Abstract
Many recently built residential houses and factories are equipped with facilities for converting energy from green sources, such as solar energy, into electricity. Electricity consumers may input the extra electricity that they do not consume into the smart grid for sale, which is allowed by law in countries such as Japan. To reduce peak-time electricity usage, time-varying pricing schemes are usually adopted in smart grids, for both the electricity sold to consumers and the electricity purchased from consumers. Thanks to the development of cyber-physical systems and advanced technologies for communication and computation, current smart grids are typically networked, and it is possible to integrate information such as weather forecasts into such a networked smart grid. Thus, we can predict future levels of electricity generation (e.g., the energy from solar and wind sources, whose generation is predominantly affected by the weather) with high accuracy using this information and historical data. The key problem for consumers then becomes how to schedule their purchases from and sales to the networked smart grid to maximize their benefits by jointly considering the current storage status, time-varying pricing, and future electricity consumption and generation. This problem is non-trivial and is vitally important for improving smart grid utilization and attracting consumer investment in new energy generation systems, among other purposes. In this paper, we target such a networked smart grid system, in which future electricity generation is predicted with reasonable accuracy based on weather forecasts. We schedule consumers' behaviors using a Markov decision process model to optimize the consumers' net benefits. The results of extensive simulations show that the proposed scheme significantly outperforms the baseline competing scheme.
| Original language | English |
|---|---|
| Article number | 7739991 |
| Pages (from-to) | 2448-2458 |
| Number of pages | 11 |
| Journal | IEEE Access |
| Volume | 5 |
| DOIs | |
| Publication status | Published - 2017 |
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Keywords
- Markov decision process
- scheduling algorithms
- Smart grids
- time-varying systems
ASJC Scopus subject areas
- Computer Science(all)
- Materials Science(all)
- Engineering(all)
Cite this
Markov-decision-process-assisted consumer scheduling in a networked smart grid. / Liu, Zhi; Zhang, Cheng; Dong, Mianxiong; Gu, Bo; Ji, Yusheng; Tanaka, Yoshiaki.
In: IEEE Access, Vol. 5, 7739991, 2017, p. 2448-2458.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Markov-decision-process-assisted consumer scheduling in a networked smart grid
AU - Liu, Zhi
AU - Zhang, Cheng
AU - Dong, Mianxiong
AU - Gu, Bo
AU - Ji, Yusheng
AU - Tanaka, Yoshiaki
PY - 2017
Y1 - 2017
N2 - Many recently built residential houses and factories are equipped with facilities for converting energy from green sources, such as solar energy, into electricity. Electricity consumers may input the extra electricity that they do not consume into the smart grid for sale, which is allowed by law in countries such as Japan. To reduce peak-time electricity usage, time-varying pricing schemes are usually adopted in smart grids, for both the electricity sold to consumers and the electricity purchased from consumers. Thanks to the development of cyber-physical systems and advanced technologies for communication and computation, current smart grids are typically networked, and it is possible to integrate information such as weather forecasts into such a networked smart grid. Thus, we can predict future levels of electricity generation (e.g., the energy from solar and wind sources, whose generation is predominantly affected by the weather) with high accuracy using this information and historical data. The key problem for consumers then becomes how to schedule their purchases from and sales to the networked smart grid to maximize their benefits by jointly considering the current storage status, time-varying pricing, and future electricity consumption and generation. This problem is non-trivial and is vitally important for improving smart grid utilization and attracting consumer investment in new energy generation systems, among other purposes. In this paper, we target such a networked smart grid system, in which future electricity generation is predicted with reasonable accuracy based on weather forecasts. We schedule consumers' behaviors using a Markov decision process model to optimize the consumers' net benefits. The results of extensive simulations show that the proposed scheme significantly outperforms the baseline competing scheme.
AB - Many recently built residential houses and factories are equipped with facilities for converting energy from green sources, such as solar energy, into electricity. Electricity consumers may input the extra electricity that they do not consume into the smart grid for sale, which is allowed by law in countries such as Japan. To reduce peak-time electricity usage, time-varying pricing schemes are usually adopted in smart grids, for both the electricity sold to consumers and the electricity purchased from consumers. Thanks to the development of cyber-physical systems and advanced technologies for communication and computation, current smart grids are typically networked, and it is possible to integrate information such as weather forecasts into such a networked smart grid. Thus, we can predict future levels of electricity generation (e.g., the energy from solar and wind sources, whose generation is predominantly affected by the weather) with high accuracy using this information and historical data. The key problem for consumers then becomes how to schedule their purchases from and sales to the networked smart grid to maximize their benefits by jointly considering the current storage status, time-varying pricing, and future electricity consumption and generation. This problem is non-trivial and is vitally important for improving smart grid utilization and attracting consumer investment in new energy generation systems, among other purposes. In this paper, we target such a networked smart grid system, in which future electricity generation is predicted with reasonable accuracy based on weather forecasts. We schedule consumers' behaviors using a Markov decision process model to optimize the consumers' net benefits. The results of extensive simulations show that the proposed scheme significantly outperforms the baseline competing scheme.
KW - Markov decision process
KW - scheduling algorithms
KW - Smart grids
KW - time-varying systems
UR - http://www.scopus.com/inward/record.url?scp=85017637064&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85017637064&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2016.2620341
DO - 10.1109/ACCESS.2016.2620341
M3 - Article
AN - SCOPUS:85017637064
VL - 5
SP - 2448
EP - 2458
JO - IEEE Access
JF - IEEE Access
SN - 2169-3536
M1 - 7739991
ER -