Stochastic gradient-based fast distributed multi-energy management for an industrial park with temporally-coupled constraints

Contemporary industrial parks are challenged by the growing concerns about high cost and low efficiency of energy supply. Moreover, in the case of uncertain supply/demand, how to mobilize delay-tolerant elastic loads and compensate real-time inelastic loads to match multi-energy generation/storage a...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Applied energy Ročník 317; s. 119107
Hlavní autoři: Zhu, Dafeng, Yang, Bo, Ma, Chengbin, Wang, Zhaojian, Zhu, Shanying, Ma, Kai, Guan, Xinping
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.07.2022
Témata:
algorithms
electricity
energy
energy costs
Fast distributed algorithm
Multi-energy industrial park
Peak loads shifting
prices
Stochastic gradient
Two-timescale optimization
Peak loads shifting
Multi-energy industrial park
Two-timescale optimization
Stochastic gradient
Fast distributed algorithm
ISSN:0306-2619, 1872-9118
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Contemporary industrial parks are challenged by the growing concerns about high cost and low efficiency of energy supply. Moreover, in the case of uncertain supply/demand, how to mobilize delay-tolerant elastic loads and compensate real-time inelastic loads to match multi-energy generation/storage and minimize energy cost is a key issue. Since energy management is hardly to be implemented offline without knowing statistical information of random variables, this paper presents a systematic online energy cost minimization framework to fulfill the complementary utilization of multi-energy with time-varying generation, demand and price. Specifically to achieve charging/discharging constraints due to storage and short-term energy balancing, a fast distributed algorithm based on stochastic gradient with two-timescale implementation is proposed to ensure online implementation. To reduce the peak loads, an incentive mechanism is implemented by estimating users’ willingness to shift. Analytical results on parameter setting are also given to guarantee feasibility and optimality of the proposed design. Numerical results show that when the bid–ask spread of electricity is small enough, the proposed algorithm can achieve the close-to-optimal cost asymptotically. •A systematic online optimization framework ensuring provable performance for multi-energy system management is presented.•A method is proposed for estimating users’ willingness to shift inelastic loads via public data.•The energy storage balance and real-time supply–demand balance can be achieved by two-timescale optimization.•Fast distributed method is proposed to deal with temporally-coupled constraints.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0306-2619
1872-9118
DOI:10.1016/j.apenergy.2022.119107