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The conventionally independent power, water, and heating networks are becoming more tightly connected, which motivates their joint optimal energy scheduling to improve the overall efficiency of an integrated energy system. However, such a joint optimization is known as a challenging problem with complex network constraints and couplings of electric, hydraulic, and thermal models that are nonlinear and nonconvex. We formulate an optimal power-water-heat flow (OPWHF) problem and develop a computationally efficient heuristic to solve it. The proposed heuristic decomposes OPWHF into subproblems, which are iteratively solved via convex relaxation and convex-concave procedure. Simulation results validate that the proposed framework can improve operational flexibility and social welfare of the integrated system, wherein the water and heating networks respond as virtual energy storage to time-varying energy prices and solar photovoltaic generation. Moreover, we perform sensitivity analysis to compare two modes of heating network control: by flow rate and by temperature. Our results reveal that the latter is more effective for heating networks with a wider space of pipeline parameters., Comment: 12 pages, 13 figures, accepted to IEEE Systems Journal

The replacement of hydrogen with conventional fossil fuel has been viewed as a feasible route to the ZERO emission transportation. Nowadays, the water electrolysis method has become mature and efficient enough to produce large-scale of hydrogen, and the condensation and reliquefaction technologies also greatly reduce the hydrogen loss via pipeline transmission, which both motivate the applications of hydrogen as a fuel. In this study, a combined power-hydrogen network is planned to supply the power demand of residential blocks and the charging/fueling demand for electrical vehicles and hydrogen vehicles, respectively. However, the joint scheduling of power-hydrogen network is challenging due to the non-linearity and non-convexity inherited from the hydraulic dynamics of hydrogen transmission. To resolve this problem, a convex-concave based sequential convex approximation method is proposed. A revised 37-bus system is used to demonstrate the validity of proposed method.

Unlike a land-based standalone microgrid, a shipboard microgrid of an all-electric ship (AES) needs to shut down generators during berthing at the port for exanimation and maintenance. Therefore, the cost of onshore power plays an important role in an economic operation for AESs. In order to fully exploit its potential, a two-stage joint scheduling model is proposed to optimally coordinate the power generation and voyage scheduling of an AES. Different from previous studies that only consider the operation cost of the ship itself, a novel coordinated framework is developed in this article to address the shore-side electricity price variations on the ship navigation route. A deep learning-based forecasting method is utilized to predict the electricity price in various harbors for ship operators. Then, a hybrid optimization algorithm is designed to solve the proposed multiobjective joint scheduling problem. A navigation route in Australia is adopted for case studies and simulation results demonstrate the high energy utilization efficiency of the proposed algorithm and the necessity of on-shore power influence on the AES voyage.

Energy storage system (ESS) management benefits the operation of all-electric ships (AESs) since its ability to balance the generation and demand. However, current works rarely consider the influence of navigation uncertainties, i.e., uncertain water waves and winds, and fail to address two characteristics of shipboard ESS, i.e., redundant capacity and distributed locations. In this paper, a two-stage robust optimal shipboard microgrid operation method is proposed to mitigate the pre-voyage and intra-voyage navigation uncertainties. The first stage is to regulate the onboard generation and voyage variables for addressing the worst pre-voyage navigation uncertainties and the second stage is an on-line recourse action which acts on intra-voyage uncertainty realization. To address the characteristics of shipboard ESS, the onboard batteries are separated into two groups and a multi-battery management strategy is proposed for the battery lifetime extension. An AES with 4 DGs and 4 batteries is used in the case studies. The simulation results show that the proposed method can well adapt to the navigation uncertainties and the multi-battery management can extend the battery lifetime by iteratively using different battery groups.

Fully electrified ships, which is known as the 'all-electric ships (AESs)', have the potentials to bring great economic /environmental benefits. To further improve the energy efficiency of AESs, PV generations are gradually integrated, which introduces uncertainties to the AES operation. However, current researches mostly focus on sizing problem whereas rarely concern the operation. In this perspective, a data-driven robust coordination of generation and demand-side is proposed to properly address the onboard PV generation uncertainties as well as reducing the fuel cost of AESs, which consists of an extreme learning machine (ELM) based PV uncertainty forecasting method and a two-stage operating framework, where the first stage for the worst PV generation case and the second stage targets at the uncertainty realization. A 4-DG AES is implemented into the case study and the simulation results show that the ELM-based method can well characterize the PV uncertainties, and the two-stage operating framework can well accommodate the onboard PV uncertainties. Further analysis also demonstrates the proposed method has enough flexibility when facing working condition variations. Ministry of Education (MOE) Nanyang Technological University National Research Foundation (NRF) This work was supported in part by the Ministry of Education, Republic of Singapore, under Grant AcRF TIER 1 2019- T1-001-069 (RG75/19), and in part by National Research Foundation (NRF) of Singapore under Project NRF2018-SR2001-018. The work of Y. Xu was supported by Nanyang Assistant Professorship from Nanyang Technological University, Singapore. The work of S. Fang was supported by the Open Funding of Key Laboratory of Maritime Intelligent Equipment and System, Ministry of Education, Shanghai Jiao Tong University. Paper no. TPWRS-01765-2018.

Given the rapid development of the ship industry and increasing ship operating cost and gas emissions, this article studies the optimal operation of a multienergy ship (MES) microgrid to simultaneously reduce the ship operating cost and gas emissions. Apart from the conventional diesel generators, electric energy storage, and photovoltaic cells, the combined cooling heat and power (CCHP) unit is introduced for higher energy utilization efficiency. Besides, to flexiblize the CCHP operation and enhance the multienergy interaction, the thermal storage and power-to-thermal conversion unit are also included in the MES microgrid. Then, a multiobjective coordinated energy dispatch and voyage scheduling method are presented. After the constraints linearization, the coordination method is formulated as a mixed-integer linear programming problem, which is solved by the augmented ϵ-constraint method through Cplex solver. Finally, the proposed method is verified on a testing MES microgrid with necessary sensitivity analysis, and by comparing it with traditional benchmarks, its advantages are demonstrated. The simulation results indicate that the proposed method is effective for reducing the operating cost and emission, and it could provide references for the optimal operation of the emerging multienergy cruising ships.

Nowadays, extensive electrification of maritime transportation, represented by the seaport microgrids and all-electric ships (AESs), has been viewed as a feasible route to enhance the overall system flexibility as well as to mitigate the resulted in growing environmental issues. However, with the trend of electrification, the connections between the seaport and ships are no longer limited in the logistic-side, but also expanded to the electric-side, which makes the future maritime transportation management as a complex transportation-power multi-microgrid coordination problem. In land-based applications, multi-microgrid coordination is a relatively mature technology and already brings enormous economic and environmental benefits, but there still exists some gaps before those land-based technologies being integrated into maritime applications. In this perspective, this overview study emphasizes the characteristic of seaport microgrid and AESs, then several emerging technical challenges and the future research prospects are raised after a comprehensive literature survey.

Under increasingly stringent emission regulations, carbon capture system (CCS) is a feasible alternative to reduce the shipping greenhouse gas (GHG) emission before the maturity of renewable energy technology. In this sense, this article proposes an optimal sizing method to determine the capacity of shipboard CCS under strict energy efficiency operating index (EEOI) constraint. The proposed model is formulated as a two-stage planning problem, where the first stage is to determine the capacity of CCS and the expanded capacity of energy storage system to sustain the operation of CCS, and the second stage is a joint shipboard generation and demand-side management model to address the power shortage issue led by the CCS integration. Extensive simulations demonstrate that under EEOI constraint, the CCS integration is feasible to reduce the shipping GHG emission, and the proposed joint generation and demand-side management method is able to relieve the power shortage issue of shipboard CCS. The corresponding average carbon capture level increases 11.9% with the joint management.

The multi-energy microgrid (MEMG) comprises heterogeneous distributed generators (DGs) such as wind turbines, diesel generators, combined cooling, heat and power plants etc. Proper placement of these DGs is critical for the system energy efficiency and network reliability performance. This study proposes a two-stage coordinated method for optimally placing heterogeneous DGs in an MEMG project considering the uncertainties from renewable energy sources (RESs). Apart from optimising the traditional DG size and location, this method considers the optimal DG type and investment year simultaneously by maximising the project net present value (NPV), which consists of investment costs and operation costs. The whole problem is modelled as a two-stage coordinated stochastic optimisation model, where the long-term DG investment is determined at the first stage and operation decisions are determined at the second stage. The proposed method is verified on a test MEMG system. The simulation results show that its NPV is positive, which means the method is effective and should be implemented. Compared with the conventional DG placement approaches, the proposed method is more robust against the RES uncertainties and can better coordinate the heterogeneous energies with higher dispatch flexibility and economic profits.

A hybrid electric unmanned aircraft (HEUA) uses the aerial thermal engine and energy storage system (ESS)-driven electric motor to meet the load demand, thus can be viewed as a "mobile multienergy microgrid." Conventionally, for a given flight mission, only the energy of an HEUA is optimized while the flight speed is fixed; thus, the overall energy efficiency may be limited. This paper aims to coordinately schedule the energy consumption and the flight speed of an HEUA toward optimal economic and environmental objectives. First, a coordinated optimization model is proposed to coordinately schedule the energy consumption (including the power output of the thermal engine and the ESS) and flight speed (i.e., the true air speed) of an HEUA for simultaneously minimizing the fuel consumption (FC) and polluted gas emission (PGE). Second, a decomposition-based solution method is developed to convert the original nonlinear multiobjective optimization model into a bilevel programming model which can be solved iteratively. Simulation results demonstrate that, compared with the conventional single energy scheduling, the proposed coordinated scheduling method can achieve much better performance in terms of FC and PGE. MOE (Min. of Education, S’pore)

The all-electric ship (AES) usually employs battery energy storage systems (ESSs) in the shipboard microgrid. However, the battery-only storage usually experiences frequent deep discharging or charging to meet the sudden load variations in a voyage, which may lead to significant degradation of battery lifetime. This paper, hybridizes two types of ESSs and proposes a two-step multi-objective optimization method for hybrid ESS (HESS) management. The first step regulates the HESS with the onboard diesel generators to simultaneously optimize both the economic and environmental objectives, and the second step is to split the active power of HESS into two individual ESSs for minimizing the battery cycle degradation. The first step is formulated as a bi-level optimization model through constraint decomposition. Then, a normal boundary intersection method combining with the column-and-constraint generation algorithm is developed to solve the proposed model. Extensive simulations demonstrate that the HESS can effectively resolve the power-density shortage of the battery-only system, and its integration into AES is able to extend the battery lifetime and improve both the economic and environmental indices. The authors would like to thank the Key Laboratory of Maritime Intelligent Equipment and System, Ministry of Education, Shanghai Jiao tong University, for providing valuable data for the research.

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.

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.

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.

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.

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.

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.

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.

Numerous underlying causes of power-quality (PQ) disturbances have enhanced the application of situational awareness to power systems. This application provides an optimal overall response for contingencies. With measurement data acquired by a multi-source PQ monitoring system, we propose an interactive visualization tool for PQ disturbance data based on a geographic information system (GIS). This tool demonstrates the spatio–temporal distribution of the PQ disturbance events and the cross-correlation between PQ records and environmental factors, leveraging Getis statistics and random matrix theory. A methodology based on entity matching is also introduced to analyze the underlying causes of PQ disturbance events. Based on real-world data obtained from an actual power system, offline and online PQ data visualization scenarios are provided to verify the effectiveness and robustness of the proposed framework.

An all-electric ship (AES) uses diesel generators and energy storage system (ESS) to meet both propulsion and service loads. Thus, it can be viewed as a mobile microgrid. During the operation of an AES, significant uncertainties such as water wave and wind introduce considerable speed loss, which may lead to severe voyage delays. To fully address this issue, a new robust energy management model is proposed to coordinately schedule an AES's power generation and voyage considering the uncertain wave and wind. Two objectives are minimized simultaneously: the fuel consumption (FC) and energy efficiency operational indicator (EEOI). The problem is formulated as a bi-level robust optimization model after certain constraint decomposition. Normal boundary intersection method is utilized to solve this multi-objective programming. Compared with existing joint scheduling methods, the proposed method can fully guarantee the on-time rates of AES in various uncertain scenarios and providing high-quality Pareto solutions. Nanyang Technological University National Research Foundation (NRF) The work in this paper was supported in part by National Research Foundation (NRF) of Singapore under project NRF2018-SR2001-018. Y. Xu’s work is supported by Nanyang Assistant Professorship from Nanyang Technological University, Singapore.

Owing to the variations of loads and sustainable energy, the power injections and the load increment direction are of uncertainties, which pose great challenges to the traditional models of power system operation. To determine the effects of those uncertainties on the reactive power reserve (RPR), an interval optimal RPR dispatch with generator rescheduling (GR) is formulated. To evaluate the RPR interval more comprehensively, two sub-models, namely the optimistic and pessimistic sub-models, are built. The proposed method is applied to several IEEE systems to illustrate its effectiveness. Case tests show that, proper GR schemes can enhance the RPR of power system by postponing the limit hitting of the generator reactive power. The obtained RPR scheme shows better robustness to the uncertainties of power injections compared with that of the conventional RPR dispatches, and the restriction on the RPR interval widths also suggests the operating risk reduction.

This paper deals with the distribution network reconfiguration problem. A hybrid algorithm of particle swarm optimization (PSO) and tabu search (TS) is proposed as the searching algorithm. The new algorithm shares the advantages of PSO and TS, which has a fast computation speed and a strong ability to avoid local optimal solution. After a thorough comparison, network random key (NRK) is introduced as the corresponding coding strategy among various tree representation strategies. NRK could completely avoid the generation of infeasible solutions during the searching process and has a good locality property, which allows the new hybrid algorithm to perform to its fullest potential. The proposed algorithm has been validated through an IEEE 33 bus test case. Compared with other algorithms, the proposed method is both accurate and computationally efficient. Furthermore, a test to solve another problem also proves the robustness of the proposed algorithm for a different problem.

Summary Conventional optimal reactive power reserve dispatch approaches are mostly modeled in deterministic forms, where the power injections are fixed. With the integration of intermittent renewable sources, the power injections could become uncertain. To maintain the security, a stochastic optimal reactive power reserve dispatch model is proposed, which is formulated as a chance-constrained optimal power flow problem. To begin, a multistage partitioning method considering both the decoupling degree and the robustness to failure is proposed to obtain the voltage control areas. Then, the original nonlinear stochastic optimal reactive power reserve dispatch model is decomposed into a successive multiarea linearized programming problem. Finally, a chance constraint relaxation–based method is proposed to solve the above problem. The results show that the proposed method can significantly enhance the voltage stability margin, and the security in uncertain scenarios can also be maintained for the satisfaction of the chance constraints.