Energy management systems for a network of electrified cranes with energy storage

Alasali, Feras; Haben, Stephen; Holderbaum, William

doi:10.1016/j.ijepes.2018.10.001

Cited by 38 publications

(84 citation statements)

References 33 publications

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“…This section aims to address the RTG's system topology and introduces the power system structure of a network of cranes. An electrified RTG, as shown in Figure 1, has been retrofitted to be powered by [5,7,9]. This RTG crane has three main types of motors to drive the crane and move containers, as follows:…”

Section: Rtg Crane Network Topologymentioning

confidence: 99%

“…An electrified RTG, as shown in Figure 1, has been retrofitted to be powered by the low voltage network at the Port of Felixstowe. This crane was manufactured by Shanghai Zhenhua Heavy Industries (ZPMC, Shanghai, China) [5,7,9]. This RTG crane has three main types of motors to drive the crane and move containers, as follows:…”

Section: Rtg Crane Network Topologymentioning

confidence: 99%

“…The electricity is then distributed to consumers by step-down voltage transformers and the distribution network. In the first stage, the voltage is stepped down to 33 kV then 11 kV (medium voltage) and finally to the low voltage (LV) network level of 415 V [7,9].…”

Section: Energy Analysis Of Rtg Crane Systemmentioning

confidence: 99%

“…The primary substation is a step-down station from 33 kV to 11 kV, and is split across the medium voltage network for the port. A secondary step-down substation with 11 kV input and 415 V output provides the RTG cranes with the required power through the LV cables and conductor rail, as shown in Figures 3 and 4 [7,9]. The step-down transformer from 11 kV/ 415 V provides the cranes with power through 30 meters of low voltage cables and 217 meters of conductor rail bar.…”

Section: Energy Analysis Of Rtg Crane Systemmentioning

confidence: 99%

“…The literature introducing the potential impact of using energy storage systems (ESS) to support the diesel and electric RTG cranes is beginning to appear [6][7][8][9][10][11]. For example, Flynn et al [10] developed a hybrid energy source model for an RTG crane including a flywheel energy store.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A Comparative Study of Energy Storage Systems and Active Front Ends for Networks of Two Electrified RTG Cranes

Alasali

Ĺuque

Mayer³

et al. 2019

Energies

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The global consumerism trend and the increase in worldwide population is increasing the need to improve the efficiency of marine container transportation. The high operating costs, pollution and noise of the diesel yard equipment is leading sea ports to move towards replacing diesel RTG cranes with electric Rubber Tyre Gantry (RTG) cranes which offer reduced environmental impact and higher energy efficiency. However, ports will require smarter solutions to meet the increased demand on the electrical distribution network due to the electrification of RTGs. This paper aims to highlight the peak demand problem in the two electrical cranes network and attempts to increase the energy saving at ports by using two different technologies: Energy Storage System (ESS) and Active Front End (AFE). This article introduces one of the first extensive investigations into different networks of RTG crane models and compares the benefits of using either AFE or ESS. The proposed RTG crane models and network parameters are validated using data collected at the Port of Felixstowe, UK. The results of the proposed RTG cranes network show a significant peak demand reduction and energy cost saving.

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Section: Rtg Crane Network Topologymentioning

confidence: 99%

Section: Rtg Crane Network Topologymentioning

confidence: 99%

Section: Energy Analysis Of Rtg Crane Systemmentioning

confidence: 99%

Section: Energy Analysis Of Rtg Crane Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Comparative Study of Energy Storage Systems and Active Front Ends for Networks of Two Electrified RTG Cranes

Alasali

Ĺuque

Mayer³

et al. 2019

Energies

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An analysis of optimal power flow strategies for a power network incorporating stochastic renewable energy resources

Alasali

Nusair

Obeidat

et al. 2021

Int Trans Electr Energ Syst

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Summary An optimal power management solution is a potential tool to produce cost‐effective and environmentally friendly power supply using renewable energy sources (RESs) for the electrical power network. Therefore, the article introduces a novel optimization algorithm inspired by the vitality, namely, Manta Ray Foraging Optimization (MRFO), to figure out both multi‐ and single‐objective problems of optimal power flow (OPF) incorporating stochastic RES. The OPF problems are designed by considering four different objective functions: transmission power loss, emission index, fuel operational costs, and voltage deviation. The stochastic and volatile nature of RES increases the complexity of the OPF issue. In this study, a new MRFO algorithm and some modern metaheuristic algorithms were used to settle the issue of OPF, enhance the energy efficiency, and environmental and cost performance of the power network. The test cases, with and without RES, different RES locations on the network, increase in the load, and outages of some transmission lines, are considered by addressing the challenge of the proposed OPF. These cases are tested with bus systems as 30 and 118, and the outcome from the suggested MRFO is compared with six metaheuristic optimization algorithms. Moreover, OPF challenges are successfully settled by the MRFO algorithm and outperform the proposed metaheuristic optimization methods.

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Optimal placement ofFACTSdevices and power‐flow solutions for a power network system integrated with stochastic renewable energy resources using new metaheuristic optimization techniques

et al. 2021

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Summary Flexible AC transmission systems (FACTS) and optimal power‐flow (OPF) solutions play an important role in solving power operation problems. The volatile nature of the power generation profiles from renewable energy sources, solar and wind systems, and determining the optimal locations and sizes of FACTS devices increase the complexity of the OPF problems in modern power network models, such as transmission power loss, power generation operation cost and voltage deviation, as a highly nonlinear‐nonconvex optimization problem. Therefore, this article introduces and employs four new independent, reliable and efficient optimization algorithms inspired by nature and biological nature, namely: Slime Mould Algorithm (SMA), Artificial Ecosystem‐based Optimization (AEO), Marine Predators Algorithm (MPA) and Jellyfish Search (JS), for solving both multi‐ and single‐OPF objective problems for a power network incorporating FACTS and stochastic renewable energy sources. The proposed new metaheuristic optimization techniques are compared to the common and available alternatives in the literature, Particle Swarm Optimization (PSO), Moth Flame Optimization (MFO) and Grey Wolf Optimizer (GWO), using IEEE 30‐bus test system. To consider and address the challenges of the OPF in modern power network models, the proposed optimization techniques tested under different operation cases such as an increasing in the load, with and without FCTAS and renewable energy sources, different renewable energy sources locations on the network. The result showed that the MPA, SMA, JS and AEO algorithms are more effective solvers for the OPF problems cases compared to the PSO, GWO and MFO algorithms. For example, the AEO obtained 0.0844 p.u. in case of minimizing the voltage deviation compared to 0.1155 p.u. for PSO, which means that the AEO algorithm improved the voltage deviation term by 27% compared to the PSO algorithm.

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Energy management systems for a network of electrified cranes with energy storage

Cited by 38 publications

References 33 publications

A Comparative Study of Energy Storage Systems and Active Front Ends for Networks of Two Electrified RTG Cranes

A Comparative Study of Energy Storage Systems and Active Front Ends for Networks of Two Electrified RTG Cranes

An analysis of optimal power flow strategies for a power network incorporating stochastic renewable energy resources

Optimal placement ofFACTSdevices and power‐flow solutions for a power network system integrated with stochastic renewable energy resources using new metaheuristic optimization techniques

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