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...
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| Vydáno v: | Applied energy Ročník 317; s. 119107 |
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| Hlavní autoři: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier Ltd
01.07.2022
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| Témata: | |
| ISSN: | 0306-2619, 1872-9118 |
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| Abstract | 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. |
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| AbstractList | 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. 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. |
| ArticleNumber | 119107 |
| Author | Zhu, Shanying Ma, Chengbin Yang, Bo Ma, Kai Guan, Xinping Zhu, Dafeng Wang, Zhaojian |
| Author_xml | – sequence: 1 givenname: Dafeng surname: Zhu fullname: Zhu, Dafeng organization: Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China – sequence: 2 givenname: Bo orcidid: 0000-0001-9268-8436 surname: Yang fullname: Yang, Bo email: bo.yang@sjtu.edu.cn organization: Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China – sequence: 3 givenname: Chengbin surname: Ma fullname: Ma, Chengbin organization: Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China – sequence: 4 givenname: Zhaojian surname: Wang fullname: Wang, Zhaojian organization: Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China – sequence: 5 givenname: Shanying surname: Zhu fullname: Zhu, Shanying organization: Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China – sequence: 6 givenname: Kai surname: Ma fullname: Ma, Kai organization: Key Laboratory of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao 066004, China – sequence: 7 givenname: Xinping surname: Guan fullname: Guan, Xinping organization: Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China |
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| Keywords | Peak loads shifting Multi-energy industrial park Two-timescale optimization Stochastic gradient Fast distributed algorithm |
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| SubjectTerms | algorithms electricity energy energy costs Fast distributed algorithm Multi-energy industrial park Peak loads shifting prices Stochastic gradient Two-timescale optimization |
| Title | Stochastic gradient-based fast distributed multi-energy management for an industrial park with temporally-coupled constraints |
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