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Integrated port energy system considering integrated demand response and energy interconnection
Electrical Power and Energy Systems 117 (2020) 105654
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Integrated port energy system considering integrated demand response and energy interconnection
T
⁎
Tianli Songa,b, Yang Lia, , Xiao-Ping Zhangb, Cong Wub, Jianing Lib, Yi Guoc, Haifei Gua a
School of Electrical Engineering, Southeast University, Nanjing 210096, China Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Birmingham B15 2TT, UK c Logistics Engineering College, Shanghai Maritime University, Shanghai 201306, China b
A R T I C LE I N FO
A B S T R A C T
Keywords: Integrated port energy system Energy hub Integrated demand response Energy interconnection Shore-side power
This paper proposes a framework for modeling an integrated port energy system (IPES). A configuration and sizing model of energy hub (EH) is built for the port area considering integrated demand response (IDR) and energy interconnection (EI). A comparative study of five different planning scenarios, traditional energy supply solution, EH solution, EH with IDR, EH with EI, and EH with both IDR and EI are conducted to identify the best option for minimizing the total planning cost of an IPES. Furthermore, the daily operating conditions of EHs are discussed and sensitivity analyses with varying energy prices, line loss rates, and IDR participation rates are performed to demonstrate the robustness of the model. The impacts of the uncertainties of ships using shore-side power on planning costs are also analyzed. Based on the proposed methods, numerical simulation results show the effectiveness of the EHs with multiple energy infrastructures in the IPES. In addition, after considering IDR and EI, the total planning cost decreases significantly, which demonstrates the necessity and benefits of IDR and EI.
1. Introduction Ports are the interface of maritime transport and are considered as the engines of the port cities. They play a significant role to support the exchange of goods and in linking peripheral areas with the mainland. According to the statistics of the International Maritime Organization (IMO), over 90% of the world’s trade is carried by sea and it is, by far, the most cost-effective way to move goods and raw materials around the world [1]. At the same time, ports are primarily industrial and commercial areas with significant energy consumptions. All transportation sectors including road, train, air, and sea account for nearly 20% of total energy consumption and sea mode is responsible for about 4% of them [2]. However, conventional port energy infrastructures like electricity, heat and cool deal independently which makes the system less robust and low efficient with high operational cost. Meanwhile, due to the vast volumes of crude oil in the form of imports or exports, the port environment is susceptible to heavy pollution from carbon dioxide (CO2) emissions. Therefore, it is necessary and urgent to find an integrated energy solution with both economic and environmental objectives for achieving green ports [3–6]. Integrated energy system (IES), which is defined as an inventible
⁎
solution for a future energy system, expected to increase sustainability, reliability, security, efficiency, and resiliency of the conventional energy system [7,8]. It is also aimed to reduce air pollutions and support multiple transportations. Accordingly, an attendant energy hub (EH) is employed for achieving the interaction between different energy carriers [9–11]. Unlike traditional energy systems depending on singleenergy-supply equipment, EH can serve as a functional unit where different energy sources are converted, stored, and dispatched [12]. For a single EH, it could range from the aggregation of energy sources and loads at the customer level to the aggregation of distributed energy resources and customer clusters, and it can even be extended to an entire city [13]. As for the EH in the port area, since there is a variety of different energy sources and complicated working conditions, it needs the configuration and sizing of EH more optimal. Generally, the modeling of EH can be expressed using a coupling matrix [11,14]. Ref. [15] presents an EH model embedded with a wind power unit based on the general model. A novel approach for EH modeling based on graph and network theory is introduced in [16]. With regard to the configuration and sizing of the EH, an approach in [17] considers the planning capacities of combined heat and power (CHP) unit, gas boiler (GB), absorption chiller and energy storage equipment collaboratively. The result illustrates that the proposed
Corresponding author. E-mail address: [email protected] (Y. Li).
https://doi.org/10.1016/j.ijepes.2019.105654 Received 18 June 2019; Received in revised form 22 October 2019; Accepted 25 October 2019 0142-0615/ © 2019 Elsevier Ltd. All rights reserved.
Electrical Power and Energy Systems 117 (2020) 105654
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pCCHP / GB / PtG / GS / AC unit maintenance cost for each EH component (¥/kW h) pcurt − wind unit punishment for wind curtailment (¥/kW h) r interest rate (%) energy conversion rate (%) η
Nomenclature Acronyms AC CCHP CFR EH(s) EI GB GS IDR IPES PtG
air conditioning combined heat, cooling, heating, and power capital recovery factor energy hub(s) energy interconnection gas boiler gas storage integrated demand response integrated port energy system power to gas
Variables
Ctot Cinv CE Cmat CDR Ccurt − wind Picurt − wind PiEI ,j Piele ele Pi cchp PiPtG PiWind qiAC cool qi cchp
Indices and sets
i/j t T s k ϕD
port area time time periods that DR can be invoked different seasons energy type aggregation of sizing options for various devices
total cost (¥) investment cost (¥) energy cost (¥) maintenance cost (¥) demand response cost (¥) wind curtailment cost (¥) the amount of wind curtailment (kW) power exchange between port area i and j (kW) power trading from the grid (kW) power generated by CCHP (kW) electricity consumed by PtG (kW) actual utilized wind power (kW) cool supplied by AC (kW) cool supplied by CCHP (kW)
qiGB heat qi cchp Si, GS Vigas ViPtG , gas χω, i
heat supplied by GB (kW) heat supplied by CCHP (kW) capacity of GS (m3) purchase volume of natural gas (m3/h) natural gas produced by PtG (m3/h) binary variables of electrical line and EH components for the size option ω (1/0) Δdri+, k the amount of increased load (kW) the amount of decreased load (kW) Δdri−, k ΔREloadi,+k, t the amount of rebound load (kW) αβ γ dispatch factor for injected energy (%)
Constants
pdr unit DR cost (¥/kW h) CiCCHP / GB / PtG / GS / AC constructing cost for each EH component (¥/kW h) number of days in the season s Ds heating value of natural gas (kW h/m3) Hgas load demand of k in port area i (kW) Lik pele price of electricity (¥/kW h) p gas price of natural gas
(1) This study initially proposes the framework of the IPES, which considers multiple energy resources together in the EHs. Further, configuration and sizing models of EH are built to size the energy components optimally. (2) To enhance economic efficiency and system flexibility, this study incorporates IDR and EI to integrate different energy into the IPES. The effectiveness of different scenarios with and without IDR and EI are evaluated. (3) Sensitivity analyses are performed to demonstrate the robustness of the IPES planning method with reference to different electricity and natural gas price fluctuation rates, line loss rates, and IDR participation rates. In addition, the impacts of uncertainties of ships using shore-side power on the planning cost are analyzed.
approach can reduce the total cost more effectively compared with the traditional method. Ref. [18] shows an IES embedded with a power-togas (PtG) unit, which converts electrical power to a gas fuel when there is surplus power from renewable energy generation. Ref. [19] sets the minimum annual cost and maximum exergy efficiency as the goal to optimize the capacity of each device in the cogeneration system. Meanwhile, in order to reduce costs and enhance energy utilization efficiency, various economic and technical factors should be taken into consideration. Ref. [20] considers the power and gas interconnection between EHs. Ref. [21] testifies the multi-energy complementary and multi-EH coordination would be the most economical and environmental among all the operation plans. Also, a coordination framework for tie line scheduling and power dispatch to operate multi-area systems is described in [38]. Furthermore, with the development of energy internet, demand-side management plays a more critical role than ever [22]. It aims at enhancing economic efficiency and system flexibility. Some literature [23,24] focused on the applications of the demand response (DR) in an IES. The concept of IDR is introduced in [25] to present the switching between energy resources. Ref. [26] shows that an IDR program in a smart EH can reduce the dependency on customers’ motivation. For both electrical and thermal load, a new framework of IDR is proposed in [27], which can efficiently reduce the total cost of the EH. However, to the best knowledge of the authors, there are few studies related to the modeling of an IPES. Therefore, the objective of the study is to lay a framework for modeling the IPES, which aims at minimizing the total planning cost of the system with lower emission levels and higher energy efficiency. In contrast to previous work, the main contributions of this paper can be summarized as follows.
The rest of the article is organized as follows. A framework of the IPES and the model of EH are presented in Section 2. Section 3 formulates the EH-based planning problem and its associated constraints. Section 4 presents the case studies with and without IDR and EI, followed by discussions of operating conditions of EHs and sensitivity analyses. Finally, the conclusions drawn from the study are presented in Section 5.
2. The EH model with IDR and EI in IPES 2.1. Description of the IPES Ports are intensive energy extended areas with multiple systems that require many tens of megawatts. Generally, the area near the coastline of the port can be the best area for the utilization of wind energy 2
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heating load demands in the port area i. It is worth noting that electrical load means the total electricity consumption in the port area except for the load of ships using shore-side power. PiPtG is the load of the PtG device. Piele denotes the electricity purchased from the power grid for the hub i , Vigas , pur denotes the gas fuel used by the CCHP unit and GB. The dispatch factor α and β define the dispatch of wind power and purchasing power for both electrical load and the AC. H gas (9.78 kW·h/m3) is the heating value of natural gas, ηGB (0.92) is the efficiency of the GB, and η AC (4.00) stands for the coefficient of performance (COP) of the AC. The CCHP unit is the main converter equipment, characterized by cool the conversion efficiencies, which stand for gas to cold (ηcchp , 1.2) and
resources, which is suitable for developing medium and small wind power generation. With regard to the energy consumptions in IPES, ports can be deployed with the topologically distinct terminal for passengers, for logistics commercial and industrial shipbuilding activities, unloading and loading goods in container terminals, rail yards, air condition warehousing, commercial buildings and residential areas [5,28]. From the perspective of energy production and consumption, it can be summarized as port appliances consume electricity or natural gas to provide electrical, heating and cooling demands. Therefore, according to the energy generation, demand and conversion characteristics in port areas, the framework of the IPES is proposed in this paper. Fig. 1(a) is the whole structure of IPES and Fig. 1(b) shows the model of EH within the IPES. The entire IPES composed of three EHs is connected with the port distribution power network and natural gas network. Each EH is in charge of the dispatch of the regional energy system. Within each EH, there are a combined cooling, heating, and power (CCHP) unit, a PtG unit, an electric air conditioning (AC) unit, a GB unit, and a gas storage (GS) unit. They feed all the energy activities including unloading and loading goods and shore-side power supply in the terminals. The wind power generation unit (Piwind ) is connected to the local energy supply bus. Meanwhile, with the increasing penetration of variable wind power in the port area, the operational stability of the IPES may become a problem. Therefore, PtG technology is introduced here to converts surplus electricity into natural gas for storage and utilization [29]. As a result, a GS device is also included in EH. It is noted that the shore-side power system is an important part of the IPES. With the promotion of electric power substitution in the port area, more and more ships will use shore-side power instead of their auxiliary engines to continue operations such as loading, unloading, hoteling, communicating, and lighting when staying in the port. It is widely seen as an effective method to improve the environment in the port area [30]. The primary function of the IPES is to combine the multi-energy system with the power system in the port area and adopt intelligent dispatching means to resolve the contradiction between the intermittent power generation of renewable energy systems and the continuity of load power consumption. Multi-energy systems can be connected to the distribution network in different forms, which can achieve mutual benefits among different energy sources. In addition, it can reduce the emission levels of pollutants in the port area and improve the utilization efficiency of various energy sources. In other words, IPES can be seen as the application of the IES in the port area.
heat , 0.9) separately. α and β mean the dispatch factor gas to thermal (ηcchp of wind power and purchased electricity for shore-side power, electrical load, and AC. Meanwhile, the dispatch factor λ denotes the natural gas consumed by both CCHP and GB. Within each EH, the energy management system can be employed to control the dispatch factor to optimize the EH schedule [26]. The last but not the least, PiEI , j denotes the energy exchange between the EH i and the EH j through the electrical network. In addition, the PtG unit and the GS unit are included in EH to produce and store natural gas. The gas balance can be modeled as follows.
2.2. EH model As shown in Fig. 1(b), the hourly electrical balance between input energy and output demand can be expressed as follows:
Liele + Liship + PiPtG ele = αPiele + Pi cchp + βPiWind + ∑ (PiEI ,j ) j ele = αPiele + γηcchp H gasVigas + βPiWind + ∑ (PiEI ,j ) j
(1)
The cooling balance is defined as:
Licool
cool = qi cchp + qiAC cool gas gas = γηcchp H Vi + ((1 − α ) Piele + (1 − β ) PiWind ) η AC
(2)
The heating balance can be express as:
Liheat
heat = qi cchp + qiGB heat gas gas = γηcchp H Vi + (1 - γ ) ηGB H gasVigas
where
Liship ,
Liele ,
Licool ,
and
Liheat
(3)
Fig. 1. The proposed framework for IPES (a) The whole structure of IPES (b) The EH model.
denote the ship, electrical, cooling, and 3
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3. EH configuration and sizing model
(4)
PtG where Vigas , pur is the per-unit-time purchase volume of gas. Vi, gas denotes gas ̇ the natural gas produced by the PtG unit and Vi, store are the vector of the storage gas variation.
3.1. Objective function It is assumed that the whole IPES is managed by a single integrated energy company in the port area. As a result, the objective of the optimization is to minimize the total cost of the company for planning the IPES. In this paper, a time horizon composed of three typical days is considered, representing the average daily electrical, heating and cooling demands in each season: the cooling season (s = c), heating season (s = h), and transition season (s = t). Thus, the objective function is expressed as follows:
2.3. IDR model DR can increase system flexibility on the demand side by reducing peak power demand or temporarily shifting as responses to the price signals or incentive mechanisms. Taking advantage of the complementarities of different inertia of multi-energy, IDR is aimed at fully exploiting the capabilities of all the customers [31]. The coupling between different energy carriers enables customers to participate in the IDR programs not only by means of load shifting but also by switching the source of their consumed energy. As a result, the economical and reliable operations of the IPES can be improved. Based on the traditional DR model and EH model in the previous section, the IDR is modeled according to different characteristics.
(9)
min Ctot = Cinv + CE + Cmat + CDR + Ccurt − wind
∑
Cinv =
i
CCHP (C CCHP χ CCHP ) + CFRGB (C GB χ GB )+ ⎞ ⎛CFR i i ω, i ω, i ⎜ CFRPtG (C PtG χ PtG ) + CFRGS (C GS χ GS )+ ⎟ i i ω, i ω, i ⎟ ⎜ Line ⎟ ⎜ CFR AC (CiAC χ AC ) + CFRLine (CiLine χ j ω, i ω, i - j ) ⎠ ⎝
(10)
24
CE =
Ds · ∑ (pele (t ) Piele (t ) + p gas (t ) Vigas , pur (t ))
∑ ∑
(1) Transferable IDR
s = c, h, t
i
t=1
(11)
24
Transferable IDR refers to the starting time of using energy that can be transferred but the usage period is fixed. Usually, the transferable load should not be interrupted once started, and the daily load remains constant. Its model can be expressed as follows:
Δdr _transi+, k, t + Nx = Δdr _transi−, k, t , ∀ t ∈ T − Nx
i
CDR =
i
,∀t∈T (6)
where Δdr _shiftk + and Δdr _shiftk− denote the amount of increased load and decreased the load in shiftable IDR. (3) Curtailable IDR Curtailable IDR means that the loads can be curtailed in a specific period, but it may cause the load rebound in the following hours. The more loads are curtailed, the more rebound appears.
ΔREloadi,+k, t = φ1 Δdr _curti−, k, t − 1 +φ2 Δdr _curti−, k, t − 2
CFR = (7)
−
+
+
24
∑ Picurt−wind (t ) ⎞⎟ t
⎠
(14)
r (1 + r )life (1 + r )life − 1
(15)
(1) Demand-supply balance Apart from the demand-supply balance constraints in Eqs. (1)–(3), IDR should be considered in each energy balance equation, which can be expressed as:
= (Δdr _transi+, k + Δdr _shifti+, k + ΔREloadi+, k ) Δdr _curti−, k )
⎛ Ds ·⎜pcurt − wind ⎝
(13)
⎠
t
3.2. Constraints
IDRi, k = Δdri+, k − Δdri−, k Δdr _shifti−, k
s = c, h, t
24
∑ (Δdri+ (t ) + Δdri− (t )) ⎞⎟
where r (5% in this study) is the interest rate and life is the lifespan of the facility.
Δdr _curtk−
denote the amount of rebound load where ΔREloadk and and curtailed load. φ1 φ2 φ3 are the load rebound coefficients at the time t − 1, t − 2 , and t − 3. The values of them are set 0.6, 0.3 and 0.1 separately in this study [32]. It is assumed that every energy type k has these three kinds of IDR presented above so the responsiveness of IDR of an energy source can be summarized as:
(Δdr _transi−, k
⎛ Ds ·⎜pdr ⎝
(12)
where each Ci and CFR are the installation cost in area i and the capital recovery factors (CFR) of power lines and EH components. CFR can be calculated using Eq. (15). A binary decision variable χi ∈ {0, 1} is assigned to each available sizing option of the component, once a candidate energy carrier of a certain capacity is installed, the investment state of the corresponding size option is changed to 1. CE is the energy cost of the system, which includes the grid power purchases and the fuel cost of natural gas energy. Ds is the number of days in different seasons (108, 105, and 152 for winter, summer and transition periods, separately). p gas and pele denote the gas and electricity price at time t separately. Cmat represents the maintenance cost for EH components. pcchp , pGB , p PtG , and p AC denote the maintenance cost per unit of the different facilities. CDR represents the cost for the IDR programs and pdr (0.3 ¥/ kW·h in this study) is the price of invoking the IDR per unit. Also, due to the problems of renewable energy curtailment in the port area, the target function involves the punishment of not using wind power Ccurt − wind and it is related to the penalty factor pcurt − wind (1.5 ¥/ kW·h in this paper) and the amount of wind curtailment P curt − wind .
T
+
cchp GB ⎛ pcchp Pi (t ) + pGB Pi (t ) ⎞ Ds · ∑ ⎜ PtG P PtG (t ) + p AC P AC (t ) ⎟ i i t = 1 ⎝+ p ⎠
∑ ∑ i
Shiftable IDR has no limitation on the power consumption at each time as long as it can satisfy the total load demand within a predetermined time interval.
+φ3 Δdr _curti−, k, t − 3, ∀ t ∈ T + 3
s = c, h, t
Ccurt − wind =
(2) Shiftable IDR
t=1
s = c, h, t
∑ ∑
(5)
where Δdr _transi,+k and Δdr _transi−, k denote the amount of increased load and decreased the load in transferable IDR separately.
∑ (Δdr _shifti+,k,t − Δdr _shifti−,k,t )= 0
∑ ∑
Cmat =
∑ Demandik = ∑ Supply ik − IDRi, k = ∑ Supply ik + Δdri−, k − Δdri+, k
(8) 4
(16)
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where ∑ Demand ki and ∑ Supply ki are the total energy load demand and supply of energy k considering power interconnection in the port area i . It is noteworthy that all the electrical, cooling and heating balance in this study can be summarized as Eq. (16).
dis ch storage state. ηGS and ηGS (both of them are set 0.95 in this study) denote the charging and discharging efficiency, and Δt is the time interval. Si, cap is the stored capacity. γ min and γ max (0.02 and 0.98, respectively, in this study) are the minimum depth of discharge (DoD) and maximum DoD. In addition, the gas storage is assumed to be balanced in the dispatching period, which means that the state at the beginning is equal to that at the end of the day.
(2) Sizing option Suppose that there is one size of each device can be built in an EH in each port area at maximum:
∑
3.3. Model linearization
χ D ⩽ 1, D ∈ {CCHP , GB , PtG , GS , AC } (17)
ω∈ϕD
The optimization model presented above is nonlinear due to the multiplication of a binary variable and a continuous viable in Eqs. (21) and (28). Therefore, in order to improve the solving efficiency, the linearization method in [33] can be used in this study as follows:
(3) operating limits of facilities
∑
χω, i Pωmin , i ⩽ Pω, i ⩽
ω ∈ φcchp, GB, PtG, AC
∑
χω, i Pωmax ,i (18)
ω ∈ φcchp, GB, PtG, AC
Line Line Line Pmin, ω ⩽ Pi − j ⩽ Pmax, ω
, start − Piend (t ) ⩽ M1 (1 − χi,Line (t )) ,j j
(31)
, start Piend (t ) ⩽ M1 χi,Line (t ) ,j j
(32)
, dis − Pich, dis (t ) ⩽ M2 (1 − χich (t )) ,j
(33)
, dis , dis Pich (t ) ⩽ M2 χich (t ) ,j ,j
(34)
(19)
min where Pωmax , i and Pω, i denote the maximum and minimum limits for each Line Line component in the hub i . Pmax, ω and Pmin, ω are the maximum and minimum capacity of lines between hub i and hub j .
(4) energy interconnection start Piend (t )(1 − δLi, j ) , j (t ) = Pi, j
(20)
Line Line PiConnect (t ) = Piend (t ) − P jstart ,j , j (t ) χi, j , i (t ) χ j, i (t )
(21)
χi,Line (t ) + χ jLine j , i (t ) ⩽ 1
(22)
0 ⩽ Pistart (t ) ⩽ PiLine ,j ,j
(23)
where M1 and M2 denote large numbers. By this means, the models can be converted to a mixed-integer linear programming (MILP) problem.
4. Case study As a case study, a typical port district in China is selected for demonstrating the developed model and method. As presented in Fig. 2, it is assumed that there are three interconnected EHs in this port district, which interconnect the three different port areas together. Each port area has a type of ship terminal with specific working conditions. They are the EH 1 (bulk terminal) with an area of 25 km2, the EH 2 (cruise terminal) with an area of 34 km2 and the EH 3 (container terminal) with an area of 41 km2. Therefore, the energy consumptions of the three areas are different from each other and the whole port district can be seen as an IPES with three EHs. Each EH’s structure is similar to that of the presented by Fig. 1.
and Piend where Pistart ,j , j are the powers at the starting and ending points of the linei-j at time t with the transfer from the hub i to the hub j. It is clear that one of the two should be 0, and their difference indicates the magnitude and direction of the interconnected power. Also, Li, j is the distance between the hub i and hub j. δ denotes the power loss rate of the power line per kilometer. Eq. (21) indicates that the power through line should be smaller than its rated capacity. (5) IDR frequency limits
0 ⩽ Δdri+ (t ) ⩽ λμi+ (t )
∑ Demandi (t )
(24)
0 ⩽ Δdri− (t ) ⩽ λμi− (t )
∑ Demandi (t )
(25)
0 ⩽ μi+ (t ) + μi− (t ) ⩽ 1
(26)
T
0⩽
∑ μi+ (t ) + μi− (t ) ⩽ Numdr
(27)
t
μi+ (t ) ,
μi− (t )
are binary variables where λ is the IDR participation rate, of invoking IDR and one of them should be 0 at time t. Numdr denotes the maximum scheduling times of IDR. (6) gas storage The gas stored in periods with redundant power generation can be supplied as required, which alleviates the mismatch of supply and demand. The GS model is model as: ch dis Si, GS (t ) = Si, GS (t − 1) + (χich Pich·ηGS + χidis Pidis / ηGS )·Δt
(28)
γ minScap ⩽ Si, GS (t ) ⩽ γ maxScap
(29)
Si, GS (1) = Si, GS (24)
(30)
Pich
Pidis
and are respectively the charging and discharging gas in where the hub i at time t. χich and χidis are the binary variables related to the
Fig. 2. The typical port district in China with three EHs. 5
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4.1. Settings of key parameters
Table 1 Parameters of the shore-side power.
The energy consumptions of the port can be generally divided as terminal operations, municipal services, business culture and daily residential consumption [34]. Due to the different load characteristics in various industries, loads of different industries are added up according to the energy types. As shown in Fig. 3, the profiles of electricity, heat, cool, ships and wind power in three EHs on typical days are presented. With regard to the prediction of ships’ load, the Monte Carlo approach has been employed to deal with the uncertainties of ships using shoreside power. On the basis of statistical regularity of ships calling at ports, the arriving time, in port duration and magnitude of shore-side power are assumed to follow normal distributions, negative exponential distributions, and normal distributions, separately [35–37]. The parameters of the ships using shore-side power are listed in Table 1. Meanwhile, it is assumed that the ships will use the shore-side power as soon as they call at the ports and it is regarded as the transferable load to participate in the IDR program. The parameters of the facilities within EH and electrical lines are given in Table 2. In addition, the following main assumptions have been made to set the problem data:
Average power demand (kW)
Average porting time (h)
Bulk ship Cruise ship Container vessel
400 800 1200
10 5 10
Table 2 Parameters of the facilities and electrical lines. Type
Maintenance cost (¥/ kW·h)
Life (year)
CCHP PtG AC GB GS Electrical Line
0.032 0.028 0.021 0.026 – –
30 15 15 20 10 40
EHs are planned separately without IDR and EI. Case C: EH with IDR and without EI. Each EH is still planned separately, but IDR is considered in each EH to achieve hourly load balances. The interconnection between EHs is not considered as in Case B. Case D: EH without IDR and with EI. Three EHs are planned together with interconnection. However, IDR is not employed here. Case E: EH with both IDR and EI. Both IDR and EI are considered in this case to achieve energy balance in three port areas.
(1) There is no heating demand in summer and no cooling demand in winter. Electrical demands are the same in three periods. (2) The price of electricity ( pele ) is 1.35, 0.90, and 0.47 ¥/ kW·h respectively for peak, flat and valley periods. The price of natural gas ( p gas ) is 2.70/m3. (3) The power loss rate (δ) of electric lines is 0.03%/km. The distances between each EHs are 6 km, 6 km, and 5 km, as shown in Fig. 2. According to the cost and capacity of the typical power line, the linear relationship between the price per length and the rated capacity is: cap ωline = 0.405PiLine − j + 49.19
Ship Type
The configuration and sizing results are presented in Tables 3 and 4. (1) Comparison of case a and case B There is no IDR and EI in these two cases. In contrast with the traditional energy supply solution (Case A), though the installation of the CCHP and other devices increases the investment and maintenance costs, the energy cost and the penalty cost for wind power curtailment drop drastically. As a result, the total annual cost is reduced by 25.96%. Thus, the IPES shows much better economic benefits than both the conventional centralized energy system in the port area.
(35)
The optimization model is implemented in MATLAB with YALMIP and CPLEX solvers on an Intel i5 laptop with 8 GB RAM. It takes less than 15 s when running the simulation. 4.2. Results of EH configuration and sizing
(2) Comparison of case B and case C
In order to illustrate the effectiveness of the IPES with IDR and EI, five cases are conducted in this study.
This comparison illustrates the influence of IDR on the EHs configuration and the planning cost. The total cost is reduced by 2.76% after introducing IDR despite there is an additional cost of dispatching it. Furthermore, the reduction of the investment cost reflects the substitute effect of IDR in specific devices, which finally results in the decrease of installed capacity. However, this impact is not apparent. The energy and maintenance costs are reduced by 9.58% and 2.63%,
Case A: Conventional energy supply solution. There is no EH in each port area. Hence, heating loads and cooling loads are provided by the GB and the AC, separately. The wind power and distribution network in the port area will supply the electric demand. Case B: EH without both IDR and EI. The EHs are implemented to satisfied the multiple energy demands in the IPES. However, three
Fig. 3. The profiles of load demand and wind power in three port areas. 6
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Table 3 Optimal capacity of eh components in five cases. Case
Port
Capacity/kW CCHP
GB
PtG
AC
GS
Line1-2
Line1-3
Line2-3
A
Port1 Port-2 Port −3
– – –
22,000 21,000 16,000
– – –
5350 8025 8500
– – –
–
–
–
B
Port-1 Port-2 Port −3
19,000 19,000 19,500
8000 9000 3000
7000 12,500 0
1050 3468 3924
2000 7000 0
–
–
–
C
Port −1 Port −2 Port −3
19,000 19,000 19,500
7000 8000 3000
7000 12,500 0
793 3093 3018
2000 7000 0
–
–
–
D
Port −1 Port −2 Port −3
19,500 19,500 19,500
8000 8000 13,000
0 0 7000
1050 3449 3924
0 0 0
0
5692
18,353
E
Port −1 Port −2 Port −3
19,500 19,500 19,500
7000 7000 11,000
0 0 0
774 2653 3018
0 0 0
3711
8000
16,872
simultaneously. In the following, the analyses of the daily optimal operation conditions of three EHs in case E are reported, as illustrated respectively in Fig. 4(a), (b) and (c). First, the cooling balance of the EHs in summer is shown in Fig. 4(a). The cooling loads in each EH are supplied by the CCHPs and the ACs. From 1:00 to 7:00, there is no cooling demand in the EH 1 and the EH 3. The AC in the EH 2 operates as the main cooling supply device. As the electricity price and cooling demand grow simultaneously, the CCHPs provide the majority of the cooling power between 9:00 and 22:00 and the ACs are used as the backup cooling source when the CCHPs cannot satisfy the cooling demand at its maximum. Meanwhile, IDR provides both positive and negative outputs in load shifting. However, due to the constraint to the IDR dispatching times and participating rate, IDR can be only invoked at a few critical operation points. The heating balance of the EHs in winter is illustrated in Fig. 4(b). Similar to the cooling balance, the heating loads in each EH are provided by the CCHPs and the GBs. The CCHPs do not operate from 23:00 to 7:00 as the electricity tariff is low, and the heating demands are satisfied by the GBs (EH 3). The CCHPs provide the majority of the thermal power between 8:00 and 22:00. When the CCHPs cannot meet thermal demand, the GBs are used as the alternative heating device. Meanwhile, IDR plays the same role as it in cooling energy operation. With regard to the electricity balance in Fig. 4(c), it is interesting to analyze the electricity exchange between each EH. It can be seen that when the electrical network exists, the advantage of EI appears. During most of the time between 1:00 and 8:00, there is sufficient wind power in port area 1 and 2. Meanwhile, IDR plays a positive role in wind power consumption. However, in order to satisfy the considerably high electrical load in the port area 3, the surplus of electrical power in port area 1 and port area 2 are injected into the electrical network (Pi-j, in red). Besides, when it still cannot satisfy the load demand, it is optimal to purchase the electricity (Ele_pur, in the darkest blue) from the grid due to its low price. From 9:00 to 22:00, the CCHPs in three EHs are almost operating at their maximum output. The IDR is preferentially used to offset part of the purchased electricity. With regard to the excess electrical power in port area 1 or port area 2, one the one hand, it is converted into cooling power through the ACs to meet the cooling demand. On the other hand, it is transmitted to port area 3 through the electrical network. As the electricity price goes down at 23:00 and 24:00, the CCHPs are shut down, and their output is covered by purchasing electricity from the grid. It is also noted that with regard to IDR in port area 3, there is much more load rebound at 23:00. This is mainly due to a large amount of curtailable load in the previous three hours.
Table 4 costs Comparison Of Five Cases (Million yuan/¥). Case
Cinv
CE
Cmat
CDR
Ccurt − wind
Ctot
A B C D E
64.32 293.65 290.60 296.16 286.72
5008.34 3537.93 3198.91 3230.30 2946.54
29.34 113.86 110.76 105.80 103.80
– – 239.59 – 221.65
238.90 8.85 4.98 0.00 0.00
5340.90 3954.29 3844.83 3632.31 3558.99
separately, indicating that IDR has played a positive role in saving power or gas resources and optimizing the outputs of the devices. (3) Comparison of case B and case D In case D, three EHs are connected to each other by the electrical networks. Therefore, this comparison shows the impact of EI. The total cost is decreased by 8.14% after adopting EI between EHs despite there is an additional cost of line investment. Results in Table 3 suggest that EI eliminates the redundant configuration of PtG and GS in some port areas, but the capacity of CCHP increases. Furthermore, the energy and maintenance costs are dropped by 8.71% and 7.02%, respectively. It should be noted that there is no charge for wind power curtailment, which indicates the advantage of EI for promoting inter-regional wind power accommodation. (4) Comparison of the Last four cases As shown in case E, when both IDR and EI are considered, the total cost is reduced by 9.99% compared with case B. The reduction is more apparent with regard to the cost of investment and energy consumption. Besides, the PtG and the GS are not installed any more though wind power is fully consumed. In summary, the implantation of EHs can reduce the total planning cost compared with the traditional energy supply solution in the IPES. After adopting IDR and EI between EHs, the total cost can be furtherly reduced, respectively. EI performs better than IDR in the total cost saving. Besides, if IDR and EI are considered together, the advantage of the two can be accumulated, with the total cost dropping by nearly 10%.
4.3. Typical daily operating conditions of EHs The optimal energy management strategies of the IPES are obtained 7
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Fig. 4. The daily energy balance in three EHs in case E (a) Cooling balance in summer (b) Heating balance in winter (c) Electrical balance in summer.
effect of some critical parameters and the uncertainty of ship using shore-side power on the IPES planning results. All the analyses are performed on the EH with both IDR and EI case (Case E) by taking stepwise values for one item, and with the remaining items holding the nominal values.
In summary, the IPES with EHs can achieve the cascade utilization of energies and the complementary of multi energies, which will bring economic benefits to the port areas. Both IDR and EI can alleviate the mismatch problem between energy output and peak-valley load demand in the PIES.
(1) Energy prices 4.4. Sensitivity analysis This part investigates the changes in the total cost and the capacities Sensitivity analyses are conducted in this section to investigate the 8
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Fig. 5. Planning Cost and capacities of CCHPs in three areas under varying energy prices.
Fig. 7. Planning cost under varying ship porting time and shore-side power (a) Investment cost (b) Total cost.
prices. In addition, it is obvious that the total cost is more sensitive to the variations in natural gas prices compared to the electricity price. This is evidently expected due to the high penetration of natural gas in the studied system. (2) line loss rate and IDR participation rate The changes in planning costs are investigated when the line loss rate or IDR participation rate changes. As shown in Fig. 6(a), the investment cost decreases as the IDR participation increases, but it remains nearly unchanged with the increasing line loss rate. This is mainly because the capacity of the devices is related to the total load level. When IDR is introduced, the peak load decreases and the demand profile becomes flattered, which ultimately results in size selection. The initial drop in investment gives way to saturation as the IDR allocation increases, which means that the effect of IDR on equipment optimal selection is limited. It is noted that there would be some load rebound after curtailment, which causes the size of ACs to increase. As a result, the investment cost increases slowly when the IDR participation rate is over 45% (nearly). By contrast, Fig. 6(b) illustrates that the total cost decreases continuously with the increasing IDR participation rate at a specific line loss rate per km. The reason is that IDR can significantly lessen the cost of energy, which results in a reduction in the total cost. However, the total cost is almost unchanged with the decreasing line loss rate because the investment cost for the electrical networks only takes up a quite
Fig. 6. Planning Cost under varying line loss rate and IDR participation rate (a) Investment cost (b) Total cost.
of CCHPs in three areas when the price of electricity or natural gas changes. As shown in Fig. 5, the capacities of CCHPs increase as the electricity price grows while they decrease (from 90% to 110%) as the natural gas price rises. However, these changes are not manifest mainly because of the large investment cost of CCHP. With regard to the total cost, results demonstrate that the highest variations of total costs (about 16%) are due to the 20% variations (from 50% to 70%) in natural gas 9
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small share in the total planning cost.
on integrated energy service under Grant SGJS0000QXWT1800670.
(3) Shore-side power and ship port time
References
This section illustrates the impact of the uncertainty of ships using shore-side power on the planning cost. It can be seen from Fig. 7(a) that the investment cost remains around 280 million yuan when the average port time or average shore-side power varies from −20% to 20% as its original value. Since investment cost the CCHPs is large, it is more economical to buy the electricity from the grid rather than upgrade the size of them. As a result, the small variations (shown in different colors) could be the changes of some other devices and the electrical line capacities, but they only account for a small percentage of the entire investment cost. However, the total cost changes significantly as the average in port time of ships or average shore-side power increases. This is mainly because the uncertainty of ships using shore-side power has a much greater impact on the energy cost rather than the investment cost, and the part of energy cost leads to the rise of total planning cost.
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Integrated energy system modeling of China for 2020 by incorporating demand response, heat pump and thermal storage. IEEE Access 2019;7:40095–108. [8] Widl E, et al. Studying the potential of multi-carrier energy distribution grids: a holistic approach. Energy 2018;153:519–29. [9] Pazouki S, Haghifam M. Optimal planning and scheduling of energy hub in presence of wind, storage and demand response under uncertainty. Int J Electr Power Energy Syst 2016;80:219–39. [10] Zhang X, Shahidehpour M, Alabdulwahab A, Abusorrah A. Optimal expansion planning of energy hub with multiple energy infrastructures. IEEE Trans Smart Grid 2015;6(5):2302–11. [11] Beigvand S, Abdi H, La Scala M. A general model for energy hub economic dispatch. Appl Energy 2017;190:1090–111. [12] Martin G. Integrated modeling and optimization of multi-carrier energy systems [Ph.D. dissertation]. Zurich, Switzerland: ETH Zurich; 2007. [13] Ma L, Liu N, Zhang J, Wang L. Real-time rolling horizon energy management for the energy-hub-coordinated prosumer community from a cooperative perspective. IEEE Trans Power Syst 2019;34(2):1227–42. [14] Gao M, Wang K, He L. Probabilistic model checking and scheduling implementation of an energy router system in energy internet for green cities. IEEE Trans Ind Inf 2018;14(4):1501–10. [15] Dolatabadi A, Mohammadi-ivatloo B, Abapour M, Tohidi S. Optimal stochastic design of wind integrated energy hub. IEEE Trans Ind Inf 2017;13(5):2379–88. [16] Wasilewski J. Integrated modeling of microgrid for steady-state analysis using modified concept of multi-carrier energy hub. Int J Electr Power Energy Syst 2015;73:891–8. [17] Bahrami S, Safe F. A financial approach to evaluate an optimized combined cooling, heat and power system. Energy Power Eng 2013;05(05):352–62. [18] Huang J, Zhou H, Wu Q, Tang S, Hua B, Zhou X. Assessment of an integrated energy system embedded with power-to-gas plant. In: 2016 IEEE Innovative Smart Grid Technologies-Asia (ISGT-Asia). Melbourne, Australia; 2016, pp. 196–201. [19] Jabbari B, Tahouni N, Ataei A, Panjeshahi M. Design and optimization of CCHP system incorporated into kraft process, using Pinch Analysis with pressure drop consideration. Appl Therm Eng 2013;61(1):88–97. [20] Salimi M, Adelpour M, Vaez-ZAdeh S, Ghasemi H. Optimal planning of energy hubs in interconnected energy systems: a case study for natural gas and electricity. IET Gener Transm Distrib 2015;9(8):695–707. [21] Maroufmashat A, et al. Modeling and optimization of a network of energy hubs to improve economic and emission considerations. Energy 2015;93:2546–58. [22] Jabir Hussein, et al. Impact of demand-side management on the reliability of generation systems. Energies 2018;11(8):2155. [23] Teh Jiashen. Adequacy assessment of wind integrated generating systems incorporating demand response and battery energy storage system. Energies 2018;11(10):2649. [24] Teh Jiashen, et al. Composite reliability evaluation of load demand side management and dynamic thermal rating systems. Energies 2018;11(2):466. [25] Sheikhi A, Rayati M, Bahrami S, Mohammad Ranjbar A. Integrated demand side management game in smart energy hubs. IEEE Trans Smart Grid 2015;6(2):675–83. [26] Bahrami S, Sheikhi A. From demand response in smart grid toward integrated demand response in smart energy hub. IEEE Trans Smart Grid 2015;7(2):650–8. [27] Alipour M, Zare K, Abapour M. MINLP probabilistic scheduling model for demand response programs integrated energy hubs. IEEE Trans Ind Inf 2018;14(1):79–88. [28] Parise G, Honorati A. Port cranes with energy balanced drive. In: 2014 AEIT Annual conference-from research to industry: the need for a more effective technology transfer (AEIT), Trieste, Italy; 2014, pp. 1–5. [29] Götz M, et al. Renewable Power-to-Gas: a technological and economic review. Renewable Energy 2016;85:1371–90. [30] Seddiek I, Mosleh M, Banawan A. Fuel saving and emissions cut through shore-side power concept for high-speed crafts at the red sea in Egypt. J Mar Sci Appl 2013;12(4):463–72. [31] Wang J, Zhong H, Ma Z, Xia Q, Kang C. Review and prospect of integrated demand response in the multi-energy system. Appl Energy 2017;202:772–82. [32] Wang Beibei, et al. Chance constrained unit commitment considering comprehensive modelling of demand response resources. IET Renewable Power Generation 2016;11(4):490–500. [33] Zhou Z, Liu P, Li Z, Ni W. An engineering approach to the optimal design of distributed energy systems in China. Appl Therm Eng 2013;53(2):387–96.
5. Conclusion Ports are vital gateways and corridors to the rest of the world. In this paper, a framework and EH based planning model are proposed for IPES. Five different port energy system schemes are compared to determine the optimal planning strategy for IPES. In addition, the optimal operation of the IPES is performed in detail and the sensitivity analyses are conducted to show the robustness of the simulation results. The main research findings can be summarized as follows. (1) Simulation results illustrate that the proposed IPES (case B) shows better economic and synergetic performances than the traditional port energy system (case A), with the total planning cost dropped by nearly 26%. (2) When IDR and EI are considered, it is beneficial to incorporate both of them (case E) to minimize total cost at the system planning stage. In this case, it avoids installing redundant PtG and GS within each EH and furtherly reduces the total planning cost by nearly 10%. It is also worth mentioning that if the two solutions are considered separately, EI (case D) performs better than IDR (case C) in the total planning cost saving. (3) Moreover, sensitivity analyses show that the performance of IDR on capital investment cost saving is limited but the total cost declines significantly as the IDR participation rate grows. However, with regard to EI, the changes in line loss rate have little influence on both investment cost and total cost. (4) Finally, the uncertainties of the ships using shore-side power have little influence on the investment cost, but they can increase the energy cost as well as the total annual cost when the average in-port time or shore-side power rises. The purpose of this study is to lay a framework for modeling IPES. More work has been done than is reported here and more work including heating networks and uncertainty of renewable energy remains to be done. Declaration of Competing Interest The authors declare no conflict of interest in this work. Acknowledgment This work is supported in part by the China Scholarship Council and British Council through the Newton Fund, in part by the 2018 State Grid Corporation Science and Technology Program: Research on port energy efficiency assessment and sustainable improvement model based 10
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Tianjin port main channel. J Wuhan Univ Technol (Transp Sci Eng) 2008;32(2). in Chinese. [37] Yu J, et al. Ship arrival prediction and its value on daily container terminal operation. Ocean Eng 2018;157:73–86. [38] Li Z. Decentralized contingency-constrained tie-line scheduling for multi-area power grids. IEEE Trans Power Syst 2016.
[34] Wang X. Study on the optimization and planning of the distributed energy sources in Tianjin port [M.S. thesis]. Beijing, China: North China Electric Power University; 2017 [in Chinese]. [35] HlaingYin Ni Ni, Hu Q, Chaojian Shi. Studying probability of ship arrival of Yangshan port with AIS. TransNav-Int J Mar Navigat Saf Sea Transp 2011;5(3):291–4. [36] Li Y, Liu J. Distribution regularity analysis of ship arrival and department at the
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Contents lists available at ScienceDirect
Electrical Power and Energy Systems journal homepage: www.elsevier.com/locate/ijepes
Integrated port energy system considering integrated demand response and energy interconnection
T
⁎
Tianli Songa,b, Yang Lia, , Xiao-Ping Zhangb, Cong Wub, Jianing Lib, Yi Guoc, Haifei Gua a
School of Electrical Engineering, Southeast University, Nanjing 210096, China Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Birmingham B15 2TT, UK c Logistics Engineering College, Shanghai Maritime University, Shanghai 201306, China b
A R T I C LE I N FO
A B S T R A C T
Keywords: Integrated port energy system Energy hub Integrated demand response Energy interconnection Shore-side power
This paper proposes a framework for modeling an integrated port energy system (IPES). A configuration and sizing model of energy hub (EH) is built for the port area considering integrated demand response (IDR) and energy interconnection (EI). A comparative study of five different planning scenarios, traditional energy supply solution, EH solution, EH with IDR, EH with EI, and EH with both IDR and EI are conducted to identify the best option for minimizing the total planning cost of an IPES. Furthermore, the daily operating conditions of EHs are discussed and sensitivity analyses with varying energy prices, line loss rates, and IDR participation rates are performed to demonstrate the robustness of the model. The impacts of the uncertainties of ships using shore-side power on planning costs are also analyzed. Based on the proposed methods, numerical simulation results show the effectiveness of the EHs with multiple energy infrastructures in the IPES. In addition, after considering IDR and EI, the total planning cost decreases significantly, which demonstrates the necessity and benefits of IDR and EI.
1. Introduction Ports are the interface of maritime transport and are considered as the engines of the port cities. They play a significant role to support the exchange of goods and in linking peripheral areas with the mainland. According to the statistics of the International Maritime Organization (IMO), over 90% of the world’s trade is carried by sea and it is, by far, the most cost-effective way to move goods and raw materials around the world [1]. At the same time, ports are primarily industrial and commercial areas with significant energy consumptions. All transportation sectors including road, train, air, and sea account for nearly 20% of total energy consumption and sea mode is responsible for about 4% of them [2]. However, conventional port energy infrastructures like electricity, heat and cool deal independently which makes the system less robust and low efficient with high operational cost. Meanwhile, due to the vast volumes of crude oil in the form of imports or exports, the port environment is susceptible to heavy pollution from carbon dioxide (CO2) emissions. Therefore, it is necessary and urgent to find an integrated energy solution with both economic and environmental objectives for achieving green ports [3–6]. Integrated energy system (IES), which is defined as an inventible
⁎
solution for a future energy system, expected to increase sustainability, reliability, security, efficiency, and resiliency of the conventional energy system [7,8]. It is also aimed to reduce air pollutions and support multiple transportations. Accordingly, an attendant energy hub (EH) is employed for achieving the interaction between different energy carriers [9–11]. Unlike traditional energy systems depending on singleenergy-supply equipment, EH can serve as a functional unit where different energy sources are converted, stored, and dispatched [12]. For a single EH, it could range from the aggregation of energy sources and loads at the customer level to the aggregation of distributed energy resources and customer clusters, and it can even be extended to an entire city [13]. As for the EH in the port area, since there is a variety of different energy sources and complicated working conditions, it needs the configuration and sizing of EH more optimal. Generally, the modeling of EH can be expressed using a coupling matrix [11,14]. Ref. [15] presents an EH model embedded with a wind power unit based on the general model. A novel approach for EH modeling based on graph and network theory is introduced in [16]. With regard to the configuration and sizing of the EH, an approach in [17] considers the planning capacities of combined heat and power (CHP) unit, gas boiler (GB), absorption chiller and energy storage equipment collaboratively. The result illustrates that the proposed
Corresponding author. E-mail address: [email protected] (Y. Li).
https://doi.org/10.1016/j.ijepes.2019.105654 Received 18 June 2019; Received in revised form 22 October 2019; Accepted 25 October 2019 0142-0615/ © 2019 Elsevier Ltd. All rights reserved.
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pCCHP / GB / PtG / GS / AC unit maintenance cost for each EH component (¥/kW h) pcurt − wind unit punishment for wind curtailment (¥/kW h) r interest rate (%) energy conversion rate (%) η
Nomenclature Acronyms AC CCHP CFR EH(s) EI GB GS IDR IPES PtG
air conditioning combined heat, cooling, heating, and power capital recovery factor energy hub(s) energy interconnection gas boiler gas storage integrated demand response integrated port energy system power to gas
Variables
Ctot Cinv CE Cmat CDR Ccurt − wind Picurt − wind PiEI ,j Piele ele Pi cchp PiPtG PiWind qiAC cool qi cchp
Indices and sets
i/j t T s k ϕD
port area time time periods that DR can be invoked different seasons energy type aggregation of sizing options for various devices
total cost (¥) investment cost (¥) energy cost (¥) maintenance cost (¥) demand response cost (¥) wind curtailment cost (¥) the amount of wind curtailment (kW) power exchange between port area i and j (kW) power trading from the grid (kW) power generated by CCHP (kW) electricity consumed by PtG (kW) actual utilized wind power (kW) cool supplied by AC (kW) cool supplied by CCHP (kW)
qiGB heat qi cchp Si, GS Vigas ViPtG , gas χω, i
heat supplied by GB (kW) heat supplied by CCHP (kW) capacity of GS (m3) purchase volume of natural gas (m3/h) natural gas produced by PtG (m3/h) binary variables of electrical line and EH components for the size option ω (1/0) Δdri+, k the amount of increased load (kW) the amount of decreased load (kW) Δdri−, k ΔREloadi,+k, t the amount of rebound load (kW) αβ γ dispatch factor for injected energy (%)
Constants
pdr unit DR cost (¥/kW h) CiCCHP / GB / PtG / GS / AC constructing cost for each EH component (¥/kW h) number of days in the season s Ds heating value of natural gas (kW h/m3) Hgas load demand of k in port area i (kW) Lik pele price of electricity (¥/kW h) p gas price of natural gas
(1) This study initially proposes the framework of the IPES, which considers multiple energy resources together in the EHs. Further, configuration and sizing models of EH are built to size the energy components optimally. (2) To enhance economic efficiency and system flexibility, this study incorporates IDR and EI to integrate different energy into the IPES. The effectiveness of different scenarios with and without IDR and EI are evaluated. (3) Sensitivity analyses are performed to demonstrate the robustness of the IPES planning method with reference to different electricity and natural gas price fluctuation rates, line loss rates, and IDR participation rates. In addition, the impacts of uncertainties of ships using shore-side power on the planning cost are analyzed.
approach can reduce the total cost more effectively compared with the traditional method. Ref. [18] shows an IES embedded with a power-togas (PtG) unit, which converts electrical power to a gas fuel when there is surplus power from renewable energy generation. Ref. [19] sets the minimum annual cost and maximum exergy efficiency as the goal to optimize the capacity of each device in the cogeneration system. Meanwhile, in order to reduce costs and enhance energy utilization efficiency, various economic and technical factors should be taken into consideration. Ref. [20] considers the power and gas interconnection between EHs. Ref. [21] testifies the multi-energy complementary and multi-EH coordination would be the most economical and environmental among all the operation plans. Also, a coordination framework for tie line scheduling and power dispatch to operate multi-area systems is described in [38]. Furthermore, with the development of energy internet, demand-side management plays a more critical role than ever [22]. It aims at enhancing economic efficiency and system flexibility. Some literature [23,24] focused on the applications of the demand response (DR) in an IES. The concept of IDR is introduced in [25] to present the switching between energy resources. Ref. [26] shows that an IDR program in a smart EH can reduce the dependency on customers’ motivation. For both electrical and thermal load, a new framework of IDR is proposed in [27], which can efficiently reduce the total cost of the EH. However, to the best knowledge of the authors, there are few studies related to the modeling of an IPES. Therefore, the objective of the study is to lay a framework for modeling the IPES, which aims at minimizing the total planning cost of the system with lower emission levels and higher energy efficiency. In contrast to previous work, the main contributions of this paper can be summarized as follows.
The rest of the article is organized as follows. A framework of the IPES and the model of EH are presented in Section 2. Section 3 formulates the EH-based planning problem and its associated constraints. Section 4 presents the case studies with and without IDR and EI, followed by discussions of operating conditions of EHs and sensitivity analyses. Finally, the conclusions drawn from the study are presented in Section 5.
2. The EH model with IDR and EI in IPES 2.1. Description of the IPES Ports are intensive energy extended areas with multiple systems that require many tens of megawatts. Generally, the area near the coastline of the port can be the best area for the utilization of wind energy 2
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heating load demands in the port area i. It is worth noting that electrical load means the total electricity consumption in the port area except for the load of ships using shore-side power. PiPtG is the load of the PtG device. Piele denotes the electricity purchased from the power grid for the hub i , Vigas , pur denotes the gas fuel used by the CCHP unit and GB. The dispatch factor α and β define the dispatch of wind power and purchasing power for both electrical load and the AC. H gas (9.78 kW·h/m3) is the heating value of natural gas, ηGB (0.92) is the efficiency of the GB, and η AC (4.00) stands for the coefficient of performance (COP) of the AC. The CCHP unit is the main converter equipment, characterized by cool the conversion efficiencies, which stand for gas to cold (ηcchp , 1.2) and
resources, which is suitable for developing medium and small wind power generation. With regard to the energy consumptions in IPES, ports can be deployed with the topologically distinct terminal for passengers, for logistics commercial and industrial shipbuilding activities, unloading and loading goods in container terminals, rail yards, air condition warehousing, commercial buildings and residential areas [5,28]. From the perspective of energy production and consumption, it can be summarized as port appliances consume electricity or natural gas to provide electrical, heating and cooling demands. Therefore, according to the energy generation, demand and conversion characteristics in port areas, the framework of the IPES is proposed in this paper. Fig. 1(a) is the whole structure of IPES and Fig. 1(b) shows the model of EH within the IPES. The entire IPES composed of three EHs is connected with the port distribution power network and natural gas network. Each EH is in charge of the dispatch of the regional energy system. Within each EH, there are a combined cooling, heating, and power (CCHP) unit, a PtG unit, an electric air conditioning (AC) unit, a GB unit, and a gas storage (GS) unit. They feed all the energy activities including unloading and loading goods and shore-side power supply in the terminals. The wind power generation unit (Piwind ) is connected to the local energy supply bus. Meanwhile, with the increasing penetration of variable wind power in the port area, the operational stability of the IPES may become a problem. Therefore, PtG technology is introduced here to converts surplus electricity into natural gas for storage and utilization [29]. As a result, a GS device is also included in EH. It is noted that the shore-side power system is an important part of the IPES. With the promotion of electric power substitution in the port area, more and more ships will use shore-side power instead of their auxiliary engines to continue operations such as loading, unloading, hoteling, communicating, and lighting when staying in the port. It is widely seen as an effective method to improve the environment in the port area [30]. The primary function of the IPES is to combine the multi-energy system with the power system in the port area and adopt intelligent dispatching means to resolve the contradiction between the intermittent power generation of renewable energy systems and the continuity of load power consumption. Multi-energy systems can be connected to the distribution network in different forms, which can achieve mutual benefits among different energy sources. In addition, it can reduce the emission levels of pollutants in the port area and improve the utilization efficiency of various energy sources. In other words, IPES can be seen as the application of the IES in the port area.
heat , 0.9) separately. α and β mean the dispatch factor gas to thermal (ηcchp of wind power and purchased electricity for shore-side power, electrical load, and AC. Meanwhile, the dispatch factor λ denotes the natural gas consumed by both CCHP and GB. Within each EH, the energy management system can be employed to control the dispatch factor to optimize the EH schedule [26]. The last but not the least, PiEI , j denotes the energy exchange between the EH i and the EH j through the electrical network. In addition, the PtG unit and the GS unit are included in EH to produce and store natural gas. The gas balance can be modeled as follows.
2.2. EH model As shown in Fig. 1(b), the hourly electrical balance between input energy and output demand can be expressed as follows:
Liele + Liship + PiPtG ele = αPiele + Pi cchp + βPiWind + ∑ (PiEI ,j ) j ele = αPiele + γηcchp H gasVigas + βPiWind + ∑ (PiEI ,j ) j
(1)
The cooling balance is defined as:
Licool
cool = qi cchp + qiAC cool gas gas = γηcchp H Vi + ((1 − α ) Piele + (1 − β ) PiWind ) η AC
(2)
The heating balance can be express as:
Liheat
heat = qi cchp + qiGB heat gas gas = γηcchp H Vi + (1 - γ ) ηGB H gasVigas
where
Liship ,
Liele ,
Licool ,
and
Liheat
(3)
Fig. 1. The proposed framework for IPES (a) The whole structure of IPES (b) The EH model.
denote the ship, electrical, cooling, and 3
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3. EH configuration and sizing model
(4)
PtG where Vigas , pur is the per-unit-time purchase volume of gas. Vi, gas denotes gas ̇ the natural gas produced by the PtG unit and Vi, store are the vector of the storage gas variation.
3.1. Objective function It is assumed that the whole IPES is managed by a single integrated energy company in the port area. As a result, the objective of the optimization is to minimize the total cost of the company for planning the IPES. In this paper, a time horizon composed of three typical days is considered, representing the average daily electrical, heating and cooling demands in each season: the cooling season (s = c), heating season (s = h), and transition season (s = t). Thus, the objective function is expressed as follows:
2.3. IDR model DR can increase system flexibility on the demand side by reducing peak power demand or temporarily shifting as responses to the price signals or incentive mechanisms. Taking advantage of the complementarities of different inertia of multi-energy, IDR is aimed at fully exploiting the capabilities of all the customers [31]. The coupling between different energy carriers enables customers to participate in the IDR programs not only by means of load shifting but also by switching the source of their consumed energy. As a result, the economical and reliable operations of the IPES can be improved. Based on the traditional DR model and EH model in the previous section, the IDR is modeled according to different characteristics.
(9)
min Ctot = Cinv + CE + Cmat + CDR + Ccurt − wind
∑
Cinv =
i
CCHP (C CCHP χ CCHP ) + CFRGB (C GB χ GB )+ ⎞ ⎛CFR i i ω, i ω, i ⎜ CFRPtG (C PtG χ PtG ) + CFRGS (C GS χ GS )+ ⎟ i i ω, i ω, i ⎟ ⎜ Line ⎟ ⎜ CFR AC (CiAC χ AC ) + CFRLine (CiLine χ j ω, i ω, i - j ) ⎠ ⎝
(10)
24
CE =
Ds · ∑ (pele (t ) Piele (t ) + p gas (t ) Vigas , pur (t ))
∑ ∑
(1) Transferable IDR
s = c, h, t
i
t=1
(11)
24
Transferable IDR refers to the starting time of using energy that can be transferred but the usage period is fixed. Usually, the transferable load should not be interrupted once started, and the daily load remains constant. Its model can be expressed as follows:
Δdr _transi+, k, t + Nx = Δdr _transi−, k, t , ∀ t ∈ T − Nx
i
CDR =
i
,∀t∈T (6)
where Δdr _shiftk + and Δdr _shiftk− denote the amount of increased load and decreased the load in shiftable IDR. (3) Curtailable IDR Curtailable IDR means that the loads can be curtailed in a specific period, but it may cause the load rebound in the following hours. The more loads are curtailed, the more rebound appears.
ΔREloadi,+k, t = φ1 Δdr _curti−, k, t − 1 +φ2 Δdr _curti−, k, t − 2
CFR = (7)
−
+
+
24
∑ Picurt−wind (t ) ⎞⎟ t
⎠
(14)
r (1 + r )life (1 + r )life − 1
(15)
(1) Demand-supply balance Apart from the demand-supply balance constraints in Eqs. (1)–(3), IDR should be considered in each energy balance equation, which can be expressed as:
= (Δdr _transi+, k + Δdr _shifti+, k + ΔREloadi+, k ) Δdr _curti−, k )
⎛ Ds ·⎜pcurt − wind ⎝
(13)
⎠
t
3.2. Constraints
IDRi, k = Δdri+, k − Δdri−, k Δdr _shifti−, k
s = c, h, t
24
∑ (Δdri+ (t ) + Δdri− (t )) ⎞⎟
where r (5% in this study) is the interest rate and life is the lifespan of the facility.
Δdr _curtk−
denote the amount of rebound load where ΔREloadk and and curtailed load. φ1 φ2 φ3 are the load rebound coefficients at the time t − 1, t − 2 , and t − 3. The values of them are set 0.6, 0.3 and 0.1 separately in this study [32]. It is assumed that every energy type k has these three kinds of IDR presented above so the responsiveness of IDR of an energy source can be summarized as:
(Δdr _transi−, k
⎛ Ds ·⎜pdr ⎝
(12)
where each Ci and CFR are the installation cost in area i and the capital recovery factors (CFR) of power lines and EH components. CFR can be calculated using Eq. (15). A binary decision variable χi ∈ {0, 1} is assigned to each available sizing option of the component, once a candidate energy carrier of a certain capacity is installed, the investment state of the corresponding size option is changed to 1. CE is the energy cost of the system, which includes the grid power purchases and the fuel cost of natural gas energy. Ds is the number of days in different seasons (108, 105, and 152 for winter, summer and transition periods, separately). p gas and pele denote the gas and electricity price at time t separately. Cmat represents the maintenance cost for EH components. pcchp , pGB , p PtG , and p AC denote the maintenance cost per unit of the different facilities. CDR represents the cost for the IDR programs and pdr (0.3 ¥/ kW·h in this study) is the price of invoking the IDR per unit. Also, due to the problems of renewable energy curtailment in the port area, the target function involves the punishment of not using wind power Ccurt − wind and it is related to the penalty factor pcurt − wind (1.5 ¥/ kW·h in this paper) and the amount of wind curtailment P curt − wind .
T
+
cchp GB ⎛ pcchp Pi (t ) + pGB Pi (t ) ⎞ Ds · ∑ ⎜ PtG P PtG (t ) + p AC P AC (t ) ⎟ i i t = 1 ⎝+ p ⎠
∑ ∑ i
Shiftable IDR has no limitation on the power consumption at each time as long as it can satisfy the total load demand within a predetermined time interval.
+φ3 Δdr _curti−, k, t − 3, ∀ t ∈ T + 3
s = c, h, t
Ccurt − wind =
(2) Shiftable IDR
t=1
s = c, h, t
∑ ∑
(5)
where Δdr _transi,+k and Δdr _transi−, k denote the amount of increased load and decreased the load in transferable IDR separately.
∑ (Δdr _shifti+,k,t − Δdr _shifti−,k,t )= 0
∑ ∑
Cmat =
∑ Demandik = ∑ Supply ik − IDRi, k = ∑ Supply ik + Δdri−, k − Δdri+, k
(8) 4
(16)
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where ∑ Demand ki and ∑ Supply ki are the total energy load demand and supply of energy k considering power interconnection in the port area i . It is noteworthy that all the electrical, cooling and heating balance in this study can be summarized as Eq. (16).
dis ch storage state. ηGS and ηGS (both of them are set 0.95 in this study) denote the charging and discharging efficiency, and Δt is the time interval. Si, cap is the stored capacity. γ min and γ max (0.02 and 0.98, respectively, in this study) are the minimum depth of discharge (DoD) and maximum DoD. In addition, the gas storage is assumed to be balanced in the dispatching period, which means that the state at the beginning is equal to that at the end of the day.
(2) Sizing option Suppose that there is one size of each device can be built in an EH in each port area at maximum:
∑
3.3. Model linearization
χ D ⩽ 1, D ∈ {CCHP , GB , PtG , GS , AC } (17)
ω∈ϕD
The optimization model presented above is nonlinear due to the multiplication of a binary variable and a continuous viable in Eqs. (21) and (28). Therefore, in order to improve the solving efficiency, the linearization method in [33] can be used in this study as follows:
(3) operating limits of facilities
∑
χω, i Pωmin , i ⩽ Pω, i ⩽
ω ∈ φcchp, GB, PtG, AC
∑
χω, i Pωmax ,i (18)
ω ∈ φcchp, GB, PtG, AC
Line Line Line Pmin, ω ⩽ Pi − j ⩽ Pmax, ω
, start − Piend (t ) ⩽ M1 (1 − χi,Line (t )) ,j j
(31)
, start Piend (t ) ⩽ M1 χi,Line (t ) ,j j
(32)
, dis − Pich, dis (t ) ⩽ M2 (1 − χich (t )) ,j
(33)
, dis , dis Pich (t ) ⩽ M2 χich (t ) ,j ,j
(34)
(19)
min where Pωmax , i and Pω, i denote the maximum and minimum limits for each Line Line component in the hub i . Pmax, ω and Pmin, ω are the maximum and minimum capacity of lines between hub i and hub j .
(4) energy interconnection start Piend (t )(1 − δLi, j ) , j (t ) = Pi, j
(20)
Line Line PiConnect (t ) = Piend (t ) − P jstart ,j , j (t ) χi, j , i (t ) χ j, i (t )
(21)
χi,Line (t ) + χ jLine j , i (t ) ⩽ 1
(22)
0 ⩽ Pistart (t ) ⩽ PiLine ,j ,j
(23)
where M1 and M2 denote large numbers. By this means, the models can be converted to a mixed-integer linear programming (MILP) problem.
4. Case study As a case study, a typical port district in China is selected for demonstrating the developed model and method. As presented in Fig. 2, it is assumed that there are three interconnected EHs in this port district, which interconnect the three different port areas together. Each port area has a type of ship terminal with specific working conditions. They are the EH 1 (bulk terminal) with an area of 25 km2, the EH 2 (cruise terminal) with an area of 34 km2 and the EH 3 (container terminal) with an area of 41 km2. Therefore, the energy consumptions of the three areas are different from each other and the whole port district can be seen as an IPES with three EHs. Each EH’s structure is similar to that of the presented by Fig. 1.
and Piend where Pistart ,j , j are the powers at the starting and ending points of the linei-j at time t with the transfer from the hub i to the hub j. It is clear that one of the two should be 0, and their difference indicates the magnitude and direction of the interconnected power. Also, Li, j is the distance between the hub i and hub j. δ denotes the power loss rate of the power line per kilometer. Eq. (21) indicates that the power through line should be smaller than its rated capacity. (5) IDR frequency limits
0 ⩽ Δdri+ (t ) ⩽ λμi+ (t )
∑ Demandi (t )
(24)
0 ⩽ Δdri− (t ) ⩽ λμi− (t )
∑ Demandi (t )
(25)
0 ⩽ μi+ (t ) + μi− (t ) ⩽ 1
(26)
T
0⩽
∑ μi+ (t ) + μi− (t ) ⩽ Numdr
(27)
t
μi+ (t ) ,
μi− (t )
are binary variables where λ is the IDR participation rate, of invoking IDR and one of them should be 0 at time t. Numdr denotes the maximum scheduling times of IDR. (6) gas storage The gas stored in periods with redundant power generation can be supplied as required, which alleviates the mismatch of supply and demand. The GS model is model as: ch dis Si, GS (t ) = Si, GS (t − 1) + (χich Pich·ηGS + χidis Pidis / ηGS )·Δt
(28)
γ minScap ⩽ Si, GS (t ) ⩽ γ maxScap
(29)
Si, GS (1) = Si, GS (24)
(30)
Pich
Pidis
and are respectively the charging and discharging gas in where the hub i at time t. χich and χidis are the binary variables related to the
Fig. 2. The typical port district in China with three EHs. 5
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4.1. Settings of key parameters
Table 1 Parameters of the shore-side power.
The energy consumptions of the port can be generally divided as terminal operations, municipal services, business culture and daily residential consumption [34]. Due to the different load characteristics in various industries, loads of different industries are added up according to the energy types. As shown in Fig. 3, the profiles of electricity, heat, cool, ships and wind power in three EHs on typical days are presented. With regard to the prediction of ships’ load, the Monte Carlo approach has been employed to deal with the uncertainties of ships using shoreside power. On the basis of statistical regularity of ships calling at ports, the arriving time, in port duration and magnitude of shore-side power are assumed to follow normal distributions, negative exponential distributions, and normal distributions, separately [35–37]. The parameters of the ships using shore-side power are listed in Table 1. Meanwhile, it is assumed that the ships will use the shore-side power as soon as they call at the ports and it is regarded as the transferable load to participate in the IDR program. The parameters of the facilities within EH and electrical lines are given in Table 2. In addition, the following main assumptions have been made to set the problem data:
Average power demand (kW)
Average porting time (h)
Bulk ship Cruise ship Container vessel
400 800 1200
10 5 10
Table 2 Parameters of the facilities and electrical lines. Type
Maintenance cost (¥/ kW·h)
Life (year)
CCHP PtG AC GB GS Electrical Line
0.032 0.028 0.021 0.026 – –
30 15 15 20 10 40
EHs are planned separately without IDR and EI. Case C: EH with IDR and without EI. Each EH is still planned separately, but IDR is considered in each EH to achieve hourly load balances. The interconnection between EHs is not considered as in Case B. Case D: EH without IDR and with EI. Three EHs are planned together with interconnection. However, IDR is not employed here. Case E: EH with both IDR and EI. Both IDR and EI are considered in this case to achieve energy balance in three port areas.
(1) There is no heating demand in summer and no cooling demand in winter. Electrical demands are the same in three periods. (2) The price of electricity ( pele ) is 1.35, 0.90, and 0.47 ¥/ kW·h respectively for peak, flat and valley periods. The price of natural gas ( p gas ) is 2.70/m3. (3) The power loss rate (δ) of electric lines is 0.03%/km. The distances between each EHs are 6 km, 6 km, and 5 km, as shown in Fig. 2. According to the cost and capacity of the typical power line, the linear relationship between the price per length and the rated capacity is: cap ωline = 0.405PiLine − j + 49.19
Ship Type
The configuration and sizing results are presented in Tables 3 and 4. (1) Comparison of case a and case B There is no IDR and EI in these two cases. In contrast with the traditional energy supply solution (Case A), though the installation of the CCHP and other devices increases the investment and maintenance costs, the energy cost and the penalty cost for wind power curtailment drop drastically. As a result, the total annual cost is reduced by 25.96%. Thus, the IPES shows much better economic benefits than both the conventional centralized energy system in the port area.
(35)
The optimization model is implemented in MATLAB with YALMIP and CPLEX solvers on an Intel i5 laptop with 8 GB RAM. It takes less than 15 s when running the simulation. 4.2. Results of EH configuration and sizing
(2) Comparison of case B and case C
In order to illustrate the effectiveness of the IPES with IDR and EI, five cases are conducted in this study.
This comparison illustrates the influence of IDR on the EHs configuration and the planning cost. The total cost is reduced by 2.76% after introducing IDR despite there is an additional cost of dispatching it. Furthermore, the reduction of the investment cost reflects the substitute effect of IDR in specific devices, which finally results in the decrease of installed capacity. However, this impact is not apparent. The energy and maintenance costs are reduced by 9.58% and 2.63%,
Case A: Conventional energy supply solution. There is no EH in each port area. Hence, heating loads and cooling loads are provided by the GB and the AC, separately. The wind power and distribution network in the port area will supply the electric demand. Case B: EH without both IDR and EI. The EHs are implemented to satisfied the multiple energy demands in the IPES. However, three
Fig. 3. The profiles of load demand and wind power in three port areas. 6
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Table 3 Optimal capacity of eh components in five cases. Case
Port
Capacity/kW CCHP
GB
PtG
AC
GS
Line1-2
Line1-3
Line2-3
A
Port1 Port-2 Port −3
– – –
22,000 21,000 16,000
– – –
5350 8025 8500
– – –
–
–
–
B
Port-1 Port-2 Port −3
19,000 19,000 19,500
8000 9000 3000
7000 12,500 0
1050 3468 3924
2000 7000 0
–
–
–
C
Port −1 Port −2 Port −3
19,000 19,000 19,500
7000 8000 3000
7000 12,500 0
793 3093 3018
2000 7000 0
–
–
–
D
Port −1 Port −2 Port −3
19,500 19,500 19,500
8000 8000 13,000
0 0 7000
1050 3449 3924
0 0 0
0
5692
18,353
E
Port −1 Port −2 Port −3
19,500 19,500 19,500
7000 7000 11,000
0 0 0
774 2653 3018
0 0 0
3711
8000
16,872
simultaneously. In the following, the analyses of the daily optimal operation conditions of three EHs in case E are reported, as illustrated respectively in Fig. 4(a), (b) and (c). First, the cooling balance of the EHs in summer is shown in Fig. 4(a). The cooling loads in each EH are supplied by the CCHPs and the ACs. From 1:00 to 7:00, there is no cooling demand in the EH 1 and the EH 3. The AC in the EH 2 operates as the main cooling supply device. As the electricity price and cooling demand grow simultaneously, the CCHPs provide the majority of the cooling power between 9:00 and 22:00 and the ACs are used as the backup cooling source when the CCHPs cannot satisfy the cooling demand at its maximum. Meanwhile, IDR provides both positive and negative outputs in load shifting. However, due to the constraint to the IDR dispatching times and participating rate, IDR can be only invoked at a few critical operation points. The heating balance of the EHs in winter is illustrated in Fig. 4(b). Similar to the cooling balance, the heating loads in each EH are provided by the CCHPs and the GBs. The CCHPs do not operate from 23:00 to 7:00 as the electricity tariff is low, and the heating demands are satisfied by the GBs (EH 3). The CCHPs provide the majority of the thermal power between 8:00 and 22:00. When the CCHPs cannot meet thermal demand, the GBs are used as the alternative heating device. Meanwhile, IDR plays the same role as it in cooling energy operation. With regard to the electricity balance in Fig. 4(c), it is interesting to analyze the electricity exchange between each EH. It can be seen that when the electrical network exists, the advantage of EI appears. During most of the time between 1:00 and 8:00, there is sufficient wind power in port area 1 and 2. Meanwhile, IDR plays a positive role in wind power consumption. However, in order to satisfy the considerably high electrical load in the port area 3, the surplus of electrical power in port area 1 and port area 2 are injected into the electrical network (Pi-j, in red). Besides, when it still cannot satisfy the load demand, it is optimal to purchase the electricity (Ele_pur, in the darkest blue) from the grid due to its low price. From 9:00 to 22:00, the CCHPs in three EHs are almost operating at their maximum output. The IDR is preferentially used to offset part of the purchased electricity. With regard to the excess electrical power in port area 1 or port area 2, one the one hand, it is converted into cooling power through the ACs to meet the cooling demand. On the other hand, it is transmitted to port area 3 through the electrical network. As the electricity price goes down at 23:00 and 24:00, the CCHPs are shut down, and their output is covered by purchasing electricity from the grid. It is also noted that with regard to IDR in port area 3, there is much more load rebound at 23:00. This is mainly due to a large amount of curtailable load in the previous three hours.
Table 4 costs Comparison Of Five Cases (Million yuan/¥). Case
Cinv
CE
Cmat
CDR
Ccurt − wind
Ctot
A B C D E
64.32 293.65 290.60 296.16 286.72
5008.34 3537.93 3198.91 3230.30 2946.54
29.34 113.86 110.76 105.80 103.80
– – 239.59 – 221.65
238.90 8.85 4.98 0.00 0.00
5340.90 3954.29 3844.83 3632.31 3558.99
separately, indicating that IDR has played a positive role in saving power or gas resources and optimizing the outputs of the devices. (3) Comparison of case B and case D In case D, three EHs are connected to each other by the electrical networks. Therefore, this comparison shows the impact of EI. The total cost is decreased by 8.14% after adopting EI between EHs despite there is an additional cost of line investment. Results in Table 3 suggest that EI eliminates the redundant configuration of PtG and GS in some port areas, but the capacity of CCHP increases. Furthermore, the energy and maintenance costs are dropped by 8.71% and 7.02%, respectively. It should be noted that there is no charge for wind power curtailment, which indicates the advantage of EI for promoting inter-regional wind power accommodation. (4) Comparison of the Last four cases As shown in case E, when both IDR and EI are considered, the total cost is reduced by 9.99% compared with case B. The reduction is more apparent with regard to the cost of investment and energy consumption. Besides, the PtG and the GS are not installed any more though wind power is fully consumed. In summary, the implantation of EHs can reduce the total planning cost compared with the traditional energy supply solution in the IPES. After adopting IDR and EI between EHs, the total cost can be furtherly reduced, respectively. EI performs better than IDR in the total cost saving. Besides, if IDR and EI are considered together, the advantage of the two can be accumulated, with the total cost dropping by nearly 10%.
4.3. Typical daily operating conditions of EHs The optimal energy management strategies of the IPES are obtained 7
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Fig. 4. The daily energy balance in three EHs in case E (a) Cooling balance in summer (b) Heating balance in winter (c) Electrical balance in summer.
effect of some critical parameters and the uncertainty of ship using shore-side power on the IPES planning results. All the analyses are performed on the EH with both IDR and EI case (Case E) by taking stepwise values for one item, and with the remaining items holding the nominal values.
In summary, the IPES with EHs can achieve the cascade utilization of energies and the complementary of multi energies, which will bring economic benefits to the port areas. Both IDR and EI can alleviate the mismatch problem between energy output and peak-valley load demand in the PIES.
(1) Energy prices 4.4. Sensitivity analysis This part investigates the changes in the total cost and the capacities Sensitivity analyses are conducted in this section to investigate the 8
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Fig. 5. Planning Cost and capacities of CCHPs in three areas under varying energy prices.
Fig. 7. Planning cost under varying ship porting time and shore-side power (a) Investment cost (b) Total cost.
prices. In addition, it is obvious that the total cost is more sensitive to the variations in natural gas prices compared to the electricity price. This is evidently expected due to the high penetration of natural gas in the studied system. (2) line loss rate and IDR participation rate The changes in planning costs are investigated when the line loss rate or IDR participation rate changes. As shown in Fig. 6(a), the investment cost decreases as the IDR participation increases, but it remains nearly unchanged with the increasing line loss rate. This is mainly because the capacity of the devices is related to the total load level. When IDR is introduced, the peak load decreases and the demand profile becomes flattered, which ultimately results in size selection. The initial drop in investment gives way to saturation as the IDR allocation increases, which means that the effect of IDR on equipment optimal selection is limited. It is noted that there would be some load rebound after curtailment, which causes the size of ACs to increase. As a result, the investment cost increases slowly when the IDR participation rate is over 45% (nearly). By contrast, Fig. 6(b) illustrates that the total cost decreases continuously with the increasing IDR participation rate at a specific line loss rate per km. The reason is that IDR can significantly lessen the cost of energy, which results in a reduction in the total cost. However, the total cost is almost unchanged with the decreasing line loss rate because the investment cost for the electrical networks only takes up a quite
Fig. 6. Planning Cost under varying line loss rate and IDR participation rate (a) Investment cost (b) Total cost.
of CCHPs in three areas when the price of electricity or natural gas changes. As shown in Fig. 5, the capacities of CCHPs increase as the electricity price grows while they decrease (from 90% to 110%) as the natural gas price rises. However, these changes are not manifest mainly because of the large investment cost of CCHP. With regard to the total cost, results demonstrate that the highest variations of total costs (about 16%) are due to the 20% variations (from 50% to 70%) in natural gas 9
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small share in the total planning cost.
on integrated energy service under Grant SGJS0000QXWT1800670.
(3) Shore-side power and ship port time
References
This section illustrates the impact of the uncertainty of ships using shore-side power on the planning cost. It can be seen from Fig. 7(a) that the investment cost remains around 280 million yuan when the average port time or average shore-side power varies from −20% to 20% as its original value. Since investment cost the CCHPs is large, it is more economical to buy the electricity from the grid rather than upgrade the size of them. As a result, the small variations (shown in different colors) could be the changes of some other devices and the electrical line capacities, but they only account for a small percentage of the entire investment cost. However, the total cost changes significantly as the average in port time of ships or average shore-side power increases. This is mainly because the uncertainty of ships using shore-side power has a much greater impact on the energy cost rather than the investment cost, and the part of energy cost leads to the rise of total planning cost.
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5. Conclusion Ports are vital gateways and corridors to the rest of the world. In this paper, a framework and EH based planning model are proposed for IPES. Five different port energy system schemes are compared to determine the optimal planning strategy for IPES. In addition, the optimal operation of the IPES is performed in detail and the sensitivity analyses are conducted to show the robustness of the simulation results. The main research findings can be summarized as follows. (1) Simulation results illustrate that the proposed IPES (case B) shows better economic and synergetic performances than the traditional port energy system (case A), with the total planning cost dropped by nearly 26%. (2) When IDR and EI are considered, it is beneficial to incorporate both of them (case E) to minimize total cost at the system planning stage. In this case, it avoids installing redundant PtG and GS within each EH and furtherly reduces the total planning cost by nearly 10%. It is also worth mentioning that if the two solutions are considered separately, EI (case D) performs better than IDR (case C) in the total planning cost saving. (3) Moreover, sensitivity analyses show that the performance of IDR on capital investment cost saving is limited but the total cost declines significantly as the IDR participation rate grows. However, with regard to EI, the changes in line loss rate have little influence on both investment cost and total cost. (4) Finally, the uncertainties of the ships using shore-side power have little influence on the investment cost, but they can increase the energy cost as well as the total annual cost when the average in-port time or shore-side power rises. The purpose of this study is to lay a framework for modeling IPES. More work has been done than is reported here and more work including heating networks and uncertainty of renewable energy remains to be done. Declaration of Competing Interest The authors declare no conflict of interest in this work. Acknowledgment This work is supported in part by the China Scholarship Council and British Council through the Newton Fund, in part by the 2018 State Grid Corporation Science and Technology Program: Research on port energy efficiency assessment and sustainable improvement model based 10
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