Optimization-Based Energy Management for Multi-energy Maritime Grids

Nowadays, the increment of international maritime trade decelerates by the influence of the global downside economy, and the even stricter environmental policies further intensify the competition between different sectors in maritime transportation systems, which motivates the electrification of all the attached sub-systems for higher energy efficiency, such as the all-electric ships, electrified ports, and various electrified ocean platforms. Different equipment and technologies have been integrated into those subsystems, such as fuel cell, energy storage, gas capture system, alternative fuel, multi-energy management methods, and cold-ironing facilities, which give birth to the maritime grids. A typical maritime grid consists of generation, storage, and critical loads, and can operate either in grid-connected or in islanded modes, and operate under both the constraints of energy system and maritime transportation system, and formulates as a “maritime multi-energy system”. The energy management of this special system will shape the energy efficiency of the future maritime transportation system. In this chapter, the background and motivation of maritime grids are illustrated with a comprehensive literature survey. After that, some relevant promising technologies are described, then the typical topologies and frameworks of the next-generation maritime grids are shown. At last, the contributions of this book are summarized.

In this chapter, the basics used in this book for the optimization problem are briefly introduced. The organization is shown as follows: (1) the overview of optimization problems, which gives the general forms and the classifications of optimization problems, and some frequently used models are also illustrated; (2) the introductions for the optimization problems with uncertainties, including the stochastic optimization, robust optimization, and interval optimization; (3) the introductions for the convex optimization, including the semi-definite programming (SDP), second-order cone programming (SOCP), and some convex relaxation skills; (4) the optimization frameworks which are frequently used, including the two-stage optimization, and the bi-level optimization. Six examples and the corresponding case studies based on the classic knapsack problem are reformulated to show different optimization problems. The above models are all important topics and not only used in this book, but also in many engineering scenarios. In summary, the contents in this chapter are some basics for optimization problems. The readers who are interesting in the details can refer to the professional mathematical literature or books listed in the reference and who are familiar with these basic optimization skills can also skip this chapter without any inconvenience.

In this Chapter, the main management targets of maritime grids are comprehensively reviewed and several important targets in this book are described in detail, including the (1) navigation task: the ship should arrive at the destination via the scheduled route within a pre-given time-period; (2) energy consumption: achieving the pre-given management task by consuming the minimum energy, including the navigation tasks of ships and the logistic tasks of ports; (3) gas emission and energy efficiency: reducing the gas emission and improving the energy efficiency while accomplishing the pre-given management task; (4) reliability under multiple failures: improving the ability to continuously supply critical loads during multiple failures; (5) Lifecycle cost: extending the lifetime of auxiliary equipment and reducing the corresponding lifecycle cost; and (6) quality of service: improving the satisfaction degree of customers, such as the heating/cooling service in the cruise ship, the cold-ironing service for the berthed-in ship, on-time rates of the cargo delivery. Conventionally, most of the above targets are vital operating indexes in the maritime industry. However, under the new background of extensive electrification, the above targets become energy-related and therefore could be controlled and optimized via optimization-based energy management.

The management tasks of maritime grids are comprehensively reviewed in Chap. 3, then in this Chapter, various optimization models for those goals are reviewed. According to the types of decision variables, the optimization models can be classified as three main types, (1) Synthesis optimization, which refers to the optimal configuration of maritime grids, including the construction layout, electrical layout, and the system structure; (2) Design optimization or planning optimization, which refers to determine the technical characteristics of each component, such as the system capacity, and rated power; (3) Operation optimization, which refers to determine the optimal operating states for a given system (i.e., the synthesis and design are both known) under specified conditions. Three types of optimization models can be abbreviated as Synthesis-Design-Operation (SDO) optimization, which is coordinated with each other to achieve the overall optimum. In this Chapter, the formulation and solution of SDO optimization for maritime grids are described. At first, the Synthesis-Design-Operation (SDO) optimization of maritime grids are explained, then the typical topology of maritime grids are reviewed, then the overall compact optimization models for maritime grids are formulated. At last, the solution methods are reviewed and a decomposition-based method to solve the optimization models is described.

The planning and operation of maritime grids are always under the influences of uncertainties that come to different aspects, such as (1) navigation uncertainties, (2) energy source uncertainties, and (3) equipment uncertainties. The navigation uncertainties include the uncertain wave and wind, adverse weather conditions, and calls-for-service uncertainties. The energy source uncertainties include the renewable energy uncertainties, power generation uncertainties, and main grid uncertainties. The equipment uncertainties include the equipment failure uncertainties and equipment scheduling uncertainties. In this Chapter, the above uncertainties are illustrated in detail, including their origins, effects, and solution methods. Then the effects of full electrification of maritime grids on addressing those uncertainties are analyzed. After that, two typical problems are formulated to demonstrate the effects of energy management methods on the uncertainties. The case studies demonstrate the validity of energy management to mitigate the influences of uncertainties.

Generally, energy storage is the capture of energy produced at one time for use at a later time. Since these energy shifting or shaving abilities, energy storage can act as the role of generation or demand in different time-periods. This ability has shown great potentials to change the conventional operation pattern of power systems, i.e., the real-time power balance between the generation-side and demand-side. As a special type of power system, maritime grids are also applicable platforms for energy storage integration, such as in propulsion systems, or for energy recovery, and renewable energy integration. With the development of various energy storage technologies, energy storage will soon become indispensable equipment in maritime grids, and the energy storage management will become essential correspondingly. This Chapter focuses on the energy management of energy storage in maritime grids. At first, the energy storage technologies and their great effects on the power system operation are introduced. Secondly, the characteristics of different energy storage technologies are described. After that, the applications of energy storage in maritime grids are illustrated, (1) for the navigation uncertainties and demand response, and (2) for the renewable energy integration, and (3) for the energy recovery. At last, two practical problems are studied to show the energy management schemes of energy storage to address navigation uncertainties and to extend battery lifetime, respectively.

Generally, maritime grids involve many types of energy carriers, and these energy carriers exchange, transform and distribute within the maritime grids, including the transportation of fossil fuel, fuel to electricity generation, electricity to heat, and fuel to heat, and so on. Besides, maritime grids also involve the operation of the transportation system or even wireless communication in the future. In this sense, future maritime grids can be viewed as “multi-energy transportation-power microgrids”, and the proper management of those multiple energy carriers is essential for the operation of future maritime grids. This Chapter focuses on this topic to analyze the roles of multi-energy management in future maritime grids. The organization of this Chapter is shown as follows, (1) the first section introduces the concept of multi-energy management; and (2) the second section gives a few examples of future multi-energy maritime grids; and (3) the third section formulates a general model for the multi-energy management of maritime grids and gives a decomposed solving process; and (4) the fourth section gives two typical problems, (1) multi-energy management in ships, and (2) multi-energy management in seaports, to analyze the roles of multi-energy management in the operation of maritime grids.

Conventional maritime grids mostly only have one type of main power source. For example, seaports mainly rely on the main grid, and plenty of equipment is driven by fossil fuel which is supplied by the outside transportation network. For the ships, the main engine is the main power source and the generators are driven by the main shaft. However, with the development of transportation electrification, the capacities of maritime grids grow and electricity becomes the dominating energy form. Various efficient power sources are integrated for a more economic and environmental maritime grid. Nowadays, the main integrated power sources include battery, fuel cell, renewable energy, and many demand response tools can also act as “virtual power sources” to facilitate the operation of maritime grids. To coordinate multiple power sources, this Chapter focuses on the multi-source energy management methods of maritime grids. Various power sources integrated into maritime grids are illustrated in the first place, including the (1) main grid; (2) main engines; (3) battery and fuel cell; (4) renewable energy and demand response. Then the coordination between different power sources in maritime grids is described. At last, some practical cases are given to show the effects of multi-source energy management of maritime grids.

In former Chaps. 1~8, multiple areas of optimization-based energy management methods for maritime grids are illustrated in detail. Based on current research, this Chapter focuses on future ways for maritime grids. At first, future maritime grids are defined as a series of “multi-energy microgrids involving different energy networks and undertaking multiple maritime functions” and the coordination between/within different maritime grids are comprehensively reviewed, i.e., the energy carriers and flows, networks, services coordinated in maritime grids, which provides the necessity for a properly designed optimization-based energy management system to address complicated operating scenarios. Then three main promising ways are reviewed and overviewed to show future roadmaps. The first focus is the data-driven technologies, including the forecasting of navigation uncertainties, the estimation of energy storage lifetime, and data-driven energy management, and so on, which uses current data-mining methods to facilitate the operation of maritime grids. The second focus is the siting and sizing problems, i.e., for renewable energy, energy storage, fuel cell, and gas capture system. The third focus is the multi-functional energy management systems to address the integrated energy storage systems, shipboard microgrids, seaport microgrids, multiple ocean platforms, and the coordination between them.