Energy production cost minimization in a combined heat and power generation systems using cuckoo optimization algorithm

Mehdinejad, Mehdi; Mohammadi‐Ivatloo, Behnam; Dadashzadeh-Bonab, Reza

doi:10.1007/s12053-016-9439-6

Cited by 56 publications

(29 citation statements)

References 23 publications

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“…At the top level, problems can be classified into two types: single‐agent and multi‐agent. The single‐agent problem is solved in a centralized way . For example, Kitamura et al .…”

Section: Literature Reviewmentioning

confidence: 99%

Distributed day‐ahead scheduling in community energy management systems using inter‐community energy trade

Murakami

Okada

Fukuda

et al. 2018

IEEJ Transactions Elec Engng

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A community energy management system (CEMS) stands between energy consumers in a community and energy suppliers, and provides ancillary service to the former. A CEMS performs day-ahead scheduling for its energy conversion equipment and energy purchase planning for the next day to minimize energy consumption and cost under some constraints. In this study, we assume that multiple CEMSs can cooperatively perform the aforementioned; we examine a day-ahead scheduling problem among a group of CEMSs using two key solutions for energy saving: demand response and energy trade. Using the demand response, CEMS can change the energy of consumers to improve energy utilization efficiency. Using the energy trade, CEMSs can trade surplus energy with each other. First, we build an optimization problem for a group of CEMSs. Then, the alternating direction method of multipliers is used to decompose the optimization problem into a distributed optimization problem. Results of numerical experiments show that both the demand response and the energy trade cooperatively affect energy saving of the group. MasahiroOkada (Non-member) received the B.E. and M.E. degrees in electronic and information engineering from Osaka University in 2015 and 2017, respectively. Takuya Fukuda (Student Member) received the B.E. degree in electronic and information engineering from Osaka University in 2016. He is currently working toward the M.S. degree in the Department of Electrical Engineering and Information Systems, Graduate School of Engineering, University of Tokyo. His research interests include optimal control systems for electric vehicle. Shoichi Kitamura (Member) received the B.E. and M.E. degrees in biophysical engineering and the Dr. of Eng. degree in electrical engineering from Osaka University in 2000 and 2002, and 2013, respectively. He joined the Advanced Technology R&D Center, Mitsubishi Electric Corporation, Hyogo, in 2002, where he was engaged in research on the factory energy management system. At present, he is engaged in research on smart grid and smart community-related technologies.

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“…At the top level, problems can be classified into two types: single‐agent and multi‐agent. The single‐agent problem is solved in a centralized way . For example, Kitamura et al .…”

Section: Literature Reviewmentioning

confidence: 99%

Distributed day‐ahead scheduling in community energy management systems using inter‐community energy trade

Murakami

Okada

Fukuda

et al. 2018

IEEJ Transactions Elec Engng

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“…The CSA was used in [27], [28] to solicit the optimal schedule of generation in CHP units considering transmission losses and valve-point effects. A CSA technique utilizing an external penalty function was used in [29] for the CHPED.…”

Section: Conventional Methods Include Lagrangian Relaxation (Lr)mentioning

confidence: 99%

Optimal Combined Heat and Power Economic Dispatch Using Stochastic Fractal Search Algorithm

Alomoush

2020

Journal of Modern Power Systems and Clean Energy

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Combined heat and power (CHP) generation is a valuable scheme for concurrent generation of electrical and thermal energies. The interdependency of power and heat productions in CHP units introduces complications and non-convexities in their modeling and optimization. This paper uses the stochastic fractal search (SFS) optimization technique to treat the highly non-linear CHP economic dispatch (CHPED) problem, where the objective is to minimize the total operation cost of both power and heat from generation units while fulfilling several operation interdependent limits and constraints. The CHPED problem has bounded feasible operation regions and many local minima. The SFS, which is a recent metaheuristic global optimization solver, outranks many current reputable solvers. Handling constraints of the CHPED is achieved by employing external penalty parameters, which penalize infeasible solution during the iterative process. To confirm the strength of this algorithm, it has been tested on two different test systems that are regularly used. The obtained outcomes are compared with former outcomes achieved by many different methods reported in literature of CHPED. The results of this work affirm that the SFS algorithm can achieve improved near-global solution and compare favorably with other commonly used global optimization techniques in terms of the quality of solution, handling of constraints and computation time.

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“…In the years since these pioneering works, the imaginativeness of researchers has continued to flourish. New metaheuristic methods have been applied to the CHP dispatch problem such as cuckoo search [23][24][25][26], invasive weed optimization [27], the artificial immune system algorithm [28], the firefly algorithm [29], krill herd optimization [30], the harmony search algorithm [31][32][33][34], grey wolf optimization [35,36], whale optimization [37], and the hybridizing bat algorithm with artificial bee colony [38]. This is by no means an exhaustive list of metaheuristic approaches investigated.…”

Section: Optimal Combined Heat and Power Dispatchmentioning

confidence: 99%

A Critical Review of the Modeling and Optimization of Combined Heat and Power Dispatch

Kazda¹,

Li²

2020

Processes

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Combined heat and power (CHP) systems are attracting increasing attention for their ability to improve the economics and sustainability of the electricity system. Determining how to best operate these systems is difficult because they can consist of many generating units whose operation is governed by complex nonlinear physics. Mathematical programming is a useful tool to support the operation of CHP systems, and has been the subject of substantial research attention since the early 1990s. This paper critically reviews the modeling and optimization work that has been done on the CHP economic dispatch problem, and the CHP economic and emission dispatch problem. A summary of the common models used for these problems is provided, along with comments on future modeling work that would beneficial to the field. The majority of optimization approaches studied for CHP system operation are metaheuristic algorithms. A discussion of the limitations and benefits of metaheuristic algorithms is given. Finally, a case study optimizing five classic CHP system test instances demonstrates the advantages of the using deterministic global search algorithms over metaheuristic search algorithms.

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Energy production cost minimization in a combined heat and power generation systems using cuckoo optimization algorithm

Cited by 56 publications

References 23 publications

Distributed day‐ahead scheduling in community energy management systems using inter‐community energy trade

Distributed day‐ahead scheduling in community energy management systems using inter‐community energy trade

Optimal Combined Heat and Power Economic Dispatch Using Stochastic Fractal Search Algorithm

A Critical Review of the Modeling and Optimization of Combined Heat and Power Dispatch

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