Analysis of energy usage for RTG cranes

Papaioannou, Vicky; Pietrosanti, Stefano; Holderbaum, William; Becerra, Victor M.; Mayer, Rayner M.

doi:10.1016/j.energy.2017.02.122

Cited by 48 publications

(41 citation statements)

References 11 publications

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“…For example, in order to move more than 4 million TEUs each year, Port of Felixstowe, UK, operates 85 RTG cranes. These cranes work daily up to 18 hours for 350 days a year [16,17]. According to the Port's administration, the annual electricity energy costs for two RTG cranes is around £21,000.…”

Section: Resultsmentioning

confidence: 99%

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: Resultsmentioning

confidence: 99%

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 highest forecast error was +20.58 kWh and the maximum number of errors was around zero errors. According to the RTG crane demand analysis in [29] and the historical load data in this paper, the daily average energy demand is 600 kWh and the hourly demand usage is around 25 kWh, this means that the maximum forecast error value (+20.58 kWh) is very high compared to the average hourly RTG crane demand. Furthermore, the wide range of forecast errors between −12.45 kWh and +20.58 kWh and high forecast error values, as shown in Figure 9, increase the challenges and complexity of controlling the ESS through the MPC controller.…”

Section: Forecast Errormentioning

confidence: 99%

Optimal Energy Management and MPC Strategies for Electrified RTG Cranes with Energy Storage Systems

et al. 2017

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This article presents a study of optimal control strategies for an energy storage system connected to a network of electrified Rubber Tyre Gantry (RTG) cranes. The study aims to design optimal control strategies for the power flows associated with the energy storage device, considering the highly volatile nature of RTG crane demand and difficulties in prediction. Deterministic optimal energy management controller and a Model Predictive Controller (MPC) are proposed as potentially suitable approaches to minimise the electric energy costs associated with the real-time electricity price and maximise the peak demand reduction, under given energy storage system parameters and network specifications. A specific case study is presented in to test the proposed optimal strategies and compares them to a set-point controller. The proposed models used in the study are validated using data collected from an instrumented RTG crane at the Port of Felixstowe, UK and are compared to a standard set-point controller. The results of the proposed control strategies show a significant reduction in the potential electricity costs and peak power demand from the RTG cranes.

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“…The second and third data sets are used as testing data sets. Due to the non-smooth behaviour of the RTG crane load and the lack of seasonality or trends over the time series (as described in the following section) and motivated by the literature [2,4], we assume that the gap in the time series will have negligible effect on the forecast results. In this paper, the testing period with 46 days data set allows us to evaluate the forecast model over three different time periods and test the transferability of the model trained on one crane to other cranes with the same specifications.…”

Section: Data Collection and Analysismentioning

confidence: 99%

“…In the structure of a typical individual artificial neuron, the summation of input signals y i from several synapses is transferred through an activation function F described by (2) for the chosen function sigmoid activation.…”

Section: Annmentioning

confidence: 99%

“…Diesel rubber-tyred gantry (RTG) cranes are usually environmentally and economically inefficient due to their use of more fuel, fuel cost volatility and generation of gas emissions such as carbon dioxide and nitrogen oxides. One way to reduce carbon gas emissions and operation costs is to convert from diesel to electrified RTG cranes [1,2]. Consequently, the electrical power consumption at port substations has been increasing as a result of a rise in the number of electrified RTG cranes which connected to low and medium voltage networks.…”

Section: Introductionmentioning

confidence: 99%

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Day-ahead industrial load forecasting for electric RTG cranes

Alasali

Haben

Becerra

et al. 2018

J. Mod. Power Syst. Clean Energy

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Given the increase in international trading and the significant energy and environmental challenges in ports around the world, there is a need for a greater understanding of the energy demand behaviour at ports. The move towards electrified rubber-tyred gantry (RTG) cranes is expected to reduce gas emissions and increase energy savings compared to diesel RTG cranes but it will increase electrical energy demand. Electrical load forecasting is a key tool for understanding the energy demand which is usually applied to data with strong regularities and seasonal patterns. However, the highly volatile and stochastic behaviour of the RTG crane demand creates a substantial prediction challenge. This paper is one of the first extensive investigations into short term load forecasts for electrified RTG crane demand. Options for model inputs are investigated depending on extensive data and correlation analysis. The effect of estimation accuracy of exogenous variables on the forecast accuracy is investigated as well. The models are tested on two different RTG crane data sets that were collected from the Port of Felixstowe in the UK. The results reveal the effectiveness of the forecast models when the estimation of the number of crane moves and container gross weight are accurate.

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Analysis of energy usage for RTG cranes

Cited by 48 publications

References 11 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

Optimal Energy Management and MPC Strategies for Electrified RTG Cranes with Energy Storage Systems

Day-ahead industrial load forecasting for electric RTG cranes

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