CN103107558A

CN103107558A - Multi-modal customizable green energy concentrator and method thereof

Info

Publication number: CN103107558A
Application number: CN2013100384581A
Authority: CN
Inventors: 喻洁; 时斌; 吴在军; 窦晓波; 冯其之
Original assignee: Southeast University
Current assignee: NANJING YANXU ELECTRICAL TECHNOLOGY CO LTD
Priority date: 2013-01-31
Filing date: 2013-01-31
Publication date: 2013-05-15
Anticipated expiration: 2033-01-31
Also published as: CN103107558B

Abstract

The invention discloses a multi-mode customizable green energy hub and a method thereof, comprising a data acquisition module, an interval type fuzzy prediction module, an energy management optimization module and an output control module. The hub collects information about distributed power sources and micro-grid loads, establishes an interval-type fuzzy prediction module to predict wind power, photovoltaic power generation and load values, and obtains an interval-type fuzzy prediction set; according to grid-connected operation or island operation mode, customized and large power grid The exchange power of the microgrid is established with the minimum operating cost of the microgrid as the objective function, and the energy optimization management algorithm of the microgrid is established. The invention proposes a microgrid energy management algorithm with fuzzy prediction, and obtains a power generation plan with controllable micropower reserve capacity, so that the microgrid system can operate reasonably and effectively.

Description

Multimodal customizable green energy hub and method

技术领域 technical field

本发明涉及分布式发电、微电网技术领域，特别是涉及一种多模态可定制的绿色能量集线器及其方法。 The invention relates to the technical fields of distributed power generation and micro-grid, in particular to a multi-mode customizable green energy hub and a method thereof. the

背景技术 Background technique

微电网能够促进分布式电源的有效利用，在解决能源枯竭、环保问题等表现出的巨大优势，已经受到世界各国的普遍关注。微电网中的分布式电源主要包括风力发电机、太阳能光伏电池、燃料电池、微型燃气轮机等，此外还有各类储能元件。然而，不同类型的微电源具有不同的运行特性，为了使微电网系统合理有效地运行，需要对其进行有效的能量管理优化。 The microgrid can promote the effective utilization of distributed power, and has shown great advantages in solving energy exhaustion and environmental protection issues, and has attracted widespread attention from all over the world. The distributed power sources in the microgrid mainly include wind turbines, solar photovoltaic cells, fuel cells, micro gas turbines, etc., in addition to various energy storage components. However, different types of micro-power sources have different operating characteristics. In order to make the micro-grid system operate reasonably and effectively, effective energy management optimization is required. the

风力和光伏发电依赖于风、光等自然资源，具有很强的随机性，微电网能量管理具有更大的难度与挑战。微电网并网运行时，与大电网之间能量交互双向性，与储能单元之间也存在能量双向流动，因此需要采用一定的能量管理算法控制微电网的能量流，保障系统稳定性和经济性；微电网孤岛运行时，为保证关键负荷的可靠供电，也需要采取一定的能量管理算法管理储能单元的充放电，保证微电网的能量平衡。 Wind power and photovoltaic power generation depend on natural resources such as wind and light, and have strong randomness. Microgrid energy management is more difficult and challenging. When the microgrid is connected to the grid, the energy interaction with the large grid is bidirectional, and there is also a bidirectional flow of energy with the energy storage unit. Therefore, it is necessary to use a certain energy management algorithm to control the energy flow of the microgrid to ensure system stability and economy. When the microgrid is running in an island, in order to ensure the reliable power supply of key loads, it is also necessary to adopt certain energy management algorithms to manage the charging and discharging of energy storage units to ensure the energy balance of the microgrid. the

发明内容 Contents of the invention

发明目的：本发明提供一种多模态可定制的绿色能量集线器及其方法，当微电网在并网运行或孤岛运行模态时，有效管理微电网能量管理。 Purpose of the invention: The present invention provides a multi-mode customizable green energy hub and its method, which can effectively manage the energy management of the micro-grid when the micro-grid is in grid-connected operation or island operation mode. the

技术方案：多模态可定制能量绿色集线器，包括数据采集模块、区间型预测模块、能量管理优化模块和输出控制模块，其中，区间型预测模块包括风/光发电量预测模块和负荷预测模块，预测模块包括模糊器、规则库、推理机、降型器和解模糊器；能量管理优化模块，通过能量管理优化模型进行能量优化分配。数据采集模块将采集的实时环境信息和历史数据输入区间型预测模块，预测模块输出预测数据，并将预测数据输入能量管理优化模块，能量管理优化模块计算储能单元和可控微电源的出力，由输出控制模块输出控制指令。 Technical solution: multi-mode customizable energy green hub, including data acquisition module, interval forecasting module, energy management optimization module and output control module, among which interval forecasting module includes wind/solar power generation forecasting module and load forecasting module, The prediction module includes a fuzzer, a rule base, an inference engine, a downscaler, and a defuzzifier; an energy management optimization module performs energy optimization allocation through an energy management optimization model. The data acquisition module inputs the collected real-time environmental information and historical data into the interval prediction module, the prediction module outputs the prediction data, and inputs the prediction data into the energy management optimization module, and the energy management optimization module calculates the output of the energy storage unit and the controllable micro power supply, The control command is output by the output control module. the

能量管理优化模型为： The energy management optimization model is:

1）目标函数，以发电成本最小作为目标函数，使微电网自身利润最大化，表达式为： $\min F (x) = \min Σ_{t = 1}^{T} [C_{F} + C_{OM} + C_{BUY} + C_{SEL}]$ 1) The objective function, taking the minimum power generation cost as the objective function to maximize the profit of the microgrid itself, the expression is: $\min f (x) = \min Σ_{t = 1}^{T} [C_{f} + C_{om} + C_{BUY} + C_{SEL}]$

其中，T为调度周期的时间段，C_F为燃料成本，C_OM为运行维护费用，C_BUY是从大电网购入的费用，C_SEL是出售给大电网所获得的利润， Among them, T is the time period of the scheduling cycle, C _F is the fuel cost, C _OM is the operation and maintenance cost, C _BUY is the cost of purchasing from the large power grid, and C _SEL is the profit obtained from selling to the large power grid,

a)燃料成本为C_F， $C_{F} = Σ_{i}^{n} f (P_{i}^{t}) C_{i},$ a) The cost of fuel is C _F , $C_{f} = Σ_{i}^{no} f (P_{i}^{t}) C_{i},$

其中i表示发电单元种类，n为t时刻发电单元数目，

是发电单元i在t时刻的发电量，

是t时刻发电单元i所用燃料量，C_i是发电单元i所用燃料的价格。 Where i represents the type of power generation unit, n is the number of power generation units at time t,

is the power generation of power generation unit i at time t,

is the amount of fuel used by power generation unit i at time t, and C _i is the price of fuel used by power generation unit i.

b)运行维护费用C_OM， b) Operation and maintenance costs C _OM ,

${C C}_{OM om} = = {P P}_{Mt Mt.}^{t t} \cdot \cdot {C C}_{MT MT__om om} + + {\overset{~ ~}{P P}}_{PV PV}^{t t} \cdot &Center Dot; {C C}_{PV PV__om om} + + {\overset{~ ~}{P P}}_{WT WT}^{t t} \cdot \cdot {C C}_{WT WT__om om} + + | | {P P}_{BT BT}^{t t} | | \cdot &Center Dot; {C C}_{BT BT__om om} + + {P P}_{FC FC}^{t t} \cdot \cdot {C C}_{FC FC__om om}$

其中C_{MT_om}、C_{PV_om}、C_{WT_om}、C_{BT_om}、C_{FC_om}分别为微型燃气轮机、光伏电池、风力发电机、蓄电池及燃料电池的单位运行维护费用；

为微型燃气轮机和燃料电池t时刻的发电量；为蓄电池t时刻的功率，

表示放电功率，

表示充电功率；

为光伏电池和风力发电机t时刻的发电量，通过区间型模糊预测分别得到其区间模糊集合，

其中

分别为光伏发电量预测区间的上下界，

分别为风力发电量预测区间的上下界； Among them, C _{MT_om} , C _{PV_om} , C _{WT_om} , C _{BT_om} , and C _{FC_om} are the unit operation and maintenance costs of micro gas turbines, photovoltaic cells, wind power generators, storage batteries and fuel cells, respectively;

is the power generation of micro gas turbine and fuel cell at time t; is the power of the battery at time t,

Indicates the discharge power,

Indicates the charging power;

is the power generation of photovoltaic cells and wind generators at time t, and their interval fuzzy sets are respectively obtained through interval fuzzy prediction,

in

are the upper and lower bounds of the prediction interval of photovoltaic power generation,

are the upper and lower bounds of the forecast interval of wind power generation;

c)C_BUY是从大电网购入的费用，C_SEL是出售给大电网所获得的利润，具体为 c) C _BUY is the cost of buying from the large power grid, and C _SEL is the profit obtained from selling to the large power grid, specifically

${C C}_{BUY BUY} = = {C C}_{buy buy}^{t t} \cdot \cdot {P P}_{ex ex}^{t t},, (({P P}_{ex ex}^{t t} > > 00))$

${C C}_{SEL SEL} = = {C C}_{sel sel}^{t t} \cdot &Center Dot; {P P}_{ex ex}^{t t},, (({P P}_{ex ex}^{t t} < < 00))$

其中

是t时段从大电网购入电能的价格，

是t时段出售电能给大电网的价格,

表示t时段微电网与大电网间的功率交换，

表示向大电网售电，

表示从大电网买电； in

is the price of electricity purchased from the large power grid during the period t,

is the price of selling electric energy to the large power grid in period t,

Indicates the power exchange between the microgrid and the large grid during the t period,

Indicates the sale of electricity to the large grid,

Indicates buying electricity from the large power grid;

2）约束条件，包括功率平衡约束、微电网与主网最大交互容量约束、微型燃气轮机和燃料电池的输出功率上、下限约束以及蓄电池充放电功率上、下限约束； 2) Constraints, including power balance constraints, maximum interaction capacity constraints between the microgrid and the main grid, upper and lower limit constraints on the output power of micro gas turbines and fuel cells, and upper and lower limit constraints on battery charging and discharging power;

a)功率平衡约束： $P_{MT}^{t} + P_{FC}^{t} + P_{BT_disch}^{t} \cdot η_{BT}^{disch} + P_{ex}^{t} = {\tilde{P}}_{d}^{t} - {\tilde{P}}_{PV}^{t} - {\tilde{P}}_{WT}^{t}$ a) Power balance constraints: $P_{MT}^{t} + P_{FC}^{t} + P_{BT_disc}^{t} &Center Dot; η_{BT}^{disc} + P_{ex}^{t} = {\tilde{P}}_{d}^{t} - {\tilde{P}}_{PV}^{t} - {\tilde{P}}_{WT}^{t}$

${P P}_{MT MT}^{t t} + + {P P}_{FC FC}^{t t} + + {P P}_{BT BT__ch ch}^{t t} \cdot \cdot {η η}_{BT BT}^{ch ch} + + {P P}_{ex ex}^{t t} = = {\overset{~ ~}{P P}}_{d d}^{t t} - - {\overset{~ ~}{P P}}_{PV PV}^{t t} - - {\overset{~ ~}{P P}}_{WT WT}^{t t}$

其中

均如上述所述，

分别表示t时刻蓄电池的充、放电功率；

为蓄电池的充、放电效率；

为微电网负荷预测区间值

分别为负荷预测区间的上下限； in

are as described above,

Respectively represent the charge and discharge power of the battery at time t;

is the charging and discharging efficiency of the battery;

Interval value for microgrid load prediction

are the upper and lower limits of the load forecast interval;

b)微电网与主网最大交互容量约束，即联络线的物理传输容量限值： b) The maximum interactive capacity constraint between the microgrid and the main grid, that is, the physical transmission capacity limit of the tie line:

${P P}_{ex ex}^{min min} \leq \leq {P P}^{t t}_{ex ex} \leq \leq {P P}_{ex ex}^{max max}$

其中，微电网与大电网交互容量下限；微电网与大电网交互容量上限； in, The lower limit of the interaction capacity between the microgrid and the large grid; The upper limit of the interaction capacity between the microgrid and the large grid;

c)微型燃气轮机和燃料电池的输出功率上、下限约束： c) The upper and lower limits of the output power of micro gas turbines and fuel cells:

${P P}_{FC FC}^{min min} \leq \leq {P P}_{FC FC}^{t t} \leq \leq {P P}_{FC FC}^{max max}$

${P P}_{MT MT}^{min min} \leq \leq {P P}_{MT MT}^{t t} \leq \leq {P P}_{MT MT}^{max max}$

其中

表示燃料电池、微型燃气轮机的最小出力，

表示燃料电池、微型燃气轮机的最大出力； in

Indicates the minimum output of fuel cells and micro gas turbines,

Indicates the maximum output of fuel cells and micro gas turbines;

d)蓄电池充放电功率上、下限约束： d) The upper and lower limits of battery charging and discharging power:

${P P}_{BT BT__ch ch}^{min min} \leq \leq {P P}_{BT BT__ch ch}^{t t} \leq \leq {P P}_{BT BT__ch ch}^{max max}$

${P P}_{BT BT__disch disc}^{min min} \leq \leq {P P}_{BT BT__disch disc}^{t t} \leq \leq {P P}_{BT BT__disch disc}^{max max}$

SOC_min≤SOC^t≤SOC_max SOC _min ≤ SOC ^t ≤ SOC _max

其中

为蓄电池t时刻充、放电功率；

为蓄电池最小充、放电功率；为蓄电池最大充、放电功率；SOC^t为蓄电池t时刻的存储容量，SOC_min为蓄电池存储容量最小值，SOC_max为蓄电池存储容量最大值。 in

is the charging and discharging power of the battery at time t;

The minimum charge and discharge power of the battery; is the maximum charging and discharging power of the battery; SOC ^t is the storage capacity of the battery at time t, SOC _min is the minimum storage capacity of the battery, and SOC _max is the maximum storage capacity of the battery.

所述多模态可定制绿色能量集线器的工作方法是： The working method of the multi-modal customizable green energy hub is:

1）数据采集模块采集风、光发电量及负荷预测相关信息； 1) The data acquisition module collects information related to wind, photovoltaic power generation and load forecasting;

2）分别建立风/光发电量及负荷预测的区间型模糊逻辑预测模型，输出风、光发电量预测的模糊区间集合和负荷预测的模糊区间集合； 2) Establish interval-type fuzzy logic forecasting models for wind/solar power generation and load forecasting, and output fuzzy interval sets for wind and photovoltaic power generation forecasts and fuzzy interval sets for load forecasting;

3）将2）所得各预测量的模糊区间集合输入能量管理优化模型中，定制能量交换功率P_ex，进行优化计算，得到可控微电源及蓄电池的发电计划区间集合，其上下界即为可控微电源和蓄电池备用容量，其中心值即为确定型发电计划； 3) Input the fuzzy interval set of each predicted quantity obtained in 2) into the energy management optimization model, customize the energy exchange power P _ex , and perform optimization calculations to obtain the set of power generation plan intervals of the controllable micro power supply and battery, and its upper and lower bounds are The central value is the deterministic power generation plan;

4）判断3）所得的发电计划是否合理，若不合理则返回3）重新设置能量交换功率P_ex，重新计算；若合理则其通过输出控制模块输出控制指令。 4) Judging whether the power generation plan obtained in 3) is reasonable, if not, return to 3) reset the energy exchange power P _ex , and recalculate; if it is reasonable, it outputs a control command through the output control module.

本发明采用上述技术方案，具有以下有益效果：本发明提出一种基于区间模糊预测的微电网能量管理算法，可获得具有备用容量的发电计划。通过风力和光伏发电量预测及负荷预测，得到各预测量的区间模糊集合，将其输入能量优化管理模块，最终得到可控微电源和储能单元的发电量计划值区间集合。区间集合的上、下界即为可控微电源和储能单元的上、下备用容量，区间集合的中心值即可控微电源的确定型计划出力。 The present invention adopts the above technical solution and has the following beneficial effects: the present invention proposes a microgrid energy management algorithm based on interval fuzzy prediction, which can obtain a power generation plan with reserve capacity. Through wind and photovoltaic power generation prediction and load forecasting, the interval fuzzy set of each predicted quantity is obtained, which is input into the energy optimization management module, and finally the controllable micro-power source and energy storage unit's planned value interval set of power generation is obtained. The upper and lower bounds of the interval set are the upper and lower reserve capacities of the controllable micro-power supply and the energy storage unit, and the central value of the interval set is the deterministic planned output of the controllable micro-power supply. the

附图说明 Description of drawings

图1为本发明实施例的结构示意图； Fig. 1 is the structural representation of the embodiment of the present invention;

图2为本发明实施例的方法流程图。 Fig. 2 is a flow chart of the method of the embodiment of the present invention. the

具体实施方式 Detailed ways

下面结合具体实施例，进一步阐明本发明，应理解这些实施例仅用于说明本发明而不用于限制本发明的范围，在阅读了本发明之后，本领域技术人员对本发明的各种等价形式的修改均落于本申请所附权利要求所限定的范围。 Below in conjunction with specific embodiment, further illustrate the present invention, should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention, after having read the present invention, those skilled in the art will understand various equivalent forms of the present invention All modifications fall within the scope defined by the appended claims of the present application. the

图1为本发明实施例的结构示意图，该种集线器包括数据采集模块、风/光发电量区间型模糊预测模块111、负荷区间型模糊预测模块112、能量管理优化模块12以及输出控制模块13，能够实现含有风力发电、光伏发电、微型燃气轮机、燃料电池等分布式电源和蓄电池的微电网能量优化管理，可实现微电网在不同运行模态（并网或孤岛）下的能量最优分配。具体而言： Fig. 1 is a schematic structural diagram of an embodiment of the present invention. This hub includes a data acquisition module, a wind/solar power generation interval type fuzzy prediction module 111, a load interval type fuzzy prediction module 112, an energy management optimization module 12, and an output control module 13. It can realize the energy optimization management of microgrids including wind power generation, photovoltaic power generation, micro gas turbines, fuel cells and other distributed power sources and batteries, and can realize the optimal energy distribution of microgrids under different operating modes (grid-connected or islanded). in particular:

（1）数据采集模块，用于采集风力和光伏发电量预测的相关信息，包括风速、光照强度、环境湿度及温度，历史负荷数据，此外还包括发电单元的燃料价格、微电网向大电网的出售电价及购买电价等。 (1) The data collection module is used to collect relevant information on wind power and photovoltaic power generation forecasts, including wind speed, light intensity, ambient humidity and temperature, historical load data, and also includes fuel prices of power generation units, microgrid to large grid Sell electricity price and purchase electricity price etc. the

（2）区间型模糊预测模块，用于分别建立风力、光伏发电量预测及负荷预测的区间型模糊逻辑模块，包括模糊器、规则库、推理机、降型器和解模糊器五部分。发电量模糊预测模块输入风速、光照强度、环境湿度和温度，输出风电和光伏发电量预测模糊区间集合；负荷模糊预测模块输入微电网历史负荷数据，输出负荷预测模糊区间集合。 (2) Interval-type fuzzy prediction module, which is used to establish interval-type fuzzy logic modules for wind power, photovoltaic power generation forecasting and load forecasting respectively, including five parts: fuzzer, rule base, inference engine, down-modeler and defuzzifier. The power generation fuzzy prediction module inputs wind speed, light intensity, ambient humidity and temperature, and outputs a set of wind power and photovoltaic power generation prediction fuzzy intervals; the load fuzzy prediction module inputs microgrid historical load data, and outputs a set of load prediction fuzzy intervals. the

（3）能量管理优化模块。风力发电和光伏发电很大程度上受天气状况的影响，将模糊预测得到的发电量预测区间集合作为初始输入，对可控微电源和蓄电池进行能量优化分配，建立如下能量管理优化模型： (3) Energy management optimization module. Wind power generation and photovoltaic power generation are largely affected by weather conditions. The set of power generation prediction intervals obtained by fuzzy prediction is used as the initial input, and the controllable micro power supply and storage battery are optimally allocated for energy, and the following energy management optimization model is established:

1）目标函数，以发电成本最小作为目标函数，使微电网自身利润最大化，表达式为： 1) Objective function, taking the minimum power generation cost as the objective function to maximize the profit of the microgrid itself, the expression is:

$\min F (x) = \min Σ_{t = 1}^{T} [C_{F} + C_{OM} + C_{BUY} + C_{SEL}]$ 其中T为调度周期的时间段，F(x)各量具体表达式如下， $\min f (x) = \min Σ_{t = 1}^{T} [C_{f} + C_{om} + C_{BUY} + C_{SEL}]$ Where T is the time period of the scheduling cycle, and the specific expressions of each quantity of F(x) are as follows,

a)燃料成本C_F， $C_{F} = Σ_{n}^{n} f (P_{i}^{t}) C_{i}$ a) Fuel cost C _F , $C_{f} = Σ_{no}^{no} f (P_{i}^{t}) C_{i}$

其中i表示发电单元种类，n为t时刻发电单元数目，

是发电单元i在t时刻的发电量，

是t时刻发电单元i所用燃料量，C_i是发电单元i所用燃料的价格。只有消耗化石燃料能源的发电单元才需计及燃料成本，此模型中有微型燃气轮机、燃料电池；而光伏发电、风力发电利用的是无污染的可再生自然资源，无需计及燃料成本。 Where i represents the type of power generation unit, n is the number of power generation units at time t,

is the power generation of power generation unit i at time t,

is the amount of fuel used by power generation unit i at time t, and C _i is the price of fuel used by power generation unit i. Only power generation units that consume fossil fuel energy need to take into account fuel costs. In this model, there are micro gas turbines and fuel cells; while photovoltaic power generation and wind power use non-polluting renewable natural resources, there is no need to take into account fuel costs.

b)运行维护费用C_OM， b) Operation and maintenance costs C _OM ,

${C C}_{OM om} = = {P P}_{Mt Mt.}^{t t} \cdot &Center Dot; {C C}_{MT MT__om om} + + {\overset{~ ~}{P P}}_{PV PV}^{t t} \cdot &Center Dot; {C C}_{PV PV__om om} + + {\overset{~ ~}{P P}}_{WT WT}^{t t} \cdot &Center Dot; {C C}_{WT WT__om om} + + | | {P P}_{BT BT}^{t t} | | \cdot \cdot {C C}_{BT BT__om om} + + {P P}_{FC FC}^{t t} \cdot &Center Dot; {C C}_{FC FC__om om}$

为微型燃气轮机和燃料电池t时刻的发电量；

为蓄电池t时刻的功率，

表示放电功率，

表示充电功率；

其中分别为光伏发电量预测区间的上下界，

分别为风力发电量预测区间的上下界。 Among them, C _{MT_om} , C _{PV_om} , C _{WT_om} , C _{BT_om} , and C _{FC_om} are the unit operation and maintenance costs of micro gas turbines, photovoltaic cells, wind power generators, storage batteries and fuel cells, respectively;

is the power generation of micro gas turbine and fuel cell at time t;

is the power of the battery at time t,

Indicates the discharge power,

Indicates the charging power;

in are the upper and lower bounds of the prediction interval of photovoltaic power generation,

are the upper and lower bounds of the forecast interval of wind power generation.

${C C}_{BUY BUY} = = {C C}_{buy buy}^{t t} \leq \leq {P P}_{ex ex}^{t t},, (({P P}_{ex ex}^{t t} > > 00))$

${C C}_{SEL SEL} = = {C C}_{sel sel}^{t t} \leq \leq {P P}_{ex ex}^{t t},, (({P P}_{ex ex}^{t t} > > 00))$

其中

是t时段从大电网购入电能的价格，

是t时段出售电能给大电网的价格。

表示t时段微电网与大电网间的功率交换。

表示向大电网售电，表示从大电网买电。 in

is the price of selling electric energy to the large power grid during the period t.

Indicates the power exchange between the microgrid and the large grid during the t period.

Indicates the sale of electricity to the large grid, Indicates buying electricity from the large power grid.

2）约束条件 2) Constraints

a)功率平衡约束： $P_{MT}^{t} + P_{FC}^{t} + P_{BT_disch}^{t} \cdot η_{BT}^{disch} + P_{ex}^{t} = {\tilde{P}}_{d}^{t} - {\tilde{P}}_{PV}^{t} - {\tilde{P}}_{WT}^{t}$ a) Power balance constraints: $P_{MT}^{t} + P_{FC}^{t} + P_{BT_disc}^{t} \cdot η_{BT}^{disc} + P_{ex}^{t} = {\tilde{P}}_{d}^{t} - {\tilde{P}}_{PV}^{t} - {\tilde{P}}_{WT}^{t}$

${P P}_{MT MT}^{t t} + + {P P}_{FC FC}^{t t} + + {P P}_{BT BT__ch ch}^{t t} \cdot &Center Dot; {η η}_{BT BT}^{ch ch} + + {P P}_{ex ex}^{t t} = = {\overset{~ ~}{P P}}_{d d}^{t t} - - {\overset{~ ~}{P P}}_{PV PV}^{t t} - - {\overset{~ ~}{P P}}_{WT WT}^{t t}$

其中

均如上述所述，

分别表示t时刻蓄电池的充、放电功率；

为蓄电池的充、放电效率；

为微电网负荷预测区间值

分别为负荷预测区间的上下限。 in

are as described above,

Respectively represent the charge and discharge power of the battery at time t;

is the charging and discharging efficiency of the battery;

Interval value for microgrid load prediction

are the upper and lower limits of the load forecast interval, respectively.

其中，

微电网与大电网交互容量下限；

微电网与大电网交互容量上限。 in,

The lower limit of the interaction capacity between the microgrid and the large grid;

The upper limit of the interaction capacity between the microgrid and the large grid.

其中

表示燃料电池、微型燃气轮机的最小出力，

表示燃料电池、微型燃气轮机的最大出力。 in

Indicates the minimum output of fuel cells and micro gas turbines,

Indicates the maximum output of fuel cells and micro gas turbines.

SOC_min≤SOC^t≤SOC_max SOC _min ≤ SOC ^t ≤ SOC _max

其中

为蓄电池t时刻充、放电功率；

为蓄电池最小充、放电功率；

为蓄电池最大充、放电功率；SOC^t为蓄电池t时刻的存储容量，SOC_min为蓄电池存储容量最小值，SOC_max为蓄电池存储容量最大值。 in

is the charging and discharging power of the battery at time t;

The minimum charge and discharge power of the battery;

is the maximum charging and discharging power of the battery; SOC ^t is the storage capacity of the battery at time t, SOC _min is the minimum storage capacity of the battery, and SOC _max is the maximum storage capacity of the battery.

（4）输出控制模块，基于能量管理优化模块对各可控微电源和蓄电池的出力优化分配结果，通过输出模块将控制指令输出，实现对可控微电源及储能单元的管理。 (4) The output control module, based on the output optimization distribution results of each controllable micro-power supply and battery by the energy management optimization module, outputs the control command through the output module to realize the management of the controllable micro-power supply and energy storage unit. the

图2为本发明实施例的方法流程图，具体步骤包括： Fig. 2 is the method flowchart of the embodiment of the present invention, and concrete steps comprise:

1）数据采集模块采集风、光发电量及负荷预测相关信息，包括风速、光照强度、环境湿度、温度及微电网历史负荷数据； 1) The data acquisition module collects information related to wind and photovoltaic power generation and load forecasting, including wind speed, light intensity, ambient humidity, temperature and historical load data of the microgrid;

2）分别建立风、光发电量及负荷预测的区间型模糊逻辑预测模型，将风速、光照强度、环境湿度和温度输入预测模块中，输出风、光发电量预测的模糊区间集合；将负荷历史数据输入负荷预测模块中，输出负荷预测的模糊区间集合； 2) Establish interval-type fuzzy logic forecasting models for wind and photovoltaic power generation and load forecasting respectively, input wind speed, light intensity, ambient humidity and temperature into the prediction module, and output fuzzy interval sets for wind and photovoltaic power generation forecasting; load history In the data input load forecasting module, output the fuzzy interval set of load forecasting;

3）将2）所得各预测量的模糊区间集合输入区间型能量管理优化模型中，定制能量交换功率P_ex，进行优化计算，得到可控微电源及蓄电池的发电计划区间集合，其上下界即为可控微电源和蓄电池上、下可调备用容量，其中心值即为确定型发电计划； 3) Input the fuzzy interval set of each predicted measurement obtained in 2) into the interval energy management optimization model, customize the energy exchange power P _ex , and perform optimization calculations to obtain the controllable micro power supply and battery power generation plan interval set, the upper and lower bounds of which are It is a controllable micro-power source and an adjustable reserve capacity up and down for the storage battery, and its central value is the deterministic power generation plan;

4）判断3）所得的发电计划是否合理，若不合理则返回3）重新设置能量交换功率P_ex，重新计算,；若合理则其通过输出控制模块输出。 4) Judging whether the power generation plan obtained in 3) is reasonable, if not, return to 3) reset the energy exchange power P _ex , and recalculate; if it is reasonable, it will be output through the output control module.

Claims

1. A multi-mode customizable green energy hub is characterized in that it includes a data acquisition module and an output control module, as well as an interval prediction module and an energy management optimization module, and performs energy optimization distribution through an energy management optimization model;

The data acquisition module inputs the collected data into the interval prediction module, and the interval prediction module outputs the prediction data and then inputs the data into the energy management optimization module, and finally outputs control instructions through the output control module. the

2. The multimodal customizable green energy hub according to claim 1, wherein the energy management optimization model is:

1) The objective function, taking the minimum power generation cost as the objective function to maximize the profit of the microgrid itself, the expression is:

Among them, T is the time period of the scheduling cycle, C _F is the fuel cost, C _OM is the operation and maintenance cost, C _BUY is the cost of purchasing from the large power grid, and C _SEL is the profit obtained from selling to the large power grid,

a) The cost of fuel is C _F ,

Where i represents the type of power generation unit, n is the number of power generation units at time t,

is the power generation of power generation unit i at time t,

is the amount of fuel used by power generation unit i at time t, and C _i is the price of fuel used by power generation unit i;

b) Operation and maintenance costs C _OM ,

Among them, C _{MT_om} , C _{PV_om} , C _{WT_om} , C _{BT_om} , and C _{FC_om} are the unit operation and maintenance costs of micro gas turbines, photovoltaic cells, wind power generators, storage batteries and fuel cells, respectively; is the power generation of micro gas turbine and fuel cell at time t;

is the power of the battery at time t, Indicates the discharge power,

Indicates the charging power;

in

c) C _BUY is the cost of buying from the large power grid, and C _SEL is the profit obtained from selling to the large power grid, specifically

in is the price of electricity purchased from the large power grid during the period t, is the price of selling electric energy to the large power grid in period t,

Indicates the power exchange between the microgrid and the large grid during the t period, Indicates the sale of electricity to the large grid, Indicates buying electricity from the large power grid;

2) Constraints, including power balance constraints, maximum interactive capacity constraints between microgrid and large grid, upper and lower limits of output power of micro gas turbines and fuel cells, and upper and lower limits of battery charging and discharging power;

a) Power balance constraints:

in

are as described above,

Respectively represent the charge and discharge power of the battery at time t;

is the charging and discharging efficiency of the battery;

Interval value for microgrid load prediction

are the upper and lower limits of the load forecast interval;

b) The maximum interactive capacity constraint between the microgrid and the large grid, that is, the physical transmission capacity limit of the tie line:

in,

Indicates the lower limit of the interaction capacity between the microgrid and the large grid;

: The upper limit of the interaction capacity between the microgrid and the large grid;

c) The upper and lower limits of the output power of micro gas turbines and fuel cells:

in Indicates the minimum output of fuel cells and micro gas turbines,

Indicates the maximum output of fuel cells and micro gas turbines;

d) The upper and lower limits of battery charging and discharging power:

SOC _min ≤ SOC ^t ≤ SOC _max

in

is the charging and discharging power of the battery at time t;

3. The multi-mode customizable green energy hub according to claim 1, wherein the interval type forecasting module includes a wind/light power generation forecasting module and a load forecasting module; the wind/light power generation forecasting module outputs wind/light A set of fuzzy intervals for power generation forecasting; a set of fuzzy intervals for load forecasting output by the load forecasting module. the

4. The multi-modal customizable green energy hub according to claim 3, wherein the wind/solar power generation forecasting module and the load forecasting module include a fuzzer, a rule base, an inference engine, a downsampler and a defuzzifier. the

5. Realize the method for multi-mode customizable green energy hub as claimed in claim 1, it is characterized in that, comprising the steps:

1) The data acquisition module collects environmental information such as wind force, light and historical load related information;

2) Establish interval fuzzy logic models for wind/solar power generation forecast and load forecast respectively, and output fuzzy interval sets for wind and photovoltaic power generation forecasts and load forecast fuzzy interval sets;

3) Input the fuzzy interval set of each predicted quantity obtained in 2) into the energy management optimization model, customize the energy exchange power P _ex , and perform optimization calculations to obtain the set of power generation plan intervals of the controllable micro power supply and battery, and its upper and lower bounds are The central value is the deterministic power generation plan;

4) Judging whether the power generation plan obtained in 3) is reasonable, if not, return to 3) reset the energy exchange power P _ex , and recalculate; if it is reasonable, it outputs a control command through the output control module.