A framework to expedite joint energy-reserve payment cost minimization using a custom-designed method based on Mixed Integer Genetic Algorithm

Hamian, Melika; Darvishan, Ayda; Hosseinzadeh, Mehdi; Lariche, Milad Janghorban; Ghadimi, Noradin; Nouri, Alireza

doi:10.1016/j.engappai.2018.03.022

Cited by 284 publications

(81 citation statements)

References 20 publications

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“…Multitude researches have investigated the microgrid operation with various [4][5][6] distributed generations (DGs) Optimized coordinated power dispatch approach for a microgrid (MG) scheduling 7 Optimal method with high robustness for MG 8 Stochastic planning scheme for 24 h scheduling for an MG 9 Optimization method for reduction of whole expenditure in an MG 8 Multiobjective optimization methods by assuming expenditure, emission, etc 10,11 Multiobjective optimization for economic dispatch for microgrid with reliability 12 Management method for renewable sources 13 Hybrid-integer nonlinear approach for MG scheduling for achieving upper photovoltaic power 14 Smart power management of microgrid using artificial intelligence methods 15 Cost-effective combined heat and power dispatch with heat/energy reliance features [16][17][18][19] Demand response (DR) problem that can optimize the scheduling using optimization algorithm 20 DR program on stochastic power provided to big users by green resources 21,22 Other models [23][24][25] The considered MO optimization is handled in this work using an epsilon limitation approach. A fuzzy satisfying method is utilized for opting the best conciliation answer, and DRP is exerted to decrease the operation expenditure.…”

Section: Methods Referencementioning

confidence: 99%

New optimization method based on energy management in microgrids based on energy storage systems and combined heat and power

Zeng

Berti

2019

Computational Intelligence

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Microgrids can be assumed as a solution model for green energy sources, energy storage systems, and combined heat and power (CHP) systems. In this work, the cost and emission minimization based on a demand response (DR) program is considered an optimization problem. To solve the mentioned problem a new multiobjective optimization algorithm (improved particle swarm optimization) is proposed based on a fuzzy mechanism to select the optimal value. The microgrid system includes two CHP units, fuel cell and battery systems, and the heat buffer tank. In this problem, two different feasible operating regions have been assumed in CHPs. Accordingly, to decrease the operational cost, time‐of‐use, and real‐time pricing DR programs have been simulated, and the impacts of the mentioned models are evaluated overload profiles. The effectiveness of proposed models has been applied on different cases studies by different scenarios. The proposed model solved the DR program, time of use‐DR and real‐time pricing‐DR problems. The proposed model could reduce the cost about 10%.

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Section: Methods Referencementioning

confidence: 99%

New optimization method based on energy management in microgrids based on energy storage systems and combined heat and power

Zeng

Berti

2019

Computational Intelligence

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“…60,61 The main objective of the task scheduling is to optimize the completion time and the total monetary cost for users, simultaneously. 60,61 The main objective of the task scheduling is to optimize the completion time and the total monetary cost for users, simultaneously.…”

Section: Cost Functionmentioning

confidence: 99%

A fuzzy logic‐based method for solving the scheduling problem in the cloud environments using a non‐dominated sorted algorithm

Wakil

Badfar

Dehghani

et al. 2019

Concurrency and Computation

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Cloud computing as a new model of delivering IT services on the Internet has attained high attention recently. In this new paradigm, efficient service management causes the high quality of provided services. Scheduling as one of the most important duties of service management is a key problem in cloud computing that affects the total system performance. In most cases, the meta-heuristic methods are used for optimizing the scheduling issues instead of traditional methods. One of the influential evolutionary algorithms for optimizing the complicated problems is a non-dominated sorting particle swarm optimization (NSPSO) technique. In this paper, we propose a meta-heuristic technique using the NSPSO model for decreasing total cost and consumed total time. Furthermore, fuzzy set theory is applied to select the best solution.Simulation results have indicated that the efficiency of NSPSO is improved. In the many types of experiment, the proposed NSPSO algorithm was appropriate to keep a good spread of solutions and good converge. In addition, the diversity preserving mechanism applied in NSPSO has improvement against the other two investigated algorithms. KEYWORDScloud computing, multi-objective optimization, particle swarm optimization algorithm, task scheduling INTRODUCTIONCloud computing as an innovative distributed computing can provide dynamic resources, which are highly virtualized. 1,2 In addition, cloud providers deliver their services (such as software, hardware, platform, and storage) over the Internet. 3,4 Therefore, there are no shrink-wrapped boxes containing discs or hardware for users to buy. The cloud providers typically charge monthly recurring fees based on usage. 5-7 Many benefits, including the cost reduction and flexibility, make cloud computing to be superior technology in computing. 8 Due to the numerous benefits of cloud computing, many users have been attracted to use cloud-based services. 9,10 There are some issues in the cloud computing that must be addressed, such as resource discovery, 11,12 trust evaluation, 13-15 task, 16-18 resource allocation, 19-21 and so on. 22 Among them, task scheduling has a primary role in reaching the efficiency of distributed computing systems such as clouds and grids. [23][24][25] In the cloud systems, a task scheduler allocates the submitted tasks to proper resources for performing. 26-28 From the creation of cloud computing, task scheduling has gained significant attention, and many researchers have investigated many methods based on artificial intelligence-based algorithms to solve it. 29 It is responsible for finding potential cloud resources for satisfying the users' requests in the cloud environment. The main aim of task scheduler is decreasing the total cost, completion time and the computational complexity. 30,31 However, most of the existing research has focused on a single objective task scheduling in cloud systems. However, in real cases, more than one factor should be considered. 29 Since task scheduling is an NP-hard issue, many meta-heuristic algo...

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“…18,19 It is considered as one of the advantages of GA. 18,19 It is considered as one of the advantages of GA.…”

Section: Introductionmentioning

confidence: 99%

“…In such systems, because of the repeated relations on various sites, the query optimization is very challenging. 18,19 It is considered as one of the advantages of GA. Therefore, in this paper, an Artificial Bee Colony Algorithm based on Genetic Operators (ABC-GO) is proposed to find a solution to join the query optimization problems in the distributed database systems.…”

mentioning

confidence: 99%

Join query optimization in the distributed database system using an artificial bee colony algorithm and genetic operators

Panahi

Navimipour

2019

Concurrency and Computation

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As the main factor in the distributed database systems, query optimization is aimed at finding an optimal execution plan to reduce the runtime. In such systems, because of the repeated relations on various sites, the query optimization is very challenging. Moreover, the query optimization issue with large-scale distributed databases is an NP-hard problem. Therefore, in this paper, an Artificial Bee Colony Algorithm based on Genetic Operators (ABC-GO) is proposed to find a solution to join the query optimization problems in the distributed database systems. The ABC algorithm has the global-local search capabilities and genetic operators to create new candidate solutions for improving the performance of the ABC algorithm. The obtained results have shown that the cost of the query evaluation is minimized and the quality of Top-K query plans is improved for a given distributed query. Moreover, this method decreases the overhead.However, it needs a longer execution time. KEYWORDSartificial bee colony optimization, distributed database, genetic operator, query plan optimization INTRODUCTIONDue to the rapid growth of data in current systems, 1,2 a set of the interrelated databases, known as a distributed database, is stored on multiple computers to improve performance, reliability, accessibility, and modularity in such cases compared to the conventional centralized database system. 3-7 In today's world, every user of the system, whether an employee or a customer, needs access to the company's databases. The data in distributed databases may be repeated at different sites according to a distributed allocation plan. 8-10 As a result, an association among different sites is needed to create answers to a user query. 11 The query optimization is a critical subject for the distributed database management system. Therefore, the poor performance of database for answering the query is the result of poor selection of a query execution plan. 12The query is a statement or a group of statements that performs some simple database operations such as deletion, updating, and retrieving data. It plays a critical role in data management and retrieval. Commonly, the distributed queries are more complicated than centralized queries due to data distribution over different sites. 13 Consequently, the impact of efficient query processing is increasing in a large number of applications.A common method for identifying the Top-k objects is that all database objects are scored on some function. Therefore, the DQP (distributed query process) strategy aims to generate the query processing plans that reduce the amount of data transmission between sites and, as a result, reduces the response time of the distributed query. The query optimization is a critical subject for database management systems, 14 whose purpose is to define an appropriate execution plan for the user's query. 15 The cost of each plan should be produced due to more than one plan for such a query, which is significantly dependent on the amount of participation and data transfer betwe...

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A framework to expedite joint energy-reserve payment cost minimization using a custom-designed method based on Mixed Integer Genetic Algorithm

Cited by 284 publications

References 20 publications

New optimization method based on energy management in microgrids based on energy storage systems and combined heat and power

New optimization method based on energy management in microgrids based on energy storage systems and combined heat and power

A fuzzy logic‐based method for solving the scheduling problem in the cloud environments using a non‐dominated sorted algorithm

Join query optimization in the distributed database system using an artificial bee colony algorithm and genetic operators

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