CN105743081A - On-line energy dispatching method of community level DC microgrid group - Google Patents
On-line energy dispatching method of community level DC microgrid group Download PDFInfo
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- CN105743081A CN105743081A CN201610178888.7A CN201610178888A CN105743081A CN 105743081 A CN105743081 A CN 105743081A CN 201610178888 A CN201610178888 A CN 201610178888A CN 105743081 A CN105743081 A CN 105743081A
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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
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Abstract
The invention belongs to the technical field of a microgrid of a power system, in particular to an on-line energy dispatching method of a community level DC microgrid group. The on-line energy dispatching method comprises the following steps of determining a target function, a decision variable and a constraint condition of energy dispatching of the microgrid group; determining a Liapunov function and Liapunov drift with regard to an energy storage queue; converting a problem to a sub problem of each moment by a Liapunov optimization method; acquiring photovoltaic output, a load and energy storage residual electric quantity of a microgrid controller by each microgrid controller to determine charging-discharging quantity of energy storage; and calculating load vacancy and residual electric quantity of the microgrid controller, and sending the load vacancy and the residual electric quantity to a central controller to solve the sub problem, thereby obtaining the decision variable and carrying out cycle optimization. By the on-line energy dispatching method, an energy sharing problem is converted to a linear planning problem without relying on future information, only information at the current moment is needed to achieve real-time decision of electric quantity exchange among microgrids, the absorption rate of renewable energy sources is improved, the computation complexity is low, the execution efficiency is high, and the on-line energy dispatching method is applicable to scale expansion of the microgrid group.
Description
Technical field
The invention belongs to power system micro-capacitance sensor technical field, particularly relate to a kind of community-level direct-current grid group at heat input dispatching method.
Background technology
Micro-capacitance sensor is the ideal platform of distributed coordination regenerative resource and customer charge.The result of current micro-capacitance sensor specifically includes that exchange micro-capacitance sensor, direct-current grid and mixing micro-capacitance sensor.Along with direct current power load ratio is gradually increased, what energy storage device used increases, the raising that power supply quality is required by sensitive load, and the output form of most of regenerative resource is direct current, direct-current grid will become a kind of important model in following micro-capacitance sensor field.Direct-current grid, without considering the problems such as frequency stability, kelvin effect, Reactive-power control and A.C.power loss, improves the operational efficiency of whole micro-capacitance sensor.
In order to prevent the waste of power cut-off accident and regenerative resource, most of direct-current grid remain a need for keeping coupling with public electric wire net.In order to reduce the intermittence impact on electrical network of regenerative resource output, need between micro-capacitance sensor to interconnect, namely define direct-current grid group.Community-level direct-current grid group is appreciated that the community for being made up of multiple direct-current grid, and each micro-capacitance sensor is single resident, including distributed power source, energy storage device and corresponding load.Each micro-capacitance sensor all has energy scheduling controller, and can communicate with other micro-capacitance sensor, to heighten the efficiency of energy utilization of whole community.
Summary of the invention
Share to carry the energy controlled between micro-capacitance sensor, improve regenerative resource digestion capability in community, the present invention propose a kind of community-level direct-current grid group at heat input dispatching method, including:
Step 1: determine object function, decision variable and relevant constraints that micro-capacitance sensor group energy is dispatched, form original optimization problem；
Step 2: for energy storage queue, it is determined that liapunov function and Liapunov drift；
Step 3: adopt Liapunov optimization method that original optimization problem is converted into the subproblem in each moment；
Step 4: the controller of each direct-current grid obtains the photovoltaic in moment self instantly and exerts oneself, the related data of load and energy storage dump energy；
Step 5: each DC micro-electric net controller discharge and recharge according to the information decision energy storage obtained, calculates self load vacancy and dump energy；
Step 6: each DC micro-electric net controller sends the relevant information of self to central controller；
Step 7: the subproblem in moment instantly is solved by central controller according to the information obtained, and obtains the decision variable in moment instantly；
Step 8: the time that updates, to subsequent time, returns step 4, until whole optimization time interval terminates.
In described step 1, the object function of micro-capacitance sensor group energy scheduling is maximize photovoltaic dissolving in micro-capacitance sensor group, decision variable is the exchange electricity between each moment micro-capacitance sensor, constraints includes power-balance constraint, the constraint of energy storage dump energy and maximum charge-discharge electric power.
In described step 2, energy storage queue table is shown asN represents the quantity of micro-capacitance sensor in micro-capacitance sensor group；Liapunov function isRepresent the electricity of accumulation in energy storage queue；Liapunov drift is defined asFor the expectation of liapunov function value adjacent moment changing value, represent the stability of queue, EiT () is the charge level of energy storage in i-th direct-current grid.
The discharge and recharge of micro-capacitance sensor controller decision-making energy storage in described step 5, its principle of decision-making is formulated with workload demand according to photovoltaic power generation quantity；When photovoltaic power generation quantity is more than workload demand, the charge capacity of energy storage is the remaining electricity of photovoltaic, and needs the constraint being subject to stored energy capacitance with maximum charge power, and the discharge electricity amount of energy storage is 0；When workload demand is more than photovoltaic power generation quantity, the charge capacity of energy storage is 0, and the discharge electricity amount of energy storage needs the constraint being subject to energy storage dump energy with maximum discharge power.
It is the load vacancy after load supply electricity that described load vacancy is defined as in micro-capacitance sensor photovoltaic generation unit with energy-storage units in the moment instantly, namely can only be supplied the load vacancy of electricity by other micro-capacitance sensor.
Described dump energy be energy storage instantly time be engraved in inside micro-capacitance sensor to complete the dump energy after discharge and recharge, the electricity of other micro-capacitance sensor can be transferred to.
The beneficial effects of the present invention is: proposition at heat input dispatching method, energy sharing problem is converted to linear programming problem, be independent of the Future Information in system, it is only necessary to the information in moment can realize exchanging between micro-capacitance sensor the Real-time Decision of electricity instantly.In meeting micro-capacitance sensor while customer charge demand, it is effectively improved regenerative resource dissolving in micro-capacitance sensor group.Meanwhile, computation complexity is low, and execution efficiency is high, the well adapting to property of expansion to direct-current grid group's scale.
Accompanying drawing explanation
Fig. 1 is the structure chart of direct-current grid group.
Fig. 2 is the flow chart at heat input dispatching method.
Fig. 3 is that micro-capacitance sensor group optimizes front and back photovoltaic and dissolves the comparing result figure of rate.
Fig. 4 is the exchange electricity after optimizing between micro-capacitance sensor.
Fig. 5 is the charge level of each micro-capacitance sensor energy storage under stand-alone mode.
Fig. 6 is the charge level of each micro-capacitance sensor energy storage under interconnection mode.
Detailed description of the invention
Below in conjunction with accompanying drawing, embodiment is elaborated.The present invention propose a kind of community-level direct-current grid group at heat input dispatching method.
Fig. 1 is the structure chart of direct-current grid group.Element in each micro-capacitance sensor includes the controller of photovoltaic generation, energy storage device, power load, current transformer and micro-capacitance sensor.Wherein, the element within micro-capacitance sensor is carried out information gathering and controls in real time by micro-capacitance sensor controller.Two-way DC/AC current transformer is that load is powered for introducing the energy of electrical network when photovoltaic generation energy shortage, and meanwhile, DC/AC current transformer ensure that the power supply to AC load.Photovoltaic generation unit is made up of photovoltaic array, inverter etc..In each micro-capacitance sensor, first the generated energy of photovoltaic powers for internal load.After meeting workload demand, unnecessary photovoltaic electricity will be used for charging for energy storage, or share to the micro-capacitance sensor of photovoltaic shortage.Energy-storage units is made up of battery and charge-discharge machine.The effect of energy storage is to store the electricity that photovoltaic is unnecessary after photovoltaic meets micro-grid load demand.When photovoltaic deficiency, the electricity in energy storage will for for making up load vacancy.
Energy source in each micro-capacitance sensor includes photovoltaic generation unit and energy-storage units.Energy situation is real-time transmitted to central controller by each micro-capacitance sensor controller.Participant that central controller is shared according to the energy information decision-making energy of each micro-capacitance sensor and electricity size, and the DC-DC current transformer externally connected by each micro-capacitance sensor realized the energy and shares.If there is energy micro-capacitance sensor more than needed and energy starved micro-capacitance sensor in direct-current grid group simultaneously, central controller can send corresponding energy shared instruction.The energy is shared to be needed to observe following principle:
(1) for the micro-capacitance sensor that the energy is more than needed, when providing energy for energy starved micro-capacitance sensor, energy source pays the utmost attention to unnecessary photovoltaic electricity, next to that energy storage electricity.
(2) considering the charge-discharge energy loss of energy storage, the exchange electricity between micro-capacitance sensor is only used for supplying load, is not used to energy storage charging.
Fig. 2 is the flow chart at heat input dispatching method.Adopt in Fig. 2 at heat input dispatching method, it is possible to the effect reached is, by adjusting the exchange electricity between each moment micro-capacitance sensor, improves photovoltaic rate of dissolving in micro-capacitance sensor group, and ensures the stability of system.The Liapunov optimization method adopted, it is not necessary to following information, only depends on the information in moment instantly and is optimized, and complicated optimization problem is converted to the linear programming problem being easy to solve, makes computation complexity be substantially reduced.Meanwhile, compared with tradition optimized algorithm, when system variable dimension increases, the computation complexity of this algorithm can't exponentially increase by type, suffers " dimension disaster ", but increases in line style, the well adapting to property of expansion to micro-capacitance sensor group's scale.
With 10 direct-current grid for object of study, carrying out one-week emulation at heat input dispatching method, the photovoltaic rate Comparative result of dissolving before and after can optimizing is as shown in table 1.
Table 1: optimize before and after photovoltaic dissolve rate contrast
| Before optimization | After optimization | Enhancing rate |
| 0.472 | 0.540 | 14.41% |
Fig. 3 is that micro-capacitance sensor group optimizes front and back photovoltaic and dissolves the comparing result figure of rate.It can be seen that optimize the photovoltaic rate of dissolving of most of the time section in one day after can reach 100%.In the period in morning of every day, owing to the electricity in energy storage reaches depth of discharge, photovoltaic power generation quantity is 0, and therefore the photovoltaic rate of dissolving is 0.
Fig. 4 is the exchange electricity after optimizing between micro-capacitance sensor.Wherein, represent this micro-capacitance sensor above abscissa and transmit energy to other micro-capacitance sensor, represent this micro-capacitance sensor below abscissa and receive energy from other micro-capacitance sensor.And Fig. 3 contrast is it can be seen that when there is exchange electricity between micro-capacitance sensor, the photovoltaic replacement rate after optimization is higher than before optimizing.In the period at noon of every day, owing to photovoltaic is relatively sufficient, each micro-capacitance sensor can meet load by the energy of self, so there is no exchange.In the period in morning of every day, owing to photovoltaic power generation quantity is 0, the electricity in energy storage simultaneously exhausts, and therefore cannot exchange.
Fig. 5 and Fig. 6 is stand-alone mode and the charge level of each micro-capacitance sensor energy storage under interconnection mode respectively.It can be seen that in of every day, there is larger difference in the result before and after optimizing the dusk time-division.In result after optimization, charge transport unnecessary in micro-capacitance sensor energy storage gives other micro-capacitance sensor to make up load vacancy, it is ensured that the stability of energy storage queue, substantially increases the energy utilization rate of energy-storage units.Owing to the energy in energy-storage units is all from photovoltaic energy, the on-site elimination rate of photovoltaic is also greatly enhanced.
In order to verify the execution efficiency put forward at heat input dispatching method, for the micro-capacitance sensor group of different scales, test the operation time of central controller.Adopting conventional computer as test environment, wherein processor is Inteli5-4200U, dominant frequency 1.6GHz, RAM capacity is 4GB.Can obtaining, when micro-capacitance sensor quantity changes in 10～100, the excursion of central controller decision time is 0.005～0.009s, has stronger real-time.
Present invention may apply to resident or the micro-capacitance sensor group of small business users composition.The present invention propose at heat input dispatching method, energy sharing problem is converted to linear programming problem, is independent of the Future Information in system, it is only necessary to the information in moment can realize exchanging between micro-capacitance sensor the Real-time Decision of electricity instantly.In meeting micro-capacitance sensor while customer charge demand, it is effectively improved regenerative resource dissolving in micro-capacitance sensor group.Meanwhile, computation complexity is low, and execution efficiency is high, the well adapting to property of expansion to direct-current grid group's scale.
This embodiment is only the present invention preferably detailed description of the invention; but protection scope of the present invention is not limited thereto; any those familiar with the art in the technical scope that the invention discloses, the change that can readily occur in or replacement, all should be encompassed within protection scope of the present invention.Therefore, protection scope of the present invention should be as the criterion with scope of the claims.
Claims (6)
1. a community-level direct-current grid group at heat input dispatching method, it is characterised in that including:
Step 1: determine object function, decision variable and relevant constraints that micro-capacitance sensor group energy is dispatched, form original optimization problem；
Step 2: for energy storage queue, it is determined that liapunov function and Liapunov drift；
Step 3: adopt Liapunov optimization method that original optimization problem is converted into the subproblem in each moment；
Step 4: the controller of each direct-current grid obtains the photovoltaic in moment self instantly and exerts oneself, the related data of load and energy storage dump energy；
Step 5: each DC micro-electric net controller discharge and recharge according to the information decision energy storage obtained, calculates self load vacancy and dump energy；
Step 6: each DC micro-electric net controller sends the relevant information of self to central controller；
Step 7: the subproblem in moment instantly is solved by central controller according to the information obtained, and obtains the decision variable in moment instantly；
Step 8: the time that updates, to subsequent time, returns step 4, until whole optimization time interval terminates.
2. method according to claim 1, it is characterized in that, in described step 1, the object function of micro-capacitance sensor group energy scheduling is maximize photovoltaic dissolving in micro-capacitance sensor group, decision variable is the exchange electricity between each moment micro-capacitance sensor, constraints includes power-balance constraint, the constraint of energy storage dump energy and maximum charge-discharge electric power.
3. method according to claim 1, it is characterised in that in described step 2, energy storage queue table is shown asN represents the quantity of micro-capacitance sensor in micro-capacitance sensor group；Liapunov function isRepresent the electricity of accumulation in energy storage queue；Liapunov drift is defined asFor the expectation of liapunov function value adjacent moment changing value, represent the stability of queue, EiT () is the charge level of energy storage in i-th direct-current grid.
4. method according to claim 1, it is characterised in that the discharge and recharge of micro-capacitance sensor controller decision-making energy storage in described step 5, its principle of decision-making is formulated with workload demand according to photovoltaic power generation quantity；When photovoltaic power generation quantity is more than workload demand, the charge capacity of energy storage is the remaining electricity of photovoltaic, and needs the constraint being subject to stored energy capacitance with maximum charge power, and the discharge electricity amount of energy storage is 0；When workload demand is more than photovoltaic power generation quantity, the charge capacity of energy storage is 0, and the discharge electricity amount of energy storage needs the constraint being subject to energy storage dump energy with maximum discharge power.
5. method according to claim 1, it is characterized in that, it is the load vacancy after load supply electricity that described load vacancy is defined as in micro-capacitance sensor photovoltaic generation unit with energy-storage units in the moment instantly, namely can only be supplied the load vacancy of electricity by other micro-capacitance sensor.
6. method according to claim 1, it is characterised in that described dump energy be energy storage instantly time be engraved in inside micro-capacitance sensor to complete the dump energy after discharge and recharge, the electricity of other micro-capacitance sensor can be transferred to.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108667030A (en) * | 2018-04-24 | 2018-10-16 | 国网天津市电力公司电力科学研究院 | A kind of polynary duty control method based on prediction model |
| CN112448416A (en) * | 2020-09-10 | 2021-03-05 | 中国电建集团江西省电力建设有限公司 | Off-grid type micro-grid group and power balance control method thereof |
| CN113784865A (en) * | 2019-02-08 | 2021-12-10 | 霍利瓦特公司 | Device for supplying at least one energy consumption unit or at least one energy recovery unit |
| CN113937802A (en) * | 2021-09-10 | 2022-01-14 | 南京南瑞继保电气有限公司 | Micro-grid real-time scheduling method and device based on Lyapunov optimization |
| CN118040789A (en) * | 2024-01-30 | 2024-05-14 | 广东工业大学 | New energy micro-grid group scheduling method considering stability constraint |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102710013A (en) * | 2012-05-23 | 2012-10-03 | 中国电力科学研究院 | Park energy-network energy optimizing management system based on microgrids and implementing method thereof |
| CN104484757A (en) * | 2014-12-15 | 2015-04-01 | 中山大学 | Heterogeneous load scheduling and energy management method applied to intelligent micro grid |
| CN104767224A (en) * | 2015-03-04 | 2015-07-08 | 华南理工大学 | Energy management method for multi-class energy storage grid-connected wind-solar energy storage micro-grid |
| CN105262129A (en) * | 2015-10-22 | 2016-01-20 | 华南理工大学 | Multi-objective optimization system and multi-objective optimization method containing composite energy storage micro grid |
| CN105406515A (en) * | 2015-12-29 | 2016-03-16 | 中国科学院广州能源研究所 | Hierarchically-controlled independent microgrid |
-
2016
- 2016-03-25 CN CN201610178888.7A patent/CN105743081A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102710013A (en) * | 2012-05-23 | 2012-10-03 | 中国电力科学研究院 | Park energy-network energy optimizing management system based on microgrids and implementing method thereof |
| CN104484757A (en) * | 2014-12-15 | 2015-04-01 | 中山大学 | Heterogeneous load scheduling and energy management method applied to intelligent micro grid |
| CN104767224A (en) * | 2015-03-04 | 2015-07-08 | 华南理工大学 | Energy management method for multi-class energy storage grid-connected wind-solar energy storage micro-grid |
| CN105262129A (en) * | 2015-10-22 | 2016-01-20 | 华南理工大学 | Multi-objective optimization system and multi-objective optimization method containing composite energy storage micro grid |
| CN105406515A (en) * | 2015-12-29 | 2016-03-16 | 中国科学院广州能源研究所 | Hierarchically-controlled independent microgrid |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108667030A (en) * | 2018-04-24 | 2018-10-16 | 国网天津市电力公司电力科学研究院 | A kind of polynary duty control method based on prediction model |
| CN113784865A (en) * | 2019-02-08 | 2021-12-10 | 霍利瓦特公司 | Device for supplying at least one energy consumption unit or at least one energy recovery unit |
| CN112448416A (en) * | 2020-09-10 | 2021-03-05 | 中国电建集团江西省电力建设有限公司 | Off-grid type micro-grid group and power balance control method thereof |
| CN113937802A (en) * | 2021-09-10 | 2022-01-14 | 南京南瑞继保电气有限公司 | Micro-grid real-time scheduling method and device based on Lyapunov optimization |
| CN118040789A (en) * | 2024-01-30 | 2024-05-14 | 广东工业大学 | New energy micro-grid group scheduling method considering stability constraint |
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