在本論文中我們著重的部分在用戶端的調配用電情形,
在用戶端的用電排程問題,主要可以分成兩大項,一個是日
前預先排程的問題,另一個是即時排程的問題,日前預先排
程中考慮到如果已知使用者的用電資訊,將如何根據已知的
電價與用電資訊做排程,在即時排程中,我們就不假設已知
使用者的用電資訊,然而我們可以對這些用電需求做延遲或
直接配電的控制,並對這些用電需求做即時的調整。我們將
這兩種排程應用在家庭用電的系統上,並提出相對應的演算
法來解決上述的排程問題,並將我們的系統推廣到整個社區
的使用,並定義出在社區裡索價的相對公平性。

This work examines the scheduling and adaptive re-scheduling of energy usage in in-home
applications. And the work can expand to the specific power usage region such as in home
case, in company or in community. In this work, the discussion can be divided into two
parts. First, we try to formulate the practical power scheduling issue under the assumption
that we know the scheduling information in advance. To be more precise, we know
the day ahead information about power usage and the cost information, and we try to rearrange
those power usage jobs optimally. We proposed an algorithm to schedule those
jobs which can be predicted from history data. From simulation result, we claim that our
proposed CREPES(Convex Relaxation Based Energy Pre-Scheduling) algorithm is quasioptimal.
When we apply our algorithm in the scenario of community. The fairness occurs.
Since if we try to schedule our job greedily for every member in the community, the optimality
for the community can’t be achieved. We can achieve the optimality if all the members in
the community try to cooperate. This will result in the deviation of optimality for individual
from the whole community. Therefore, we also introduce the fairness index in the off-line
scheduling case, which is a index to judge the deviation from the performance of individual
scheduling from cooperation scheduling, which motivates us to minmax the fairness index to
confirm our system costs each user fairly. In the second part of our work we try to extend
our system with PHEVs, that is, we take battery of PHEVs into consideration. And we
also permit our job to be executed with tolerable delay. We try to take an online action for
i

the scheduling system. The resulting problem is a stochastic dynamic programming for cost
optimization problem. The standard way to solve this kind of problem is Lypunov optimization
procedure. In the online scheduling system, in some case we can find that the electricity
energy is relatively cheap at that time and then who can buy the power becomes an issue.
That is, what is the most fair scheduling way in the online scheduling system becomes a
problem we concern. This motivates us to introduce the concept of proportional fairness
in our online scheduling system, which is a standard way to allocate resource in wireless
communication system.

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