Abstract
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 “multienergy transportationpower 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 multienergy management in future maritime grids. The organization of this Chapter is shown as follows, (1) the first section introduces the concept of multienergy management; and (2) the second section gives a few examples of future multienergy maritime grids; and (3) the third section formulates a general model for the multienergy management of maritime grids and gives a decomposed solving process; and (4) the fourth section gives two typical problems, (1) multienergy management in ships, and (2) multienergy management in seaports, to analyze the roles of multienergy management in the operation of maritime grids.
Keywords
 Multienergy management
 Multienergy system
 Transportation power system
 Maritime grids
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7.1 Concept of Multienergy Management
7.1.1 Motivation and Background
Generally, all the energy systems are “multienergy systems” in the sense that multiple energy sectors interact at different levels. For example in conventional power systems, the coal or gas used for generating electricity should be transported to each power plant, and this process implies the couplings between fossil energy and electrical energy. Another case is, the heating service by the combined heatpower plant also last for decades, and this process includes the coupling between heating energy and electrical energy. However, those energy couplings between different systems are conventionally weak compared with the relationship within a single energy system, and that is the main reason for the past studies of power system mostly only consider the electrical energy [1,2,3]. However, the interactions between different energy systems become tighter and more frequent recently, and this trend is about to continue in the future [4,5,6,7], such as the electricgas energy system, and the coordinated heatpower system, or even the transportationpower system motivated by the transportation electrification. In this sense, conventional energy management for a single energy system may not be valid in the future, which drives the research of multienergy management.
In literature, [8,9,10,11] focus on the coordination between the gas system and power systems [12,13,14,15]. Study the energy management methods for heatpower systems [16, 17]. Study the waterpower systems and [18,19,20,21,22] investigate the coupling between the transportation system and power system by electric vehicles’ charging and discharging. The above research has brought a new perspective in energy system analysis, particularly in the light of reducing the economic and environmental burden of energy services. In summary, three benefits can be achieved by multienergy management:

a.
Increasing or improving the energy efficiency of the entire system and the utilization of primary energy sources. The reason is the multienergy system can use the energy at different levels. For example, the waste heat after generating electricity can be used for heating services and the energy efficiency of the entire system improves.

b.
Better deploying various energy resources at multiple system levels. For example, smallscale gas turbines can respond to volatile electricity market prices in a windrich energy system.

c.
Increasing the energy system flexibility by the coordinations between different energy systems. For example, scheduled charging/discharging of the electric vehicles acts as demand response tool for power system. Or the thermal storage tank can bring flexibility for combined powerheat plants.
Since the above three main advantages, the research on multienergy management is essential for future energy systems. However, different energy systems have different administrators and quite distinct characteristics, and their coordinations are much more complex compared with the coordinations within a single energy system. Proper modeling methods and control strategies should be proposed to facilitate their operation.
7.1.2 Classification of Multienergy Systems
The multienergy systems can be classified by different perspectives, and there are mainly four perspectives to characterize the MES. The first is the spatial perspective. This perspective points out how MES can intend at different levels of aggregation in terms of components or even just conceptually. These levels go from buildings to district and finally to regions and even countries. This classification is shown in Fig. 7.1a.
The second perspective focuses on the provision of multiple services by optimally scheduling different energy systems, particularly at the supply levels. Such as the services provided by the MES, including electricity supply, water supply, heating service, EV charging services, gas filling services, and so on. This classification is shown in Fig. 7.1b.
The third perspective highlights how different types of fuels can be integrated together for providing optimal energy services, typically for economic or environmental targets. The fuel types range from classical fossil fuel, such as oil, coal and natural gas, to biomass fuels, and renewable energy. This classification is shown in Fig. 7.1c.
The fourth perspective discusses the coordinations between different energy systems, especially the coordination between different networks, such as the electrical network, gas network, district heating/cooling network, in terms of facilitating the development of multienergy management methods and their interactions. This classification is shown in Fig. 7.1d.
Figure 7.1a classifies the MESs from the spatial perspective. An individual building exchanges energy by the transmission of electricity, heat, cooling, and natural gas. Then multiple buildings aggregate as a district, then multiple districts aggregate as a region and expand to a wider area. In this perspective, MESs can be classified as the building MES, district MES, region MES, and so on.
Figure 7.1b classifies the MESs from the service perspective. Generally, MES can provide multiple services to the customers, such as the electricity supply, heat and cooling power, and even some transport services, such as the charging/discharging of EV. In this perspective, MESs can be classified as combined electricheat MES, combined electricheatcooling MES, and even electricheatwater supply MES, since the water pump is coupled with the electric network by the electrical water pumps.
Figure 7.1c classifies the MES from the fuel perspective. For example, there exist many power sources in MES, such as power plants, boilers, gas turbines, and chillers. They may consume different types of fuels. Different power plants may consume coal, oil, or gas. A boiler may consume electricity or other fossil fuel, and a chiller may consume electricity or heat power. In this sense, the fuel type can also classify the MESs, such as the coalgas MES, gashydrogen MES, or even ammonia MES since ammonia is a new type of carbonfree fuel [23].
Figure 7.1d classifies the MES from the network perspective since every “energy carrier” should be transmitted by a designed network. The electrical network includes power systems on multiple scales. Gas and oil are transported by pipelines or transportation flows. Heat and cooling power also have certain pipelines. Those different networks can have different topologies and operating strategies, which is the main motivation of this classification method. In this sense, the networks of MESs can be classified as combined electricheat networks, combined electricheatcooling networks, and so on.
7.2 Future Multienergy Maritime Grids
7.2.1 Multienergy Nature of Maritime Grids
A sketch of MES is given in the former section to show the basic advantages and characteristics. In this section, the multienergy nature of maritime grids will be analyzed to show their similarities and differences compared with conventional MESs, and Fig. 4.1 is redrawn below as Fig. 7.2 as an illustration of future maritime grids. Two cases of maritime grids will be given after this illustration.
(1) Spatial perspective
From Fig. 7.2, maritime grids cover different spatial areas. For example, island microgrids cover an individual island, and the energy sources include offshore wind power, photovoltaic power, and underground cables. Seaport microgrids cover the harbor territory, and the energy sources include the offshore wind farm, landbased photovoltaic farm, oil pipelines, and the electricity supply from the harbor city. Other maritime grids include the drilling platforms and different types of ships. In summary, maritime grids have a very wide range on system scales, from the smallest to a ferry or a building and the biggest to a harbor city, which involves all the energy sources within a conventional MES. Different maritime grids are coupled tightly by energy connections, and current multimicrogrid coordination methods can be used in maritime grids to achieve better system characteristics.
(2) Service perspective
Figure 7.2 shows maritime grids can provide different services to customers, including the conventional services of electricity, heat, cooling in landbased MES, also including some types beyond current focuses, such as the logistic services, fuel transportation services. This is the primary difference between current studied MES (landbased MES) from the maritime grids. This is also a challenge for the research of maritime grids, since new energy models of those services should be formulated and integrated into the energy management model.
(3) Fuel perspective
Maritime grids also involve different types of fuels. In Fig. 7.2, the drilling platform can harvest crude oil or natural gas, and transport them to an island or the seaport. The industrial factory can refine crude oil into different types of fuels, such as gasoline, diesel, and so on. Those fuels may in reverse fill into the ships for sailing, into seaport for generation, and into the harbor city for daily lives. Besides, some novel fuels may also use in maritime grids, such as ammonia, methanol, and ethanol.
(4) Network perspective
Maritime grids also have different types of networks. Figure 7.2 shows some typical ones, (1) electrical networks in harbor city, seaport, industrial factory; (2) heat/cooling networks in harbor city, seaport, industrial factory; (3) fossil fuel networks between the ocean platforms and a seaport or an island; (4) electrical networks between offshore wind farms and a seaport or an island; (5) multienergy network within an island; (6) transportation network by ships and vehicles. Those networks above are connected with multiple energy and information flows and may be more complex than conventional landbased MESs.
7.2.2 Multienergy Cruise Ships
In Fig. 7.3, a typical topology of a multienergy cruise ship is shown. Detailed illustrations can be depicted as follow. The load demands can be classified into three categories, the electric load, thermal load, and propulsion load. Among the three load demands, the propulsion load is to drive the cruise ship, which consists most of, usually more than 50% of the total load demand [24]. The propulsion load has a simple cubic relationship with the cruising speed, which is under the constraints of navigation distance [25]. The electric load in cruise ships includes the illumination, recreation equipment, movie theater, and so on. This type of load scales up to tens of megawatt [24], which is provided by the electric power bus, shown as the blue lines and arrows in Fig. 7.3. The thermal load in cruise ship includes the cooling and heat load, the swimming pool, and the cooking. This type of load also scales up to tens of megawatt [27], which is provided by the thermal power network, shown as the green line and arrows in Fig. 7.3. It also should be noted that in some cruise ships the cooling and heat loads are provided by the electricity. In this work, we will compare the introduced multienergy technology with the single electric supply mode.
As for the generation systems, to provide adequate electric and thermal loads for the overall cruise ship. There exist three types of generation systems, i.e., DG, CCHP, and PTC. The DGs make up the main part of the shipboard generation, which provides most of the electric power supply. The CCHP both provides the electric power and the thermal power and the PTC uses electricity to produce thermal power. To balance both the electric and thermal loads, the HES (electric and thermal energy storage) is integrated.
7.2.3 Multienergy Seaport
We have illustrated the multienergy seaport in Chap. 1. Here we redraw Fig. 1.17 as Fig. 7.4to further show its multiple energy flows.
Generally, the seaport is connected with the main grid and various renewable energy are integrated, i.e., seaport wind farms and PV farms in Fig. 7.4. All the portside equipment, including the quay cranes, gantry cranes, transferring trunks, are electricallydriven. The seaport provides four types of services to the berthedin ships and has four subsystems for each type of services: (1) logistic service. The berth allocation and quay crane scheduling for loading/unloading cargo; (2) fuel transportation. Unloading or refilling fuel for the berthedin ships; (3) coldironing. Providing electricity to the berthedin ships and (4) refrigeration reefer for the coldchain supply. The coordination between different subsystems is shown in Fig. 7.5. Four subsystems are communicating by the seaport control center and a distributed control strategy is employed in the seaport microgrid.
7.3 General Model and Solving Method
7.3.1 Compact Form Model
From above, maritime grids involve different networks and provide multiple types of services by different types of fuels. In this sense, maritime grids have a significant characteristic, i.e., using the electric network as the backbone for energy management, and other different networks serve as the “load demand” of electric networks. For example, the heat/cooling networks couple with the electric network by CHP or electric boiler/chiller, and water supply network couple with the electric network by electric water pumps, and logistic network couple with the electric network by charging/discharging.
For this complex network, a general energy scheduling form can be shown as (7.1). Where \( f\left( x \right) \) is the objective function of the main network, generally the electric network, and \( x \) is the decision variable vector; \( g_{i} \left( {y_{i} } \right) \) is the objective function of the ith network, and \( y_{i} \) is the decision variable vector of the ith network; \( F\left( x \right) \) is the constraint set of the main network; \( G_{i} \left( {y_{i} } \right) \) is the constraint set of the ith network; \( A_{i} \cdot x = H_{i} \left( {y_{i} } \right) \) is the coupling constraint set of power consumption of coupling equipment, such as water pump, CHP, and various logistic equipment.
7.3.2 A Decomposed Solving Method
This Chapter proposes a decomposed method to solve this type of problem, which is given by the following Theorem 7.1.
Theorem 1
The above formulation is equivalent to the following form.
where \( u_{i} \) is the optimal multiplier vector of the following optimization problem.
Proof
(1) Problem (7.1) and (7.2) have the same feasible region.
(1.1) If \( \bar{x} \) be feasible for (7.1), then \( \bar{x} \) is feasible for (7.2).
Let \( \bar{x} \) be an arbitrary point in the feasible region of (7.1), then
It follows that (7.5) holds for all \( \lambda_{i} \).
Then \( \bar{x} \in V \), and \( F\left( {\bar{x}} \right) \ge 0 \). \( \bar{x} \) is also feasible for (7.2).
(1.2) If \( \bar{x} \) be feasible for (7.2), then \( \bar{x} \) is feasible for (7.1).
Let \( \bar{x} \) be an arbitrary point for (7.2), then (7.5) holds at least for one i. \( F\left( {\bar{x}} \right) \ge 0 \) is satisfied all the same, then (7.6) holds.
It follows that
Since \( \eta = 0 \) is allowed in (7.7). Now, (7.7) is the dual of the following optimization problem.
Obviously, (7.8) is feasible and has the optimal value of 0, hence, \( \bar{x} \) is feasible for (7.1).
(2) The objective function
Since \( u_{i} \) is the optimal multiplier vector of (7.3), then (7.9) holds.
In this sense,
From above, (7.1) and (7.2) are equivalent, then the solution process is given below.
Solution process: From (7.2), the original problem can be solved in a twostep process. It should be noted that, \( g_{i} \left( {y_{i} } \right) + \tau_{i} \cdot G_{i} \left( {y_{i} } \right) \) is a constant when minimizing \( x \), so it is eliminated for simplification.
Step 1: Given a feasible \( \bar{x} \), solve (7.11) for \( y_{i}^{*} \) and \( u_{i} \).
It should be noted that, there are no coupling between different networks. So (7.11) can be solved in parallel.
Step 2: Solve (7.12) for \( x. \)
Then check the convergence characteristic, if yes, terminates and if not, return to Step 1 and update \( \bar{x} \). The algorithm convergence is given below.
Algorithm convergence
It is proved that the proposed method has finite \( \varepsilon \)convergence characteristic.
Theorem 2
Assume \( X \) and \( V \) are both compact set, \( f \), \( g \), \( F \), \( G_{i} \) and \( H_{i} \) are continuous. The set \( UT\left( x \right) \) of the optimal multiplier vector for (7.3) is nonempty for all \( x \) in \( X \) and uniformly bounded. Then, for any given \( \varepsilon > 0 \), the proposed procedure terminates in a finite number of steps.
Proof
For simplification, we define (7.13).
For any sequence \( L\left( {x^{v} ,\tau^{v} ,u^{v} } \right),x^{v} \) of the optimal solution of (7.2). Firstly, the optimal multipliers sequence \( \tau^{v} \), \( u^{v} \) will converges to a point noted as (\( \bar{\tau },\bar{u} \)), since the uniformly bounded assumption of \( UT\left( x \right) \). Additionally, \( x^{v} \) will converge to a point denoted as \( \bar{x} \) since the compactness of \( X. \)
At last, since \( L\left( {x^{v} ,\tau^{v} ,u^{v} } \right) \) is a nonincreasing sequence and bounded below, there exists at least one subsequence of \( L\left( {x^{v} ,\tau^{v} ,u^{v} } \right),x^{v} \) which converges to a point, we noted it as \( L\left( {\bar{x},\bar{\tau },\bar{u}} \right),\bar{x} \).
Since the weak duality, (\( \bar{\tau },\bar{u} \)) is the optimal multiplier for \( \bar{x} \) and (7.14) holds.
Then, for any given \( \varepsilon > 0 \), there should be finite \( v \) to make (7.15) hold.
Then the proposed method should converge in finite steps.
7.4 Typical Problems
7.4.1 Multienergy Management for Cruise Ships
This section uses the cruise ship in Fig. 7.3 as the test case to show the effects of energy management. For a more economic and environmental operation of the cruise ship, the shipboard energy management system will optimally dispatch the outputs of the DG, CCHP, PTC, and HES to fulfill the propulsion, onboard electric, and thermal loads. However, in practice, those control variables are not on the same timescale. During the navigation, the ship will constantly cruise and the speed cannot be regulated rapidly [25], and the onboard facilities for tourists also should keep working till night. This makes the propulsion and electric loads should be fulfilled in a longterm horizon (every hour in this work). Besides, the thermal load should be satisfied in a shortterm horizon (20 min) to meet the realtime constraints of indoor temperature and hot water supply. To coordinately satisfy the above load demands in two timescales, in this work we propose a twostage operation framework for the cruise ship, which is shown as follow:
From the Fig. 7.6, the first stage hourly schedules the DGs, CCHP, and battery to fulfill the voyage distance constraints and hourly electric load demand. The thermal power produced by the CCHP is stored in the thermal energy storage. In the second stage, every 20 min, the PTC and thermal energy storage is dispatched to meet the thermal load demand. With the above operation framework, both the propulsion and electric loads can be met in a longterm timescale, as well as the thermal load demand can be met in a shortterm timescale to improve the QoS.
To show the benefits of the proposed model, the onboard generation and battery SOC are shown in Fig. 7.7a, b, respectively. Figure 7.8 compares the results of the thermal load of the proposed twostage method.
From Fig. 7.7a, the battery can coordinate with the speed adjustment to smooth the load profiles, which facilitates the economy of cruise ships (the DGs can better operate around their economic points). From Fig. 7.7b, the battery may have much deeper charging/discharging events without the speed adjustment. That is mainly because the cruising speed is fixed during the cruising timeintervals, and the battery should quickly respond to the load profiles for the economy of navigation.
From Fig. 7.8a, the proposed twostage scheduling model can meet the thermal load demand in a more accurate timescale by simply dispatching the loading factor of the thermal storage tank, and the outputs of PTC and CCHP. The results are shown in Fig. 7.8b. The indoor temperature can be kept as a constant meanwhile the single first stage will have a maximal 3 ℃ temperature variations since the accumulated effects of thermal load demand variations. Similarly, the single first stage also cannot meet the hot water supplydemand all the time, and the thermal variations will also be accumulated and make the supplies always smaller than the demands.
Current cruise ships are mainly BOS cruise ships, which means in the BOS mode, the thermal load demand is all provided by the electricside (PTC units). In this case, the BOS ship replaces the CCHP to conventional DG with the same capacity. The parameters are the same with DG2, 3. The total load demand and EEOI of BOS and HES ships are shown in Fig. 7.9.
From Fig. 7.9, the BOS cruise ship will have much larger load demands since the thermal load is provided by the PTC unit. Correspondingly, the EEOI of the HES integrated cruise ship is also much smaller than the BOS by 8.37%.
7.4.2 Multienergy Management for Seaport Microgrids
(1) System description
From Fig. 7.10, there are three energy resources in this microgrid, i.e., photovoltaics(PVs), electrical substation, and gas pressure house. The PVs and substation inject electricity into the seaport microgrid via DC and AC buses, respectively. The gas pressure house injects gas into the seaport microgrid to the gas storage. Additionally, to improve the system flexibility, a battery energy storage system (ESS) and two thermal storages are incorporated. The AC/DC loads and heat/cooling power are supplied to the seaport loads, and DC power is used for charging the electric trunk. The power to gas equipment transforms the excess power to gas to fill the gas vehicles.
In this paper, the scheduling horizon is divided into equal time step \( \Delta t \), denoted by set \( {\mathcal{T}} = \left\{ {1,2, \ldots ,T} \right\} \). The proposed operation method is formulated as a twostage framework, where the first stage is for the dayahead timescale, and the second stage is for realtime scheduling, i.e., hourly. In the dayahead operation (first stage), the hourly energy scheme is provided considering the uncertainties, and then in the second stage, the seaport microgrid adjusts its scheduling plan responding to the realization of uncertainties in the hourly timescale. The electrical load profile, heating load profile, and cooling load profile are shown in Fig. 7.11, which are all given in 1000 scenarios. Other detailed parameters can be found in [28].
(2) Case study
To verify the effectiveness of the proposed method, different cases are formulated as follows.
Case 1: Twostage optimization is considered, meanwhile the joint constraints are considered.
Case 2: Only the firststage optimization is considered.
(2.1) Bidirectional AC/DC power flow
To show the coordination between AC and DC sides, the power flow via the bidirectional AC/DC converter is shown in Fig. 7.12. The AC to DC power is shown as the surface above the zero surface, while the DC to AC power is shown as the surface below the zero surface. Then, to show the effects of ESS, the state of charge (SOC) of battery is shown in Fig. 7.13.
From the above figure, at first, when the PV power is almost zero, i.e., t = 0–5 h, 20–24 h, the DC load is mainly met by AC to DC converter. When the DC load gradually increases, the AC to DC power is also increasing, and the battery discharges to further support DC load, i.e., t = 5, 6 h. After that, with the PV power increasing, the power demands also become larger, i.e., both DC and AC loads during t = 10–16 h. In those time intervals, the PV output is beyond the maximal DC load, which leads the PV power change to AC via AC/DC converter to support the AC load or charge into battery, which is shown as the surface below zero in Fig. 7.12 and the charging event in Fig. 7.13. From the above results, the integration of the AC/DC converter can bring great flexibilities to meet both DC and AC loads. The DC power for PV and AC power from UG and CHP can coordinately operate to enhance energy efficiency.
(2.2) Multiple energy flows
In this seaport microgrid, various energy carriers are working coordinately to enhance operation flexibility. To show those coordinations, the power of CHP is shown in Fig. 7.14, the power of heat storage is shown in Fig. 7.15, the power of cooling storage is shown in Fig. 7.16, and the power of powertogas facility is shown in Fig. 7.17.
From Fig. 7.11d and e, there are two demand impulses of both heat and cooling demands in t = 6, 7 h. The CHP responds to those demand impulses and consumes the gas to produce electricity and heat. The heat energy is stored and both the heat and cooling storages are discharging in this period to satisfy the demand, which is shown as the great valleys in their energy curves in Figs. 7.15 and 7.16. After that, CHP is shutdown since the total electricity demand is limited. The thermal demands are then met by the coordination of thermal storage and the gas boiler.
It should be noted that when t = 10–15 h, the temperature increases and requires great airconditioning power demand. While in this time period, the PV power is also in its peakhours. Then the PV power is converted to gas for the gas boiler to meet the airconditioning power demand, which is shown as in Fig. 7.17.
The above results show that different energy carriers can be coordinated flexibly in a seaport microgrid. The excess electricity can be converted to gas for thermal demand. With the interactions between different energy carriers, the electric and thermal demand can both be satisfied and the flexibility can be enhanced.
(2.3) Electric and gas trucks
The energy demand of trucks is quite important in future seaport since they play a major role for cargo lifting and transporting. However, before the completed electrification of vehicles, the gas trunks and electric trunks will both exist in seaport microgrid. To satisfy their energy demands, the electric and gas subsystems of seaport microgrid should be operated in coordination, respectively. In this case, the equivalent energy of gas trucks are shown in Fig. 7.18, and the charging power of electric trucks are shown in Fig. 7.19.
From Fig. 7.18, the energy peaks of gas vehicles are t = 10–15 h and 20–24 h. The first peak period corresponds to the working hours, and the second is the vehicles coming back for charging. From the results in Fig. 7.19, the charging patterns are more periodic with three peak hours, i.e., t = 10–15, 16–18, and 20–24 h. From the above results, both the gas and electricity demands of trunks can be satisfied.
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Fang, S., Wang, H. (2021). Multienergy Management of Maritime Grids. In: OptimizationBased Energy Management for Multienergy Maritime Grids. Springer Series on Naval Architecture, Marine Engineering, Shipbuilding and Shipping, vol 11. Springer, Singapore. https://doi.org/10.1007/9789813367340_7
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DOI: https://doi.org/10.1007/9789813367340_7
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