Stochastic System of Systems Architecture for Adaptive Expansion of Smart Distribution Grids

被引:32
作者
Arasteh, Hamidreza [1 ,2 ]
Vahidinasab, Vahid [1 ]
Sepasian, Mohammad Sadegh [3 ]
Aghaei, Jamshid [3 ,4 ]
机构
[1] Shahid Beheshti Univ, Dept Elect Engn, Abbaspour Sch Engn, Tehran 1983969411, Iran
[2] Niroo Res Inst, Tehran 1468613113, Iran
[3] Shiraz Univ Technol, Dept Elect & Elect Engn, Shiraz 715555313, Iran
[4] Norwegian Univ Sci & Technol, Dept Elect Power Engn, NO-7491 Trondheim, Norway
关键词
Chance constraint (CC); conditional value at risk (CVaR); distribution expansion planning (DEP); demand response (DR) provider; private investor (PI); system of systems (SoS) architecture; ACTIVE DISTRIBUTION NETWORKS; DEMAND RESPONSE; POWER-GENERATION; OPTIMIZATION; MODEL; RECONFIGURATION; RELIABILITY; ALLOCATION;
D O I
10.1109/TII.2018.2808268
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The incorporation of the reconfiguration into the expansion planning of smart distribution networks is addressed in this paper, in which the potential of distributed energy resources and demand response (DR) are modeled. The system of systems (SoS) architecture is employed to model the strategy of a distribution company (DISCO), a private investor (PI), and a DR provider (DRP). The SoS is an efficient modeling architecture to model the behavior of independent and autonomous systems with distinct objective functions who are able to share some data and work together. The aim of the DISCO is to upgrade the system with the optimal cost and reliability, whereas the PI and DRP want to maximize their profit. The DISCO should try to persuade the PI to install DGs (Distributed generations) by offering the guaranteed purchasing prices. Furthermore, the DRP is a market player who can negotiate with the DISCO to sign a contract to sell the purchased DR capacities from the customers. The uncertainties of the DISCO problem is handled by using the chance-constraint method, but the PI and DRP use the conditional value at risk method to model their uncertainties. Finally, to solve the proposed model, the multiobjective optimization algorithm is employed.
引用
收藏
页码:377 / 389
页数:13
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