CN106447127A - Energy consumption optimization planning method for smart grid - Google Patents

Abstract

The invention discloses an energy consumption optimization planning method for a smart grid. Aiming at the problem that optimization planning for energy consumption in the smart grid is difficult to realize, the method realizes a dynamic and adaptive adjustment of energy consumption in the smart grid by establishing a peak energy consumption to average load ratio model and partially optimizing the average load ratio.

Description

A kind of energy ezpenditure Method for optimized planning in intelligent grid

Technical field

The present invention relates to intelligent grid field, more particularly to communication network, and optimum theory.

Background technology

Intelligent grid (Smart Grid) is considered to solve the electrical network skill of future generation of existing challenge by academia and industrial quarters Art solution.Follow-on intelligent grid will be equipped with communications facility and real-time measurement facility, improves the reliability of electrical network, steady Qualitative, efficiency, can prevent simultaneously, tackle electrical network inside and outside crisis.Effective process electric power is transported by intelligent grid And the uncertainty in scheduling process, new cleaning fuel is provided, optimizes adapted network system and lift the quality of supply of electric power And reliability, predict more on one's own initiative and manage the unpredictable event in electric grid operating, reduce power networks risk.

The energy-optimised problem of management of intelligent adapted electrical network is mainly concerned with four problems:Advanced measurement technology, intelligently joins Use communication system of power grids network, smart electric grid system safety, energy-optimised management control and optimized algorithm.Advanced measurement technology and intelligence Adapted communication system of power grids network has the brand-new feature in terms of infrastructure construction of intelligent grid, will be for realizing energy-optimised management There is provided hardware foundation guarantee, wherein advanced measurement technology is responsible for perception, collection, is processed related electricity consumption data.

Energy-optimised management, is one of principal character of intelligent grid.The principal contradiction of energy-optimised management is to power Company produces has greatest differences in electric power and all types of user consumption electric power time scale.All types of user determines according to self-demand Consumption habit and electricity consumption load curve, such as factory determine the flow work situation, business premises root according to production procedure and plan Determine room power consumption condition according to company work system and office worker's job placement, domestic consumer determines to use according to life and consumption habit The working condition of electric equipment, City Rail Transit System mainly has for the impact of user's request according to the energy-optimised management of traffic Two classes:1) reduce user power utilization demand；2) user power utilization demand is shifted on time scale.Energy-optimised management strategy mainly has Two big class:1) response policy based on excitation；2) response policy based on price.Wherein warp is used based on the response management of price Important tool in Ji, such as pricing strategy are so that the response of user's request disclosure satisfy that system reliability, energy using effectively Property, the requirement of security, all pricing strategies be based on this assume:The need for electricity of all types of user is had with the electricity price in electricity consumption moment And its close negative incidence.Energy-optimised management refers to the energy management strategies that network system manager and user adopt, and reduces Electrical demand peak average value ratio, improves energy ecology and the reliability of electrical network.Research Requirements side response management problem The service efficiency of the energy will be improved, reduce Peak power use amount, improve the safety and stability of electrical network.In intelligent grid system In, the construction of bidirectional communication network will be helpful to the data of grid management centre collection perception electricity consumption side, for arranging production, passing Defeated, distribution decision provides reliable data to support, improves production and the efficiency of transmission of the energy, intelligent power distribution architectures of communication networks is such as Shown in Fig. 1.

In sum：On the premise of not changing existing adapted electrical network infrastructure construction, energy-optimised management is for joining Embody, with the significance of electrical network, the steady safe operation being to ensure electrical network, improve reliability and stability it is therefore necessary to Set up efficient energy ezpenditure Method for optimized planning, the Operating ettectiveness of lifting intelligent grid.

Content of the invention

The technical problem to be solved is：Than model and carry out peak value by setting up peak value energy consumption with average load With the local optimum of average load ratio, realize the energy ezpenditure dynamic self-adapting regulating power in intelligent grid.

The present invention comprises the following steps by solving the technical scheme that above-mentioned technical problem is adopted, as shown in Figure 2：

A, set up peak value energy consumption and model is compared in average load；

B, carry out the local optimum of energy resource.

In described step A, specially：Each with there being energy ezpenditure scheduling unit per family, and energy ezpenditure scheduling unit with Power line is connected with LAN, obtains peak value energy consumption and with average load ratio isWhereinFor the total load in working time φ,For total load in a day for the user n, L_max=max_φ∈HL_φ For the peak load in a day,For average load, ω_n,aFor the desired value of energy ezpenditure,For one hour Electrical equipment energy ezpenditure, N gathers for user, and E is electrical equipment set,For energy ezpenditure collection Close, a ∈ E identifies for electrical equipment, T is the IDC the longest working time, H gathered for the working time, IDC is data center, such as Fig. 3 Shown.

In described step A, set up income and cost associated response time threshold model λ_f,iT () is the energy arrival rate distributing to IDCi in time slot t server f, m_iT () is in running order in IDCi Number of servers, μ_iAverage service rate for IDCi, F is the number of IDC, and IDC is data center, T_iFarm labourer for IDCi Make the time.

In described step B, set up minimum energy consumption model

s.t.α_n,a<β_n,a

Wherein α_n,aFor the energy ezpenditure initial time of user, β_n,aFor the energy ezpenditure end time of user,For electric Plant capacity consumes instantaneous value.

In described step B it is characterised in that：Set up energy ezpenditure cost minimization model：

s.t.α_n,a<β_n,a

WhereinWithIt is respectivelyLower limit and the upper limit, C_φIt is the energy ezpenditure cost in time φ.

In described step B, for step B, user can select corresponding Optimized model according to specific application scenarios, or right Decomposition step in claim B carries out combined optimization, and obtains optimal solution or the equivalent optimal solution of engineering；Obtain optimal solution or work Journey equivalent optimal solution correction income and cost associated response time threshold model.

Brief description

Fig. 1 intelligent power distribution architectures of communication networks schematic diagram

Energy ezpenditure optimization planning schematic flow sheet in Fig. 2 intelligent grid

Energy transfer schematic diagram in Fig. 3 intelligent grid

Specific embodiment

For reaching above-mentioned purpose, technical scheme is as follows：

The first step, sets up peak value energy consumption and average load than model, specially：Each is with there being energy ezpenditure scheduling single per family Unit, and energy ezpenditure scheduling unit is connected with power line and LAN, obtaining peak value energy consumption with average load ratio isWhereinFor the total load in working time φ,For user n at one day In total load, L_max=max_φ∈HL_φFor the peak load in a day,For average load, ω_n,aDisappear for energy The desired value of consumption,Electrical equipment energy ezpenditure for one hour, N gathers for user, and E is electrical equipment set,For energy ezpenditure set, a ∈ E is electrical equipment mark, and T is the IDC the longest working time, and H is work Make time set, IDC is data center.

Second step, carries out the local optimum of peak value and average load ratio, concretely comprises the following steps：Set up income and associate sound with cost Threshold Model between seasonableλ_f,iT () is to distribute to IDCi's in time slot t server f Energy arrival rate, m_iT () is in running order number of servers in IDCi, μ_iAverage service rate for IDCi, F is The number of IDC, IDC is data center, T_iThe longest working time for IDCi.

3rd step, carries out the local optimum of peak value and average load ratio, specially：Set up minimum energy consumption model

s.t.α_n,a<β_n,a

Wherein α_n,aFor the energy ezpenditure initial time of user, β_n,aFor the energy ezpenditure end time of user,For electric Plant capacity consumes instantaneous value.

4th step, sets up energy ezpenditure cost minimization model

s.t.α_n,a<β_n,a

WhereinWithIt is respectivelyLower limit and the upper limit, C_φIt is the energy ezpenditure cost in time φ.

5th step, user can select the 3rd step or the corresponding Optimized model of the 4th step according to specific application scenarios, or right Step 3 or 4 carries out combined optimization, and obtains optimal solution or the equivalent optimal solution of engineering；Obtain optimal solution or the equivalent optimal solution of engineering Revise income and cost associated response time threshold model

The present invention proposes the energy ezpenditure Method for optimized planning in a kind of intelligent grid, by setting up peak value energy consumption and putting down All duty factor model and local optimums carrying out peak value and average load ratio, the energy ezpenditure realized in intelligent grid is dynamically adaptive Answer regulating power.

Claims (6)

1. the energy ezpenditure Method for optimized planning in a kind of intelligent grid, by set up peak value energy consumption and average load than model and Carry out the local optimum of energy resource, realize the energy ezpenditure dynamic self-adapting regulating power in intelligent grid, walk including following Suddenly：

A, set up peak value energy consumption and model is compared in average load；

B, carry out the local optimum of energy resource.

2. method according to claim 1, for described step A it is characterized in that：It is specially：Each is with there being energy ezpenditure per family Scheduling unit, and energy ezpenditure scheduling unit is connected with power line and LAN, obtaining peak value energy consumption with average load ratio isWhereinFor the total load in working time φ,For user n at one day In total load, L_max=max_φ∈HL_φFor the peak load in a day,For average load, ω_n,aDisappear for energy The desired value of consumption,Electrical equipment energy ezpenditure for one hour, N gathers for user, and E is electrical equipment set,For energy ezpenditure set, a ∈ E is electrical equipment mark, and T is the IDC the longest working time, and H is work Make time set, IDC is data center.

3. method according to claim 1, for described step A it is characterized in that：Set up income and cost associated response time Threshold Modelλ_f,iT () is the energy that data center f distributes to IDCi in time slot t Arrival rate, m_iT () is in running order number of servers in IDCi, μ_iAverage service rate for IDCi, F is IDC's Number, IDC is data center, T_iThe longest working time for IDCi.

4. method according to claim 1, for described step B it is characterized in that：Set up minimum energy consumption model

$\begin{array}{c}\underset{X_n,\&ForAll;n\&Element;N}{\min }\frac{\mathrm{\&mu;}\underset{\mathrm{\&phi;}\&Element;H}{\max }\left(\underset{n\&Element;N}{\&Sigma;}\underset{n\&Element;E}{\&Sigma;}{x}_{n,a}^{\mathrm{\&phi;}}\right)}{\underset{n\&Element;N}{\&Sigma;}\underset{n\&Element;E}{\&Sigma;}{\mathrm{\&omega;}}_{n,a}}\\ \begin{array}{cc}s.t.& {\mathrm{\&alpha;}}_{n,a}<{\mathrm{\&beta;}}_{n,a}\end{array}\\ \underset{\mathrm{\&phi;}={\mathrm{\&alpha;}}_{n,a}}{\overset{{\mathrm{\&beta;}}_{n,a}}{\&Sigma;}}{x}_{\mathrm{\&phi;}}^a={\mathrm{\&omega;}}_{n,a}\\ \underset{\mathrm{\&phi;}\&Element;H}{\&Sigma;}{L}_{\mathrm{\&phi;}}=\underset{n\&Element;N}{\&Sigma;}\underset{a\&Element;E}{\&Sigma;}{\mathrm{\&omega;}}_{n,a}\\ {\mathrm{\&gamma;}}_{n,a}^{\min }\&le;x_{n,a}^{\mathrm{\&phi;}}\&le;{\mathrm{\&gamma;}}_{n,a}^{\max }\end{array},$

Wherein α_n,aFor the energy ezpenditure initial time of user, β_n,aFor the energy ezpenditure end time of user,For electrical equipment Energy ezpenditure instantaneous value.

5. method according to claim 1, for described step B it is characterized in that：Set up energy ezpenditure cost minimization model

$\begin{array}{c}\underset{X_n\&ForAll;n\&Element;N}{\min }\left\{\underset{\mathrm{\&phi;}=1}{\&Sigma;}{C}_{\mathrm{\&phi;}}\left(\underset{n\&Element;N}{\&Sigma;}\underset{n\&Element;E}{\&Sigma;}{x}_{n,a}^{\mathrm{\&phi;}}\right)\right\}\\ \begin{array}{cc}s.t.& {\mathrm{\&alpha;}}_{n,a}<{\mathrm{\&beta;}}_{n,a}\end{array}\\ \underset{\mathrm{\&phi;}={\mathrm{\&alpha;}}_{n,a}}{\overset{{\mathrm{\&beta;}}_{n,a}}{\&Sigma;}}{x}_{\mathrm{\&phi;}}^a={\mathrm{\&omega;}}_{n,a}\\ \underset{\mathrm{\&phi;}\&Element;H}{\&Sigma;}{L}_{\mathrm{\&phi;}}=\underset{n\&Element;N}{\&Sigma;}\underset{a\&Element;E}{\&Sigma;}{\mathrm{\&omega;}}_{n,a}\\ {\mathrm{\&gamma;}}_{n,a}^{\min }\&le;x_{n,a}^{\mathrm{\&phi;}}\&le;{\mathrm{\&gamma;}}_{n,a}^{\max }\end{array},$

WhereinWithIt is respectivelyLower limit and the upper limit, C_φIt is the energy ezpenditure cost in time φ.

6. method according to claim 1, for described step B it is characterized in that：For claim 3 and 4, user can basis Specific application scenarios select corresponding Optimized model, or carry out combined optimization to claim 3 and 4, and obtain optimal solution or The equivalent optimal solution of engineering；Obtain optimal solution or engineering equivalent optimal solution correction income and cost associated response time threshold model.