最大下爬坡速率和煤耗曲线的参数；
    所述风电机组参数包括最大弃风率和预测发电功率；
    所述分布式储能参数包括充电效率、放电效率、功率容量、能量容量和初始能量。
  4. 根据权利要求1所述的分布式储能调度方法，其特征在于，所述分布式储能调度模型的构建包括：
    对预先构建的基于交通路段的通行容量的交通潮流分配模型进行求解，得到额外通勤时间成本；
    对预先构建的电力系统调度模型进行求解，得到火电机组运行费用；
    以所述火电机组运行费用和额外通勤时间成本之和最小为目标，以修正考虑充电负荷的电力系统功率平衡为约束，构建分布式储能调度模型。
  5. 根据权利要求4所述的分布式储能调度方法，其特征在于，所述交通潮流分配模型的构建，包括：
    以实际通勤时间成本、最小通勤时间成本确定的通勤时间成本为目标构建所述交通潮流分配模型的第一目标函数；
    设定约束条件；
    其中，所述约束条件包括交通潮流平衡约束、备选路径约束、交通路段潮流分配约束和交通路段通行容量约束。
  6. 根据权利要求5所述的分布式储能调度方法，其特征在于，所述电力系统调度模型的构建，包括：
    构建所述电力系统调度模型的第二目标函数；
    设定约束条件；
    其中，所述约束条件包括火电机组约束、风电机组约束、分布式储能约束和电网约束。
  7. 根据权利要求6所述的分布式储能调度方法，其特征在于，所述火电机组约束如下式：
    Figure PCTCN2018121392-appb-100001
    Figure PCTCN2018121392-appb-100002
    Figure PCTCN2018121392-appb-100003
    Figure PCTCN2018121392-appb-100004
    式中，
    Figure PCTCN2018121392-appb-100005
    为t时段第i台火电机组的运行费用；Ug it表示t时段火电机组i的开关状态，火电机组i开机时，Ug it取1，火电机组i关机时，Ug it取0；F i G,min为火电机组i处于最小技术出力时的发电费用；cg im表示火电机组i在煤耗曲线第m分段时的单位功率煤耗成本；
    Figure PCTCN2018121392-appb-100006
    为t时段火电机组i在煤耗曲线第m分段的发电功率；P G,it为t时段火电机组i的有功功率，P G，it-1为t-1时段火电机组i的有功功率；P i G,min为火电机组i的出力下限，
    Figure PCTCN2018121392-appb-100007
    为火电机组i在煤耗曲线第m分段的出力上限；
    Figure PCTCN2018121392-appb-100008
    为火电机组i的最大下爬坡速率，
    Figure PCTCN2018121392-appb-100009
    为火电机组i的最大上爬坡速率；
    所述分布式储能约束如下式：
    Figure PCTCN2018121392-appb-100010
    Figure PCTCN2018121392-appb-100011
    Figure PCTCN2018121392-appb-100012
    Figure PCTCN2018121392-appb-100013
    Figure PCTCN2018121392-appb-100014
    E kT＝E k0
    式中，
    Figure PCTCN2018121392-appb-100015
    为t时段分布式储能k的放电功率，
    Figure PCTCN2018121392-appb-100016
    为t时段分布式储能k的充电功率，
    Figure PCTCN2018121392-appb-100017
    为t时段分布式储能k的有功功率；
    Figure PCTCN2018121392-appb-100018
    为分布式储能k的功率容量；U kt为t时段分布式储能k的充放电状态，充电状态时，U kt取0，放电状态时，U kt取1；E kt为t时段分布式储能k的剩余能量，E k,t-1为t-1时段分布式储能k的剩余能量，E k0为分布式储能k的初始能量，E kT为T时段分布式储能k的剩余能量；
    Figure PCTCN2018121392-appb-100019
    为t时段分布式储能k的能量容量，η c为分布式储能k的充电效率，η d为分布式储能k的放电效率；
    所述电网约束如下式：
    Figure PCTCN2018121392-appb-100020
    Figure PCTCN2018121392-appb-100021
    Figure PCTCN2018121392-appb-100022
    P lt≤S l
    式中，
    Figure PCTCN2018121392-appb-100023
    为t时段流入节点p的有功功率，
    Figure PCTCN2018121392-appb-100024
    为t时段流出节点p的有功功率，
    Figure PCTCN2018121392-appb-100025
    为t时段节点p处电力负荷的有功功率，P i G,min为火电机组i的出力下限，P i G,max为火电机组i的出力上限；x a,t为t时段分配至交通路段a的交通潮流；η为交通潮流与充电负荷的转换系数；
    Figure PCTCN2018121392-appb-100026
    为t时段电网向上调节备用容量，
    Figure PCTCN2018121392-appb-100027
    为t时段电网向下调节备用容量；S l为电力线路l的输送容量，P lt为t时段电力线路l的输送功率。
  8. 根据权利要求7所述的分布式储能调度方法，其特征在于，所述分布式储能调度模型的目标函数如下式：
    Figure PCTCN2018121392-appb-100028
    式中，F为火电机组运行费用和额外通勤时间成本之和；
    Figure PCTCN2018121392-appb-100029
    为火电机组运行费 用，
    Figure PCTCN2018121392-appb-100030
    为实际通勤时间成本，
    Figure PCTCN2018121392-appb-100031
    为最小通勤时间成本；T为总时段数，N g为火电机组的数量，w为通勤时间成本系数，Ω a为交通路段集合，Ω d为交通负荷集合，
    Figure PCTCN2018121392-appb-100032
    为交通路段a的通勤时间，t d,1为交通负荷d对应的最短路径的通勤时间，x a,t为t时段分配至交通路段a的交通潮流，q d,t为t时段交通负荷d的通行需求。
  9. 根据权利要求1所述的分布式储能调度方法，其特征在于，所述求解预先构建的分布式储能调度模型，得到分布式储能调度策略，包括：
    将所述交通网参数、交通需求参数、电力设备参数和电网参数作为输入项，基于仿真计算平台，并调用求解工具对分布式储能调度模型进行求解，得到分布式储能的有功功率。
  10. 一种分布式储能调度装置，其特征在于，包括：
    获取模块，用于获取调度参数；
    求解模块，用于求解预先构建的分布式储能调度模型，得到分布式储能调度策略；
    所述分布式储能调度模型包括火电机组运行费用和额外通勤时间成本；
    所述调度参数包括交通网参数、交通需求参数、电力设备参数和电网参数。
  11. 一种计算机存储介质，其特征在于，所述计算机存储介质中存储有计算机可执行指令，所述计算机可执行指令用于执行权利要求1至9任一项所述的一种分布式储能调度方法。
  12. 一种电子设备，其特征在于，包括：至少一个处理器、至少一个存储器以及存储在所述存储器中的计算机程序指令，当所述计算机程序指令被所述处理器执行时实现如权利要求1-9任一项所述的方法。
  13. 根据权利要求12所述电子设备，其特征在于，所述电子设备还包括：用于获取交通网参数、交通需求参数、电力设备参数和电网参数的至少一个通信接口。
  14. 根据权利要求12或13所述的电子设备，其特征在于，所述电子设备为控制器、PC机或控制平台所在设备。
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