Optimal energy management and operations planning in seaports with smart grid while harnessing renewable energy under uncertainty

doi:10.1016/j.omega.2021.102445

Omega

Volume 103, September 2021, 102445

https://doi.org/10.1016/j.omega.2021.102445 Get rights and content

Highlights

•
Integrated energy management and operations planning is suggested for ports as large scale energy end users.
•
Ports can significantly reduce costs with energy arbitrage and load-shifting under a demand response mechanism in a smart grid.
•
Benefits of using port microgrid with time-variant energy prices and storage units, and the value of different storage sizes are quantified.
•
The positive impact of harnessing renewable energy in ports is quantified.
•
Energy-aware operations contribute to sustainability in ports.

Abstract

The importance of energy efficiency and demand response management while harnessing renewable energy draws more attention from many industries in recent years. Seaports, as large scale end-users, aim to adopt energy management systems (EMS) since energy prices have increased over years and sustainable operations is a key target for greening the port industry. Many seaports start to install fully electrified equipment and use electricity as the source of energy because electricity consumption, instead of carbon-intensive energy sources, contributes to the climate change mitigation targets. In this study, a mixed integer linear programming model is suggested to solve the integrated operations planning and energy management problem for seaports with smart grid (e.g. port microgrid) considering uncertain renewable energy generation. The operations planning aims to determine the number of quay cranes (QCs) and yard equipment to assign to each ship for each one hour period. It also determines each ship’s berthing duration which affects the hourly energy consumption due to the cold ironing and the available reefer containers. These plans result in energy demand. Meanwhile, energy management matches energy demand and supply considering different energy pricing schemes and bidirectional energy trading between energy sources (e.g. utility grid, renewable energy sources) and energy storage systems. Results indicate that significant cost savings can be achieved with smart grid (port microgrid) compared to conventional settings. Deploying energy storage systems in port microgrid results in important cost savings. Energy consumption is dominated by QCs, cold-ironing and reefer containers. Finally ports which harness renewable energy obtain significant costs savings on total cost.

Introduction

The annual growth in the amount of freight transported by shipping industry fuels the energy demand in ports. Ports aim to increase the use of green energy and reduce energy costs for an economic and environmental competitiveness [1]. In this sense, the increasing use of electricity (e.g. to power port equipment, to meet energy demand of ships during berthing at port, and to keep reefer containers cool) helps to achieve a transition from fossil fuels-consumer ports to green ports [2]. Therefore, electrification is one of the main pillars for next generation green ports. In light of reducing energy costs and striving for sustainability, ports have stepped up their efforts for better energy management [3].

Energy management systems (EMS) in ports aim to control and optimize energy demand, energy supply, energy flow and storage at the end-user level. It includes adjusting the energy demand to match available energy supply considering energy prices (i.e. demand side management). Energy demand is mainly from the equipment (e.g. cranes, yard handling equipment) during container handling, cold ironing for providing shoreside electrical power to ships during berthing, reefer containers for keeping them cool in the yard and other facilities. Meanwhile energy supply can be from utility grid and onsite renewable energy sources (RES). Ports can buy energy from the grid and/or use the energy generated from the RES.

Energy demand profile in each hour is a function of port operations planning which is driven and constrained by ship handling deadlines and port productivity [4], [5]. In this paper operations planning includes decisions regarding berthing start time, ship handling duration (time), and the number of quay cranes (QCs) and yard cranes (YCs) to assign in each hour for each ship. The number of QCs and YCs is allowed to be time-variant during ship berthing. To this end, port operators have mainly two components in the objective. They try to minimize the cost of the lateness of operations for all ships (compared to an expected finishing time for each ship) and the energy related costs.

Harnessing renewable energy at ports is evidently imperative for greening the industry [6], and it has gained popularity as many ports have a specific potential to utilize solar (e.g. Port of Singapore, Jurong Port in Singapore), wind (e.g. Port of Hamburg, Port of Rotterdam), waves and tides (e.g. Port of Valencia), etc. However renewable energy generation is mostly intermittent during the day. Energy storage systems (ESS) have been used to ensure that intermittent renewable energy can be stored until the right time and required demand can be controlled [7]. Renewable energy source power production is uncertain for the day ahead and we assume that a scenario set represents possible power production realisations. In this paper energy management component coordinates and optimizes the energy flow between energy consumers, utility grid and distributed energy resources (e.g. renewable energy generators, ESS) by minimizing total energy procurement costs considering different hourly energy pricing schemes. Energy management also ensures hourly power demand of consumers is met and energy in ESS is properly managed without deep battery discharging. Handling of the uncertain renewable energy generation is stage-wise in our work. We produce an initial baseline plan which includes day-ahead operations plan and energy management plan considering all port and energy related parameters with expected RES power generation. After the realisation of uncertain RES power generation, energy decisions are updated considering the baseline plan with the objective of minimizing the energy cost of all scenarios and energy deviations from the baseline plan.

The integrated operations planning and energy management is studied in this study where the bi-directional power flow opportunity between renewable energy sources, utility grid and ESS provides several energy supply, sell-back and storage options. In this study there are mainly two energy management settings, namely conventional and smart grid (which can also be referred port microgrid). In conventional setting, the energy price is a fixed value (e.g. single value throughout the day, fixed value depending peak/off-peak period) and the price is set long in advance. In smart grid setting, energy price fluctuates significantly between hours of the day as price is taken from electricity market on day-ahead basis. In smart grid, proper demand side management encourages energy arbitrage and load shifting, a strategy that refers to shifting energy demand between time periods considering time-variant hourly energy price. The operations planning component, which results in the energy demand, manages operations ensuring that port operational constraints are satisfied. A mixed integer linear programming (MILP) formulation is suggested to solve the problem, and optimal results are obtained in short computational times for all instances which include up to 40 ships to be handled in two days.

The contribution and novelty of the paper is multi-fold.

(1)
To the best of our knowledge, this is the first study that suggests an energy management system for ports as large scale end users and it enhances the concept by integrating energy management and operations planning.
(2)
Results indicate that ports can potentially reduce costs with energy arbitrage and load-shifting under a demand response mechanism in smart grid (i.e. port microgrid).
(3)
The paper presents economic benefits and practical implementation of an end-user demand response management under time-variant energy pricing schemes with storage units, and the value of different storage sizes is quantified.
(4)
Share of each energy consumer in total energy consumption is discussed for the first time including cold ironing. Results indicate that QCs, reefer containers and cold ironing dominate the energy consumption with similar shares.
(5)
The positive impact of harnessing renewable energy in ports is quantified.

The case study in this paper is inspired by ports in Singapore, namely Port of Singapore and Jurong Port. These ports have invested into Photovoltaic (PV) infrastructure for renewable energy generation to utilize Singapore’s sunlight-rich potential [8]. Ultimately, ports are large scale end-users in energy supply networks and Port of Singapore has a sizeable portion of Singapore’s daily energy demand. Therefore, investments for smart energy management, including demand response mechanisms (including smart meters, energy-management controller, etc.), energy storage and PV infrastructure are worthy to be investigated [9].

The remainder of the paper is organized as follows. In Section 2, a literature review is conducted. In Section 3, the problem definition is introduced. The mathematical model is presented in Section 4. Numerical results and case study implications are discussed in Section 5 and conclusion finalizes the paper.

Section snippets

Relevant work

Recently Bektaş et al. [1] and Zhen et al. [10] conducted literature reviews on green transportation emphasizing the importance of reducing energy related costs and establishing energy management systems in maritime supply chains. Particularly Iris and Lam [2] reviewed the energy efficiency studies in ports. Iris and Lam [2] presented technologies, operational methods and energy management systems available for ports. They emphasized that the future research should investigate load shifting

Problem definition

In this study, we consider a port where $N$ vessels are to be handled over a planning horizon of $H$ periods and each period corresponds to one hour. Each vessel has a specific number of shipping containers ( $m_{i}$ ) to be handled. For each ship, the problem aims to determine the number of QCs and YCs to be assigned for each hour of berthing. The number of QCs and YCs assigned can change between periods while total number of available QCs and YCs in the port is limited. The number of containers each QC

Mathematical model

In this section we first describe the mixed integer linear programming model for the problem. A complete list of notations, i.e. parameters, decision variables of the model, can be found in Tables 1 and 2.

The objective function mainly composes of baseline plan costs and energy management costs associated with scenario realisations. The first three terms in the objective function (1) minimize the total lateness cost of all ships ( $\sum_{i \in V} d_{i} L_{i}$ ) and the net cost of energy from the utility grid to

Data and experimental settings

In order to solve the integrated energy management and operations planning problem, mathematical models are run on a 3.2 GHz and 8 Gb of RAM computer. CPLEX 12.7 is used to solve the models. All models are solved to optimality in short computational times.

Managerial insights and conclusions

The paper addresses the integrated energy management and operations planning problem for next generation green ports. The problem aims to minimize total lateness cost of operations and total energy costs considering hourly energy price with energy selling back option under uncertain renewable energy generation. Most of studies in the literature solely focus on energy-aware micro operations planning or power management for ports. There is no comprehensive study that obtains optimal operations

Declaration of Competing Interest

Authors declare that they have no conflict of interest.

Acknowledgements

The authors would like to thank three anonymous reviewers for insightful comments and suggestions.

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^☆: Area: Data-Driven Decision Making and Analytics; Sustainable Operations. This manuscript was processed by Associate Editor Stanko Dimitrov.

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Optimal energy management and operations planning in seaports with smart grid while harnessing renewable energy under uncertainty☆

Highlights

Abstract

Introduction

Section snippets

Relevant work

Problem definition

Mathematical model

Data and experimental settings

Managerial insights and conclusions

Declaration of Competing Interest

Acknowledgements

Eur J Oper Res

Renew Sustain Energy Rev

Eur J Oper Res

Transp Res Part E

Energy Policy

Adv Eng Inform

Comput Ind Eng

Eur J Oper Res

Transp Res Part C

Int J Electr Power Energy Syst

Omega

Appl Energy

Int J Electr Power Energy Syst

Renew Energy

Comput Ind Eng

Transp Res Part E

Transp Res Part B

Transp Res Part B

Adv Eng Inform

Appl Energy