CN106230007A - A kind of micro-capacitance sensor energy storage Optimization Scheduling - Google Patents

Abstract

The present invention provides a kind of micro-capacitance sensor energy storage Optimization Scheduling, the intelligent micro-grid structure of the electricity consumption type including direct current and exchange mixing and AC/DC changeover switch is optimized scheduling, described dispatching method comprises the following steps: the load of next day and photovoltaic, wind turbine power generation are predicted by step 1, step 2 based on load and photovoltaic, wind turbine power generation predict the outcome and the transition status of AC/DC changeover switch and the charging and discharging state of energy-storage battery are set up Optimized model with total electricity charge minimum by electricity price, Optimized model is optimized by step 3；The present invention considers AC load and DC load simultaneously, is optimized the transition status of AC/DC changeover switch and the charging and discharging state of energy-storage battery, and then realizes the purpose of power cost saving, and the present invention is applicable to different types of micro-capacitance sensor user.

Description

A kind of micro-capacitance sensor energy storage Optimization Scheduling

Technical field

The present invention relates to a kind of micro-capacitance sensor energy storage Optimization Scheduling, belong to new forms of energy and electric power demand side response field.

Background technology

Along with the development of intelligent grid and popularizing of low-carbon technology, following small-scale generation of electricity by new energy, energy storage device, heat Pumps etc. will be used widely in power consumer.These technology not only have impact on original power system operating mode, also gives and uses Side, family electric energy uses the more motility brought, and user can be with the price incentive signal of responsive electricity grid to change its electricity consumption row For, i.e. Demand Side Response.Electrical network Peak power use can be reduced by Demand Side Response, promote the electricity consumption of paddy phase.Micro-capacitance sensor energy storage is excellent Changing dispatching patcher is the important means realizing user side Demand Side Response, the most increasingly comes into one's own.Micro-capacitance sensor energy storage The advantage of Optimal Scheduling is:

(1) by coordinating local generating and can use with the electrically optimized energy, promote energy use efficiency；

(2) by introducing DC load lifting energy efficiency；

(3) by Demand Side Response peak load shifting, electrical network economy type and safety are promoted；

(4) the direct economy income to terminal use.

Micro-capacitance sensor energy storage Optimal Scheduling is the use by configuring electric energy to the meaning of user's most worthy, permissible User's electricity consumption when electricity price height is transferred to electricity price low time electricity consumption, thus realize the purpose of power cost saving, current micro-capacitance sensor Energy storage Optimal Scheduling does not accounts for direct current and the electricity consumption type exchanging mixing and the collaborative optimization side of DC-AC conversion device Method.

Summary of the invention

In order to overcome above-mentioned the deficiencies in the prior art, the invention provides a kind of electricity consumption class considering direct current and exchange mixing The micro-capacitance sensor energy storage Optimal Scheduling cooperative optimization method of type and DC-AC conversion device.The method based on load, photovoltaic and Wind turbine power generation predict the outcome and electricity price to the transition status of AC/DC changeover switch and the charging and discharging state of energy-storage battery with Total minimum target of the electricity charge is optimized.By the electric energy of micro-capacitance sensor energy storage Optimal Scheduling is reasonably optimized so that The load of electricity price peak time is transferred to electricity price low ebb period by total system, thus reaches the purpose of power cost saving, and the method can To apply in different types of intelligent micro-grid energy storage Optimal Scheduling, with or without electrothermal load and alternating current-direct current load.

The technical solution adopted in the present invention is:

To including that major network supplies electricity to ac bus, ac bus carries out turning by alternating current-direct current bidirectional transducer with dc bus Change, ac bus is furnished with AC load, dc bus is furnished with DC load, energy-storage battery, photovoltaic power generation apparatus and wind The typical intelligent micro-grid structure of machine TRT is optimized scheduling,

This dispatching method comprises the steps:

Step 1: the load of next day and photovoltaic, wind turbine power generation are predicted.

Step 2: based on load and photovoltaic, the predicting the outcome and the electricity price conversion shape to AC/DC changeover switch of wind turbine power generation The charging and discharging state of state and energy-storage battery sets up Optimized model with total electricity charge minimum.

Step 3: Optimized model is optimized.

For described step 1, it was predicted that method is conventional customer charge and photovoltaic, wind turbine power generation Forecasting Methodology, such as nerve Network and support vector machine method.

For described step 2, the object function of optimization is whole day total electricity charge constraints.

$Min\underset{t=1}{\overset{96}{\Σ}}C\left(t\right)P\left(t\right)T$

In formula, t is period conversion, and 96 is whole day period sum, the value that whole day period sum can also be suitable for for other.C T () is the tou power price of period t.P (t) is the period t output power obtained from major network, and T is unit Period Length, here For 0.25h.

Constraints includes,

General power Constraints of Equilibrium:

P (t)=P_AC-load(t)+P_AC-DC(t)

In formula, P_AC-loadT () is the AC load of period t, P_AC-DCT () is the power of AC/DC changeover switch.

AC/DC changeover switch efficiency constraints:

$P_{AC-DC}\left(t\right)=\left\{\begin{array}{ccc}{\mathrm{\&eta;}}_{A/D}{P}_{DC}\left(t\right)& if& P_{DC}\left(t\right)>0\\ 0& if& P_{DC}\left(t\right)=0\\ {\mathrm{\&eta;}}_{D/A}{P}_{DC}\left(t\right)& if& P_{DC}\left(t\right)<0\end{array}\right.$

In formula, η_A/DThe conversion efficiency of direct current, η is converted to for exchange_D/AThe conversion efficiency of exchange, P it is converted into for direct current_DC T () is the dc bus total load of period t.

Dc bus total load retrains:

P_DC(t)+P_PV(t)+P_w(t)=P_DC-load(t)+P_B(t)

In formula, P_PVT () is photovoltaic generation power, P_wT () is wind-power electricity generation power, P_DC-loadT () is DC load, P_B(t) For energy-storage battery at the power of period t, P during charging_B(t) ＞ 0, P during electric discharge_B(t) ＜ 0.

Efficiency for charge-discharge retrains:

$P_D^{max}\&le;P_B\left(t\right)\&le;P_C^{max}$

In formula,For maximum charge efficiency,For maximum discharging efficiency, P_BT () is the charge efficiency of period t.

Energy-storage battery state of charge retrains:

E_min≤E_B(t)≤E_max

In formula, E_maxFor energy-storage battery maximum electricity, E_minFor the minimum electricity of energy-storage battery, E_min≤E_BT () is period t's Battery status.

Battery electric quantity state giant ties:

$\left\{\begin{array}{c}{E}_B\left(t\right)=E\left(0\right)+\underset{t=1}{\overset{i}{\&Sigma;}}E\left(t\right)\\ E\left(t\right)=P_B\left(t\right)T\\ E_{\min }\&le;E\left(0\right)\&le;E_{\max }\end{array}\right.$

In formula, E (0) is battery initial quantity of electricity, and E (t) is the accumulation electricity of period t.

Day electricity giant ties:

$\underset{t=1}{\overset{96}{\&Sigma;}}{P}_B\left(t\right)=0$

This formula represents that energy-storage battery day accumulation electricity is 0.

For described step 3, its optimization process is:

(1) load and photovoltaic, blower fan prediction and electricity price information are read

(2) model objective function and constraints are set up

(3) use branch and bound method that model is optimized

(4) output optimum results: the charge-discharge electric power of battery and period, the power of AC/DC changeover switch and period.

The invention has the beneficial effects as follows that the present invention examines simultaneously relative to existing micro-capacitance sensor energy storage Optimized Operation optimization method Consider AC load and DC load, the transition status of AC/DC changeover switch and the charging and discharging state of energy-storage battery have been carried out Optimizing, and then realize the purpose of power cost saving, the present invention is applicable to different types of micro-capacitance sensor user.

Accompanying drawing explanation

Fig. 1 is typical intelligent micro-grid structure chart；

Fig. 2 is micro-capacitance sensor energy storage Optimal Scheduling figure.

Detailed description of the invention

Describe the preferred embodiments of the present invention below in conjunction with the accompanying drawings in detail.

Embodiment is as it is shown in figure 1, typical intelligent micro-grid structure includes that major network supplies electricity to ac bus, and ac bus is with straight Stream bus is changed by alternating current-direct current bidirectional transducer, ac bus is furnished with AC load, dc bus is furnished with direct current Load, energy-storage battery, photovoltaic power generation apparatus and wind turbine power generation device.

First by conventional customer charge, photovoltaic and blower fan Forecasting Methodology, the present embodiment use neutral net and support to The load of next day and photovoltaic and wind-power electricity generation are predicted by amount machine method.

It is then based on load and photovoltaic, the predicting the outcome and the electricity price transition status to AC/DC changeover switch of wind turbine power generation And the charging and discharging state of energy-storage battery sets up Optimized model with total electricity charge minimum.

The object function optimized is whole day total electricity charge constraints.

$Min\underset{t=1}{\overset{96}{\&Sigma;}}C\left(t\right)P\left(t\right)T$

In formula, t is period conversion, and 96 is whole day period sum, the value that whole day period sum can also be suitable for for other.C T () is the tou power price of period t.P (t) is the period t output power obtained from major network, and T is unit Period Length, here For 0.25h.

Constraints includes:

General power Constraints of Equilibrium:

P (t)=P_AC-load(t)+P_AC-DC(t)

P_AC-loadT () is the AC load of period t, P_AC-DCT () is the power of AC/DC changeover switch.

AC/DC changeover switch efficiency constraints:

$P_{AC-DC}\left(t\right)=\left\{\begin{array}{ccc}{\mathrm{\&eta;}}_{A/D}{P}_{DC}\left(t\right)& if& P_{DC}\left(t\right)>0\\ 0& if& P_{DC}\left(t\right)=0\\ {\mathrm{\&eta;}}_{D/A}{P}_{DC}\left(t\right)& if& P_{DC}\left(t\right)<0\end{array}\right.$

In formula: η_A/DThe conversion efficiency of direct current, η is converted to for exchange_D/AThe conversion efficiency of exchange, P it is converted into for direct current_DC T () is the dc bus total load of period t.

Dc bus total load retrains:

P_DC(t)+P_PV(t)+P_w(t)=P_DC-load(t)+P_B(t)

P_PVT () is photovoltaic generation power, P_wT () is wind-power electricity generation power, P_DC-loadT () is DC load, P_BT () is storage Energy battery is at the power of period t, P during charging_B(t) ＞ 0, P during electric discharge_B(t) ＜ 0.

Efficiency for charge-discharge retrains:

$P_D^{max}\&le;P_B\left(t\right)\&le;P_C^{max}$

For maximum charge efficiency,For maximum discharging efficiency, P_BT () is the charge efficiency of period t.

Energy-storage battery state of charge retrains:

E_min≤E_B(t)≤E_max

E_maxFor energy-storage battery maximum electricity, E_minFor the minimum electricity of energy-storage battery, E_min≤E_BT () is the battery shape of period t State.

Battery electric quantity state giant ties:

$\left\{\begin{array}{c}{E}_B\left(t\right)=E\left(0\right)+\underset{t=1}{\overset{i}{\&Sigma;}}E\left(t\right)\\ E\left(t\right)=P_B\left(t\right)T\\ E_{\min }\&le;E\left(0\right)\&le;E_{\max }\end{array}\right.$

E (0) is battery initial quantity of electricity, and E (t) is the accumulation electricity of period t.

Day electricity giant ties:

$\underset{t=1}{\overset{96}{\&Sigma;}}{P}_B\left(t\right)=0$

This formula represents that energy-storage battery day accumulation electricity is 0.

Finally, Optimized model is optimized.

With reference to Fig. 2, its optimization process is:

(1) load is read；

(2) photovoltaic, blower fan prediction and electricity price information are read；

(3) model objective function and constraints are set up；

(4) use branch and bound method that model is optimized；

(5) output optimum results: the charge-discharge electric power of battery and period, the power of AC/DC changeover switch and period.

Claims (4)

1. a micro-capacitance sensor energy storage Optimization Scheduling, to the electricity consumption type including direct current and exchange mixing and AC/DC changeover switch Intelligent micro-grid structure be optimized scheduling, it is characterised in that described dispatching method comprises the following steps:

The load of next day and photovoltaic, wind turbine power generation are predicted by step 1.

Step 2 based on load and photovoltaic, wind turbine power generation predict the outcome and electricity price to the transition status of AC/DC changeover switch with And the charging and discharging state of energy-storage battery sets up Optimized model with total electricity charge minimum.

Optimized model is optimized by step 3.

A kind of micro-capacitance sensor energy storage Optimization Scheduling the most according to claim 1, it is characterised in that: pre-in described step 1 Survey method is neutral net and support vector machine method.

A kind of micro-capacitance sensor energy storage Optimization Scheduling the most according to claim 1, it is characterised in that: excellent in described step 2 The object function changed is whole day total electricity charge constraints

$Min\underset{t=1}{\overset{96}{\&Sigma;}}C\left(t\right)P\left(t\right)T$

In formula, t is period conversion, and 96 is whole day period sum, the value that whole day period sum can also be suitable for for other.C (t) is The tou power price of period t.P (t) is the period t output power obtained from major network, and T is unit Period Length, is here 0.25h；

Constraints includes:

General power Constraints of Equilibrium

P (t)=P_AC-load(t)+P_AC-DC(t)

In formula, P_AC-loadT () is the AC load of period t, P_AC-DCT () is the power of AC/DC changeover switch；

AC/DC changeover switch efficiency constraints

$P_{AC-DC}\left(t\right)=\left\{\begin{array}{ccc}{\mathrm{\&eta;}}_{A/D}{P}_{DC}\left(t\right)& if& P_{DC}\left(t\right)>0\\ 0& if& P_{DC}\left(t\right)=0\\ {\mathrm{\&eta;}}_{D/A}{P}_{DC}\left(t\right)& if& P_{DC}\left(t\right)<0\end{array}\right.$

In formula, η_A/DThe conversion efficiency of direct current, η is converted to for exchange_D/AThe conversion efficiency of exchange, P it is converted into for direct current_DC(t) be The dc bus total load of period t；

Dc bus total load retrains

P_DC(t)+P_PV(t)+P_w(t)=P_DC-load(t)+P_B(t)

In formula, P_PVT () is photovoltaic generation power, P_wT () is wind-power electricity generation power, P_DC-loadT () is DC load, P_BT () is storage Energy battery is at the power of period t, P during charging_B(t) ＞ 0, P during electric discharge_B(t)<0；

Efficiency for charge-discharge retrains

$P_D^{max}\&le;P_B\left(t\right)\&le;P_C^{max}$

In formula,For maximum charge efficiency,For maximum discharging efficiency, P_BT () is the charge efficiency of period t；

Energy-storage battery state of charge retrains

E_min≤E_B(t)≤E_max

In formula, E_maxFor energy-storage battery maximum electricity, E_minFor the minimum electricity of energy-storage battery, E_min≤E_BT () is the battery shape of period t State；

Battery electric quantity state giant ties

$\left\{\begin{array}{c}{E}_B\left(t\right)=E\left(0\right)+\underset{t=1}{\overset{i}{\mathrm{\&Sigma;}}}E\left(t\right)\\ E\left(t\right)=P_B\left(t\right)T\\ E_{\min }\&le;E\left(0\right)\&le;E_{\max }\end{array}\right.$

In formula, E (0) is battery initial quantity of electricity, and E (t) is the accumulation electricity of period t；

Day electricity giant ties

$\underset{t=1}{\overset{96}{\&Sigma;}}{P}_B\left(t\right)=0$

This formula represents that energy-storage battery day accumulation electricity is 0.

A kind of micro-capacitance sensor energy storage Optimization Scheduling the most according to claim 1, it is characterised in that in described step 3, it is excellent Change process is:

(1) load, photovoltaic and blower fan prediction and electricity price information are read；

(2) model objective function and constraints are set up；

(3) use branch and bound method that model is optimized；

(4) output optimum results: the charge-discharge electric power of battery and period；The power of AC/DC changeover switch and period.