Accommodating Discharging Power With Consideration of Both EVs and ESs as Commodity Based on a Two-Level Genetic Algorithm
Author(s) -
Tian Mao,
Baorong Zhou,
Xin Zhang
Publication year - 2019
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2019.2893773
Subject(s) - flexibility (engineering) , computer science , revenue , demand response , genetic algorithm , power (physics) , electric power system , battery (electricity) , base load power plant , commodity , automotive engineering , reliability engineering , electricity , business , economics , electrical engineering , engineering , finance , physics , management , quantum mechanics , machine learning
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.
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom