Accommodating discharging power with consideration of both EVs and ESs as commodity based on a two-level genetic algorithm
The emerging and booming of electric vehicles (EVs) and energy storages (ESs) endow power systems extra flexibility thanks to their ES capability. The charging and discharging activities of these facilities can be dispersed to perform demand response and benefit power grid operations. However, synch...
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| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
2021
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/146583 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | The emerging and booming of electric vehicles (EVs) and energy storages (ESs) endow power systems extra flexibility thanks to their ES capability. The charging and discharging activities of these facilities can be dispersed to perform demand response and benefit power grid operations. However, synchronous discharging from massive EVs and ESs may impose a huge power supply impact to potentially reshape the existing power markets. Unfortunately, this impact is always ignored by traditional research. To address the above-mentioned issues, discharging power from EVs and ESs is regarded as a kind of commodity in this paper. On such a basis, a pricing policy, where prices for discharging power poured into the power market and the user-side loads are regulated, is applied. The regulation strategy simultaneously incorporates the considerations of the system load condition, maximum power limit, aggregated discharging power from both the EVs and the ESs, as well as the user-side load in a fair manner. Besides, the battery degradation of EVs and EVs has also been considered. Furthermore, the price regulation obeys a hierarchical optimization procedure in which the operator acts as the leader to maximize its revenue, while the end appliances act as followers individually balancing their cost bill and comfort level. Also, the pricing policy is tested on a two-stage hierarchical market with a Genetic Algorithm-based hybrid algorithm. The outcome demonstrates that a prominent performance can be achieved in load shaping and economic benefit via the policy. |
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