Decarbonising fishery ports through smart cluster energy systems

The rising energy prices at seaports and fishing industries pose a major challenge because the pace of work and high demand for fish products has increased dra‐matically. This comes at a time of growing international pressure and global moti‐vations to address climate change and reduce carbon emissions in many different sectors of the economy. In the literature review, a few research studies were found to highlight the op‐timal use of power energy in ports, while some studies proposed certain measures that contribute to some extent to reducing energy consumption and carbon emis‐sions. However, there is an absence of a study that discusses the possibility of de‐veloping a holistic energy analysis and management that can be scaled from a site to a community level to achieve economically and environmentally viable benefits to the community. The research study that is described in this thesis aims to develop a compre‐ hensive integrated system for the optimal use of energy in seaports through the de‐velopment of a smart grid system that is based on the renewable energy at Milford Haven Port, which was developed and used as an applied case stud. It is hoped that this study will contribute to reducing energy prices and that the port will achieve economic benefits by sharing its surplus power with the national grid. A five‐stage research methodology has been developed, starting with the pro‐ cess of collecting and analysing data on fishery buildings, known as and energy audit. It then develops energy simulation models at the port using energy simula‐tion software. The next stage aims to propose a smart grid model at multi‐levels, namely a building, port and a community of 200 houses around a fishery port. The next stage consists of the development of two smart decision‐making systems: the first aimed at sharing surplus power with the neighbours of the port through a Peerto Peer (P2P) energy sharing approach; and the second aims to achieve financial in‐comes for the port by selling surplus power to the national grid when energy prices rise, a price‐based control strategy is used in this system The model was developed and tested within 24 hours on randomly selected days during the four seasons of the year. The simulation was characterised by the fact that it was carried out instantaneously to get an accurate result, which resem‐ bles a real‐life system. In addition, the optimal number of energy storage systems was determined at multi‐levels, which achieve the self‐sufficiency of the electric power that is needed to meet the energy demand during the day. Finally, a proposed road map has been developed to achieve nearly zero carbon fishery ports that can be applied to different ports in different locations

Analysis and simulation of smart energy clusters and energy value chain for fish processing industries

The Irish Seafood agency reports that 15% of global energy is consumed by operations related to refrigeration and air conditioning in the fish industry which stresses the importance of integration with clean renewables and adoption of smart energy management solutions. While fish processing industries have high energy costs with continuous refrigeration, air conditioning and ice making processes, there is a real need to analyse and model energy use in fish ports to understand environmental impacts in terms of CO2 emissions while exploring the potential for integrating renewable energy sources. In this paper, we conduct energy modelling and optimization for the Milford Haven fish processing port in South Wales. We explain how a simulation capability can be developed at the fish industry port level and propose a simulation-based optimization strategy to determine optimized schedules for appliances. The results show that energy consumption can be reduced with the use of optimized appliance schedules developed in relation to the total energy demand as well as a wide range of optimization constraints

Developing smart energy communities around fishery ports: toward zero-carbon fishery ports

Air quality and energy consumption are among the top ten environmental priorities in seaports as stated by the European Sea Ports Organization. Globally, it is estimated that 15% of energy consumption can be attributed to refrigeration and air conditioning systems in fishing activities. There is a real need to understand energy usage in fishery ports to help identify areas of improvements, with a view to optimize energy usage and minimize carbon emissions. In this study, we elaborate on ways in which a simulation capability can be developed at the community level with a fishery port, using a real-world case study seaport in Milford Heaven (Wales, UK). This simulation-based strategy is used to investigate the potential of renewable energy, including local solar farms, to meet the local power demand. This has informed the development of a simulation-based optimization strategy meant to explore how smart energy communities can be formed at the port level by integrating the smart grid with the local community energy storage. The main contribution of the paper involves a co-simulation environment that leverages calibrated energy simulation models to deliver an optimization capability that (a) manages electrical storage within a district an environment, and (b) promotes the formation of energy communities in a fishery port ecosystem. This is paving the way to policy implications, not only in terms of carbon and energy reduction, but also in the formation and sustained management of energy communities

Federating smart cluster energy grids for peer-to-peer energy sharing and trading

With the rapid growth in clean distributed energy resources involving micro-generation and flexible loads, users can actively manage their own energy and have the capability to enter in a market of energy services as prosumers while reducing their carbon footprint. The coordination between these distributed energy resources is essential in order to ensure fair trading and equality in resource sharing among a community of prosumers. Peer-to-Peer (P2P) networks can provide the underlying mechanisms for supporting such coordination and offer incentives to prosumers to participate in the energy market. In particular, the federation of energy clusters with P2P networks has the potential to unlock access to energy resources and lead to the development of new energy services in a fast-growing sharing energy economy. In this paper, we present the formation and federation of smart energy clusters using P2P networks with a view to decentralise energy markets and enable access and use of clean energy resources. We implement a P2P framework to support the federation of energy clusters and study the interaction of consumers and producers in a market of energy resources and services. We demonstrate how energy exchanges and energy costs in a federation are influenced by the energy demand, the size of energy clusters and energy types. We conduct our modelling and analysis based on a real fish industry case study in Milford Haven, South Wales, as part of the EU H2020 INTERREG piSCES project

Modelling and implementing smart micro-grids for fish-processing industry

Fish processing industries involve the usage of energy-intensive equipment, such as refrigerators, air conditioners and ice making machines leading to high energy costs and, indirectly, to an increase of the carbon emissions. As most fish industries sites are old, there is a strong need to make them more sustainable and achieve economic competitiveness in the energy market. Micro-grids have been utilised as efficient solutions in energy-intensive industries greatly balancing energy consumption and production at different scales. Smart micro-grids can also reduce carbon emissions by using renewable energy resources and applying smart energy management techniques.In this paper, we propose a smart micro-grid system for fish-processing industries with a validation use-case at Milford Haven Port in South Wales, UK. The system has been modelled using EnergyPlus and Matlab with the infinite grid, renewable energy resource, battery and charge/discharge controllers utilized for optimising energy consumption and production and for reducing carbon emissions. The preliminary results show that local power demand can meet the local power generation with the implementation of smart energy management techniques to support decision making for fish-processing industries

Decarbonisation of seaports: A review and directions for future research

Marine activities in seaports account for circa 3% of total carbon emissions worldwide, prompting several initiatives to decarbonise their energy systems and make seaports smarter and greener. This paper provides a thorough and authoritative review of the vast array of research in this field, including past and ongoing initiatives. The study reveals that existing research leverages recent advances in digital technologies while focusing on one or several of the following themes: carbon reduction, use of renewable energy resources, cost-performance optimisation, deployment of smart control technologies, the regulatory landscape for greening seaports, and implementing green port practices guidelines. As such, the paper provides a critical review of existing technologies and concepts that promote and contribute to the decarbonisation of seaports, including Smart Grids and Virtual Power Plants. Several avenues for future research are then discussed, including (a) total life cycle approach to seaport energy management, (b) Semantic-based modelling, forecasting and optimisation of seaports energy systems, (c) Secure and reliable seaports energy services, and (d) Transition towards prosumer-driven seaport energy communities. The paper concludes by emphasising the importance of an adapted energy regulatory landscape at a national and EU-wide level to meet EU phased energy reduction targets

A deep learning approach to predict and optimise energy in fish processing industries

The fish processing sector is experiencing increased pressure to reduce its energy consumption and carbon footprint as a response to (a) an increasingly stringent energy regulatory landscape, (b) rising fuel prices, and (c) the incentives to improve social and environmental performance. In this paper, a standalone forecasting computational platform is developed to optimise energy usage and reduce energy costs. This short-term forecasting model is achieved using an artificial neural network (ANN) to predict power and temperature at thirty-minute intervals in two cold rooms of a fish processing plant. The proposed ANN function is optimised by genetic algorithms (GA) with simulated annealing algorithms (SA) to model the relationships between future temperature and power and the system variables affecting them. To assess the accuracy of the proposed method, extensive experiments were conducted using real-world data sets. The results of the experiments indicate that the proposed ANN model performs with higher accuracy than (a) the long short-term memory (LSTM) as an artificial recurrent neural network (RNN) architecture, (b) peephole-LSTM, and (c) the gated recurrent unit (GRU). This paper finds that using GA & SA algorithms; ANN parameters can be optimised. The RMSE obtained by the ANN compared with the second-ranked method GRU was consequently 16% and 4% less for the predicted temperature and power. The results in one particular site demonstrate energy cost savings in the range of 15%–18% after applying the forecast-optimiser approach. The proposed prediction model is used in a fish processing plant for energy management and is scalable to other sites

Optimal control-based price strategies for smart fishery ports micro-grids

The recent ongoing digital transformation of the energy landscape provides new opportunities to decarbonise energy-intensive industries, including the fish processing sector. The paper explores the potential of deploying multi-vector smart micro-grid solutions in fishery ports, sourced from dispatchable renewable generation, including solar energy. This is demonstrated in the Milford Haven Port in South Wales, United Kingdom. The proposed system is modelled using control scenarios developed based on data and energy models of the port. The control scenario makes energy use decisions based on the availability of dispatchable renewable sources and the price of energy from the local energy market. Also, we consider local energy storage by utilising the local electric fishing boat fleet as an alternative energy storage system. The results demonstrate optimised energy use through multi-vector smart micro-grid model by providing more than 70 percent reduction of energy use from grid

Digital twins for performance management in the built environment

Recent events worldwide of climate and geological origins highlight the vulnerability of our infrastructures and stress the often dramatic consequences on our environment. Accurate digital models are needed to understand how climate change and associated risks affect buildings, while informing on ways of enhancing their adaptability and resilience. This requires a paradigm shift in design and engineering interventions as the potential for adaptation and resilience should be embedded into initial brief formulation, design, engineering, construction and facility maintenance methods. This paper argues the need for smarter and digital interventions for buildings and infrastructures and their underpinning data systems that factor in topology (including geometry), mereology, and behavioural (dynamic) considerations. Digital models can be used as a basis to understand the complex interplay between environmental variables and performance, and explore real-time response strategies (including control and actuation) to known and uncertain solicitations enabled by a new generation of technologies. The paper proposes a digital twin model for the construction and industrial assets that paves the way to a new generation of buildings and infrastructures that (a) address lifetime requirements, (b) are capable of performing optimally within the constraints of unknown future scenarios, and (c) achieve acceptable levels of adaptability, efficiency and resilience

Developing Smart Energy Communities around Fishery Ports: Toward Zero-Carbon Fishery Ports

Air quality and energy consumption are among the top ten environmental priorities in seaports as stated by the European Sea Ports Organization. Globally, it is estimated that 15% of energy consumption can be attributed to refrigeration and air conditioning systems in fishing activities. There is a real need to understand energy usage in fishery ports to help identify areas of improvements, with a view to optimize energy usage and minimize carbon emissions. In this study, we elaborate on ways in which a simulation capability can be developed at the community level with a fishery port, using a real-world case study seaport in Milford Heaven (Wales, UK). This simulation-based strategy is used to investigate the potential of renewable energy, including local solar farms, to meet the local power demand. This has informed the development of a simulation-based optimization strategy meant to explore how smart energy communities can be formed at the port level by integrating the smart grid with the local community energy storage. The main contribution of the paper involves a co-simulation environment that leverages calibrated energy simulation models to deliver an optimization capability that (a) manages electrical storage within a district an environment, and (b) promotes the formation of energy communities in a fishery port ecosystem. This is paving the way to policy implications, not only in terms of carbon and energy reduction, but also in the formation and sustained management of energy communities