Optimal Energy Control Of A Rubber Tyred Gantry Crane With Potential Energy Recovery

ThesisSeaports and rail terminals use Rubber Tired Gantry (RTG) to organise container aisles, loading, and moving cargo-containers. They operate as the link between the cranes and the means of transportation by road, rail, or sea connections. The handling of containers and the motion of RTG cranes are powered by electric motors, which are powered mainly by two sources, namely the standalone diesel powered and the grid connection from the local electrical network. Looking at an operation efficiency and energy management’s point of view, the main problem occurring in RTG crane system, is that the majority of electrical energy or fuel consumed comes from hoisting containers with different weights to several heights; additionally, the peak demand increases when the RTG crane handles heavier containers. Furthermore, during the lowering phase of the containers, potential power is dissipated as heat through resistor banks, used for braking purposes. To solve these problems, this work developed optimal energy management models of the proposed hybrid diesel/battery and hybrid grid/battery RTG cranes, respectively, to minimize the total electricity costs. The first model was based on the optimal energy management model for a RTG crane, supplied by a hybrid diesel generator/battery system. The aim of the model is to reduce the energy cost spending and CO2 emission, by minimizing the amount of fuel consumed by the diesel generator and maximizing the potential energy recovered through the regenerative braking during the container lowering phase. As a case study, a 40 tonne RTG crane, operating in South Africa, has been selected. The demand profile, size of the diesel generator, as well as the battery storage system are used as input for the developed model. Simulations, for a complete RTG handling cycle, have been conducted, to evaluate the techno-economic performances of the developed model use, to optimally dispatch the power flow in the system during the different phases of operation. As compared to the baseline case, where the diesel generator is used alone to accommodate the same demand, the simulation results for the selected day of operation have shown that, using the proposed model, a 40.6% reduction in the operation cost, as well as CO2 emission, is achievable in the case of the proposed system, without energy recovery; 82.17% is achievable in the case where the energy recovery is included. Looking further into the stochastic nature of the demand, the analysis of a year of operation has revealed that 76.04% in operation costs may be potentially saved, using the proposed system. The result of the true payback period analysis has shown that the overall investment cost may be recovered in 1.36 years. Additionally, it may be observed, from the results, that the peak power demand on the diesel generator, has been reduced. This may assist in reducing the power rating and the initial cost of the diesel generator. The second model was based on the optimal energy management model for the grid powered electric RTG, with a battery storage system. The aim of the model is to reduce the operation cost, by minimizing the component linked to the maximum demand charges from the grid, as well as the component linked to the Time of Use (ToU) pricing structure. As a case study, a RTG crane operating in South Africa, has been selected. The load profile, the battery storage system, ToU tariff, as well as the maximum demand charges, are used as input for the developed model. Simulations, for a complete RTG handling cycle, have been conducted, to evaluate the techno-economic performances of the developed model, used to optimally dispatch the power flow in the system during the different phases of operation. Three main configurations have been simulated as energy sources for the RTG crane, namely, the exclusive supply from the grid, grid/battery hybrid system without energy recovery during the lowering phase and grid/battery hybrid with energy recovery, during the lowering phase. As compared to the baseline, the simulation results have shown that, using the proposed model, a possible 50.36%, 60.6% and 64.4% cost reduction, per full handing cycle, is possible in off-peak standard and peak pricing period, for the selected winter day. Table 2 further shows that the maximum demand charges, for a full load in any of the pricing periods is USD 2 639.39, when the baseline is considered. This may be reduced by 45.20%, to USD 1446.24, when the RTG crane is supplied by the optimally controlled hybrid system, with energy recovery. The yearly analysis has revealed that the break-even point of the proposed optimally controlled hybrid grid/Battery, with energy recovery, suppling the RTG crane, may take place after 1.36 years, corresponding to USD 121 900. For the 20 years’ project lifetime, the computed lifecycle, in the case of the proposed optimally controlled grid/Battery with energy recovery, is USD 1 425 000. However, when solely the baseline is considered, the projected lifecycle cost is USD 5 384 000. There is a potential cost saving of USD 3 950 000, corresponding to 73.53%. The result of the true payback period analysis has shown that the overall investment cost may be recovered in 1.716 years. Additionally, using the proposed system, the peak power demand on the grid has been reduced. This may assist in reducing the size of the inverter by more than 50%, which may lower the initial cost of the system. These results further demonstrate that, using the proposed optimal control models, the peak power demands on the grid, or on the DG, have been reduced. This may assist in reducing the size of the inverter, or of the DG by more than 50%, which may lower the initial cost of the system. This, in turn, serves as a greater incentive for seaports and rail terminals to implement these energy management strategies, reducing their operating cost and increasing their benefits

Stochastic optimal energy management system for RTG cranes network using genetic algorithm and ensemble forecasts

In low voltage networks, Energy Storage Systems (ESSs) play a significant role in increasing energy cost savings, peak reduction and energy efficiency whilst reinforcing the electrical network infrastructure. This paper presents a stochastic optimal management system based on a Genetic Algorithm (GA) for the control of an ESS equipped with a network of electrified Rubber Tyre Gantry (RTG) cranes. The stochastic management system aims to improve the reliability and economic performance, for given ESS parameters, of a network of cranes by taking into account the uncertainty in the RTGs electrical demand. A specific case study is presented using real operational data of the RTGs netwrok in the Port of Felixstowe, UK, and the results of the stochastic control system is compared to a standard set-point controller. In this paper, two forecast data sets with different levels of accuracy are used to investigate the impact of the crane demand forecast error in the proposed ESS control system. The results of the proposed control strategies indicate that the stochastic management system successfully increases the electric energy cost savings, the peak demand reductions and successfully outperforms a comparable set-point controller

Analysis of energy usage for RTG cranes

The purpose of this paper is to study and analyse the energy that is used by the various motors of a crane of the Rubber Tyred Gantry type. For this reason a single Rubber Tyred Gantry (RTG) crane has been instrumented at port of Felixstowe and data has been collected during normal operation for eight days. This data has been analysed in terms of active and idle modes and also in terms of energy usage by the various motors. From this analysis it is possible to determine that on average about half of the energy consumed is potentially recoverable. It is also estimated that the recovery of this proportion of energy could lead to savings of 32,600 L of fuel and 8100 tonnes of CO2 per year at Port of Felixstowe

Power management system for RTG crane using fuzzy logic controller

In this research, there are two major objectives have been investigated for a Rubber Tyred Gantry (RTG) crane system: energy consumption reduction and decrease the stress on the primary source. These objectives can be met by using an advance control system that reads the status of the crane and outputs a power reference value which is fed to the storage device. This paper presents Fuzzy Logic Controller (FLC) approach to maximise the potential benefits of adding energy storage units to RTG cranes. In this work, FLC is described and simulated, with the results analysed to highlight the behaviour of the storage in association with the specific control system. An actual collected data at the Port of Felixstowe, UK has been used to develop the crane and ESS models and test the proposed control strategies in this paper. Furthermore, a comparison analysis between the FLC and the standard control system (PI) for RTG crane and ESS applications will be presented with respect to energy consumption, fuel saving and the control impact on the energy device. The simulation results of the FLC control strategy for the collected data shows that it successfully increases the energy savings by 32% and outperforms the PI controller by 26%

Future Greener Seaports:A Review of New Infrastructure, Challenges, and Energy Efficiency Measures

Recently, the application of renewable energy sources (RESs) for power distribution systems is growing immensely. This advancement brings several advantages, such as energy sustainability and reliability, easier maintenance, cost-effective energy sources, and ecofriendly. The application of RESs in maritime systems such as port microgrids massively improves energy efficiency and reduces the utilization of fossil fuels, which is a serious threat to the environment. Accordingly, ports are receiving several initiatives to improve their energy efficiency by deploying different types of RESs based on the power electronic converters. This paper conducts a systematic review to provide cutting-edge state-of-the-art on the modern electrification and infrastructure of seaports taking into account some challenges such as the environmental aspects, energy efficiency enhancement, renewable energy integration, and legislative and regulatory requirements. Moreover, the technological methods, including electrifications, digitalization, onshore power supply applications, and energy storage systems of ports, are addressed. Furthermore, details of some operational strategies such as energy-aware operations and peak-shaving are delivered. Besides, the infrastructure scheme to enhance the energy efficiency of modern ports, including port microgrids and seaport smart microgrids are delivered. Finally, the applications of nascent technologies in seaports are presented

Agent-based model for sustainable equipment expansion with co2 reduction of a container port

Conserving port environment is gaining attention, seeing local port authorities beginning to establish green policies as a normative direction into container port expansion. However, there are conflicts among port authorities, port planners, port stakeholders in converting port equipment with carbon reducing technology. This attributes to the absence of electrification approach in port expansion process. This research aims to propose a sustainable equipment expansion approach by an agent-based model (ABM) to quantify carbon-reducing equipment profile that complies with an emission reduction standard (ERS). The approach simulates the port sustainability transition from port agent interaction that determines the expansion design approach. A combination of fundamental port expansion theories and an electrification logic are developed to simulate the carbon-reducing expansion profile. It is to meet the required CO2 emission reduction standard while not forfeiting financial performance. An agent-based simulator (NETLOGO) is programmed to simulate port sustainability transition and the sustainable expansion profile. The results of PTP case study indicate that it is able to electrify all equipments by 2043. Results also indicate a viable green policy implemented at 4.5% yearly CO2 reduction starting at 2024 while meeting the required port capacity and financial performance. Analysis infers the futility of imposing high emission reduction percentage and the execution of more conversions at higher throughput demand phase. In conclusion, ABM model can be a decision-making support system for the port community to execute appropriate emission reduction standard percentage and time to realise the green port concept

Past, state-of-the-art and future of intralogistics in relation to megatrends

Nakon kratkog pregleda istorije intralogistike, ovaj rad izučava pogled na navedene tehnologije. One su u vezi sa t.z.v. 'megatrendovima' kao što su globalizacija, urbanizacija, demografske i klimatske promene, za koje se očekuje da donesu globalne promene u nekoliko sledećih decenija i koje će najverovatnije odrediti buduću ulogu intralogistike i fokus istraživanja u ovoj oblasti.After briefly reviewing the history of intralogistics, this paper examines the outlook for the technologies concerned. This is related to the so-called 'megatrends', such as globalisation, urbanisation, demographic shifts and climate change, which are expected to bring about major global transformations over the next few decades, and which are also likely to determine the future functions of intralogistics and the focus of research in the field

Past, state-of-the-art and future of intralogistics in relation to megatrends

Nakon kratkog pregleda istorije intralogistike, ovaj rad izučava pogled na navedene tehnologije. One su u vezi sa t.z.v. 'megatrendovima' kao što su globalizacija, urbanizacija, demografske i klimatske promene, za koje se očekuje da donesu globalne promene u nekoliko sledećih decenija i koje će najverovatnije odrediti buduću ulogu intralogistike i fokus istraživanja u ovoj oblasti.After briefly reviewing the history of intralogistics, this paper examines the outlook for the technologies concerned. This is related to the so-called 'megatrends', such as globalisation, urbanisation, demographic shifts and climate change, which are expected to bring about major global transformations over the next few decades, and which are also likely to determine the future functions of intralogistics and the focus of research in the field

Optimal energy controllers of energy storage systems based on load forecasting for RTG cranes network

Given the increased international trading in ports around the world, there are significant challenges facing ports such as rising energy consumption and greenhouse emissions. The electrification of Rubber Tyred Gantry (RTG) cranes is one approach used to reduce gas emissions and fuel costs at port, but has also increased the electrical demand across the electrical distribution network. This will force port operators to reinforce the low voltage network to meet this increased demand and remain within the operating constraints. An energy storage system is one potential solution to increase the energy efficiency of the low voltage distribution networks whilst avoiding expensive reinforcement of the power system. This thesis aims to highlight and address the peak demand problem in the network of electrified RTG cranes and attempts to reduce peak demand and electricity costs by optimality controlling the energy storage system by utilising load forecasts. Since there is currently lack of understanding of the volatile demand behaviour, the research begins by investigating the unique characteristics of the electrical demand of the RTG crane. This understanding is a vital tool to develop an accurate forecast model and maximise the benefits of using an energy storage system through a control system. Several short-term load forecast models have been developed based on the ARIMAX and ANN models to predict accurate day-ahead electrical R TG crane demand. The forecast results show that the highly volatile demand behaviour creates a substantial prediction challenge compared to normal residential low voltage network demand. This thesis then presents the significance of forecasting the crane demand to improve the energy performance of an electrical distribution network with an ESS by employing several optimal controllers. The novel optimal control algorithms considered for the network of RTG cranes are split into: a Model Predictive Controller (MPC) with rolling forecast system and a Stochastic Model Predictive Controller (SMPC) based on a stochastic prediction demand model. The proposed MPC and SMPC control models are compared to an optimal controller based on a fixed load forecast profile and a common and standard set-point controller. Results show that the optimal controllers based on a load forecast have improved the storage device performance for the peak reduction and cost savings compared to the traditional control algorithm. Further improvements are then presented for the receding horizon controllers, MPC and SMPC, which better treat the volatility of the crane demand and the uncertainty in the forecasts. Furthermore, an economic analysis of the results for different ESS location scenarios is presented to assess their viability