Development and optimization of an integrated energy network with centralized and decentralized energy systems using mathematical modelling approach

Energy 183 (2019) 617e629

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Energy journal homepage: www.elsevier.com/locate/energy

Development and optimization of an integrated energy network with centralized and decentralized energy systems using mathematical modelling approach Wen Hui Liu a, b, Wai Shin Ho a, b, *, Ming Yang Lee a, b, Haslenda Hashim a, b, Jeng Shiun Lim a, b, Jirí J. Klemes c, Angel Xin Yee Mah a, b a Process Systems Engineering Centre (PROSPECT), Research Institute on Sustainable Environment (RISE), Universiti Teknologi Malaysia, 81310 UTM, Johor Bahru, Johor, Malaysia b School of Chemical and Energy Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, UTM, Johor Bahru, Johor, Malaysia c  Sustainable Process Integration Laboratory e SPIL, NETME Centre, Faculty of Mechanical Engineering, Brno University of Technology - VUT Brno, Technicka 2896/2, 616 00, Brno, Czech Republic

a r t i c l e i n f o

a b s t r a c t

Article history: Received 16 January 2019 Received in revised form 24 May 2019 Accepted 25 June 2019 Available online 26 June 2019

Decentralized energy generation (DEG) system which characterizes local power generation and utilization are recently getting more attention in energy system planning and implementation because it produces cleaner energy from renewable resources and is capable to avoid significant energy losses during the power transfer from the centralized power plants. These DEGs are however scattered in locations and have intermittent power supply, making it difficult to self-sustain. In this study, a novel integrated energy system consisting of multiple DEGs connected to the existing CEG is proposed. New aspects that are included in the model include the distribution and transmission losses as well as impact of operating load on the heat rate of power plants. Energy storage system were also modelled to operate within the DEG and CEG network. Through a case study demonstration, the capacities of the integrated system were optimized using superstructure-based mixed integer non-linear programming (MINLP) mathematical modelling. The system was also optimized based on economic and energy efficiency to study the effects and trade-off between the two parameters. The results revealed that the optimal system can be obtained with levelized cost of electricity of MYR 0.44/kWh. The result also revealed that biomass and wind energy favours industrial users. © 2019 Elsevier Ltd. All rights reserved.

Keywords: Integrated energy system Decentralized energy generation Mathematical modelling Optimization Power transmission and distribution

1. Introduction Nowadays, the power generation has become more localized with the deployment of decentralized energy generation (DEG) systems. The interest in the development of DEG has been catalysed by environmental policies and regulations, governmental subsidies (such as net metering and feed-in tariff scheme) as well as the advancement of DEG technologies [1]. Compared to the conventional and centralized large power plants, DEG is relatively smaller in scale with its capacity ranges between 1 kW and 250 MW [2]. It is constructed within the vicinity of the end-consumers [3],

* Corresponding author. Process Systems Engineering Centre (PROSPECT), Research Institute on Sustainable Environment (RISE), Universiti Teknologi Malaysia, 81310, UTM, Johor Bahru, Johor, Malaysia. E-mail address: [email protected] (W.S. Ho). https://doi.org/10.1016/j.energy.2019.06.158 0360-5442/© 2019 Elsevier Ltd. All rights reserved.

harnessing clean energy from locally available and renewable energy (RE) resources such as solar, wind and biomass. One popular form of a DEG is Hybrid Power System (HPS) in which the system combines two or more resources to generate power. However, there are issues regarding the operability of the DEG systems. Most DEG resources, which are renewable-based, are geographically scattered and intermittent. The scenarios at different DEGs are very much dependent on the local resource availability and the local consumption pattern such that at one point of time, some DEGs may have excess electricity and some may have deficits. Even if the DEG can be self-sustained, it also requires a substantial energy storage (ES) capacity as the support system. In term of its economic of scale, DEG is more expensive when comparing the fuel cost and the technology maturity aspects with the established and fossil-based centralized energy generation (CEG) system.

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Nomenclature AC BBFB CAES CEG DC DEG ES GAMS HPS MINLP NGCC PV RE

Alternating current Biomass bubbling fluidized bed Compressed air energy storage Centralized energy generation Direct current Decentralized energy generation Energy storage General Algebraic Modelling System Hybrid power system Mixed integer non-linear programming Natural gas combined cycle Photovoltaic Renewable energy

Indices p q s t

Power plants {P1, P2, P3, P4, P5, C1} Power demand {Q1, Q2, Q3, Q4, Q5} Range of plant operating load {s1, s2, …, s12} Time of analysis (h) {t1, t2, …, t24}

Symbols AFES

Capital recovery factor of energy storage (dimensionless) AFGen Capital recovery factor of generator (dimensionless) BERCactual Actual capacity of energy storage after DoD (energyrelated) (MWh) BFuel Amount of biomass fuel used for power generation for every range of operating load (tonne/h) BGen Total biomass energy generation for every range of operating load (MWh/h) BGenCap Capacity of biomass power plant (MW) BMTG Generated biomass power supplied to demand directly (MWh/h) BMTS Generated biomass power charged into storage (MWh/h) BPRC Capacity of energy storage (power-related) (MWh) BSA Amount of biomass source available annually (tonne/ y) Ch Selection of energy storage charging mode CC Annualized capital cost of individual power plant (MYR/y) CCBM Capital cost of biomass power plant (MYR/kW) CCERC Capital cost of energy storage capacity (energyrelated) (MYR/kWh) CCNG Capital cost of natural gas power plant (MYR/kW) CCPRC Capital cost of energy storage capacity (powerrelated) (MYR/kW) CCPV Capital cost of solar PV system (MYR/kWp) CCWT Capital cost of wind turbine (MYR/kW) Cost Annualized cost of individual power plant (MYR/y) Cp Maximum power coefficient of wind turbine (dimensionless) DL Distribution loss (% fraction) DLF Binary factor for allowable distribution routes from a power plant to a power demand (dimensionless) Dch Selection of energy storage discharging mode DoD Depth of discharge of storage system (% fraction) EffES Charging and discharging efficiency of energy storage system (% fraction) Energy efficiency of solar PV system (% fraction) EffPV Converter efficiency (% fraction) Effiv

FOMBM FOMNG FOMPV FOMWT GTD

Fixed O&M cost of biomass power plant (MYR/kW.y) Fixed O&M of natural gas power plant (MYR/kW.y) Fixed O&M cost of solar PV system (MYR/kWp.y) Fixed O&M cost of wind turbine (MYR/kW.y) Generated AC power from generator to demand (MWh/h) GTS Generated power from generator to storage (MWh/h) HRBM Heat rate of biomass power plant wrt s (MJ/MWh) HRNG Heat rate of natural gas power plant wrt s (MJ/MWh) HRo Heat rate of power plant at full load (MJ/MWh) HVBM Net calorific value of biomass fuel (MJ/tonne) HVNG Net calorific value of natural gas fuel (MJ/m3) ICon Initial energy content of energy storage at t ¼ 0 (MWh) LA Rooftop/land area available for solar PV construction (m2) LDem Hourly local demand in a day (MWh/h) LF Load factor or operating load (%) LLF Lower load factor wrt s (%) LN A large value (applied for linearization purpose) NGFuel Amount of natural gas fuel used for power generation for every range of operating load (m3/h) NGGen Total natural gas energy generation for every range of operating load (MWh/h) NGGenCap Capacity of natural gas power plant (MW) NGSA Amount of natural gas source available annually (m3/ y) NGTG Generated natural gas power supplied to demand directly (MWh/h) NGTS Generated natural gas power charged into storage (MWh/h) OM Annual operating and maintenance cost of individual power plant (MYR/y) OMES O&M cost of energy storage capacity (MYR/kW.y) PVA Area required for solar PV system (m2) RA Rotor swept area for wind turbine (m2) RB Area of rotor blade required for wind turbine (m2) Rad Average hourly solar radiation on a sunny day (kW/ m2.h) SOTG Generated solar power supplied to demand directly (MWh/h) SOTS Generated solar power charged into storage (MWh/ h) SPVCap Capacity of solar PV system (MWp) STD Storage output to demand (MWh/h) STF Binary factor for allowable energy transfer from storage to power demand (dimensionless) SoGen Total solar energy generation (MWh/h) Speed Hourly wind speed in a day (m/s) Stored Cumulative energy in energy storage at instantaneous time t (MWh) TBFuel Total amount of biomass fuel used (tonne/d) TBGen Total biomass energy generated (MWh/h) TL Transmission loss (% fraction) TLF Binary factor for allowable transmission routes from a power plant to a power demand (dimensionless) TNGFuel Total amount of natural gas fuel used (m3/d) TNGGen Total natural gas energy generated (MWh/h) TPGen Total plant generation (by individual power plant) (MWh/d) TSC Total system annualized cost (MYR/y) TSOE Overall system operational efficiency (%)

W.H. Liu et al. / Energy 183 (2019) 617e629

TSysGen ULF V1 V2 VOMBM VOMNG WGen WITG

Total plant generation of the overall energy system (MWh/d) Upper load factor wrt s (%) Set 1 of conversion efficiency (% fraction) Set 2 of conversion efficiency (% fraction) Variable O&M cost of biomass power plant (MYR/ MWh) Variable O&M cost of natural gas power plant (MYR/ MWh) Total wind energy generation (MWh/h) Generated wind power supplied to demand directly (MWh/h)

From the literature, different modelling and simulation approaches have been developed for DEG systems. Deterministic and probabilistic modelling approaches were first combined by Sreeraj et al. [4] for an isolated solar-wind-battery HPS to cater both fixed and random (due to the nature of RE resource) data. Khatib et al. [5] completed an optimization model for hybrid photovoltaic (PV)/ wind system based on loss of load probability (LLP) and system cost. The optimization was performed using a hybrid iterative to generate possible configuration set and a genetic algorithm to find the optimum configuration (size of PV array, wind turbine, battery storage, converter, tilt angle) of the PV/wind hybrid system. The linear programming (LP) model developed by Lee et al. [6] considered power losses from supply to demand. Lee's mathematical model was tested and compared with same case studies as demonstrated earlier by Wan Alwi et al. [7] and Mohammad Rozali et al. [8]. Both of their work were based on numerical method to optimize HPS while considering power losses. Sharafi and ElMekkawy [9] applied stochastic optimization framework inclusive of dynamic multi-objective particle swarm optimization (DMOPSO) algorithm, simulation module and sampling average technique to estimate a Pareto front for an HPS design. A multi-period mixed integer linear programming (MILP) planning and scheduling model based on an hourly basis was investigated by Ho et al. [10]. The model is an advanced methodology from Hashim et al. [11]; taking various weather conditions, energy profiles, palm oil mill operation modes and consumption patterns, while practising load shifting and installing ES as energy efficient approaches. Some researchers had also applied and tested the feasibility and performance of the developed methods to real case studies, mainly for the benefit of communities that are in need of an efficient and cost-effective power system. An economic analysis was performed by Kumaravel and Ashok [12] to analyse the economic performance of implementing hybrid energy system for a rural village in India using HOMER, a renowned software. A research done by Fadaeenejad et al. [13] used a cost-effective software, iHOGA, to design and optimize a PV-wind-battery HPS at Kampung Opar, Sarawak. The optimization tool is able to report the best optimum number of PV modules, batteries and wind turbines that should be installed in the study area. Theo et al. [14] proposed an on-grid HPS in an ecoindustrial park and performed optimization on the system economics and design (specifically on the ES selection). Gao et al. [15] investigated the energy performance of integrated district energy systems and district cooling systems for the campus of the Hong Kong Polytechnic University, and result showed that the integrated system can be energy efficient in subtropical and high-density areas with energy saving of 10e19%. To date, there are still limited researches on modelling and optimization of power system which is operated beyond a single DEG system. Doagou-Mojarrad et al. [16] utilized a modified interactive fuzzy satisfying method, called the Hybrid Modified

WITS WTCap Z j m nES nGen r x

r

619

Generated wind power charged into storage (MWh/ h) Capacity of wind turbine system (MW) Number of days in a year (d/y) Interest rate (% fraction) Inverse of TSOE (fraction) (applied for linearization purpose) Number of lifetime of energy storage system (y) Number of lifetime of power generator (y) Heat rate increment (% fraction) Selection of a range of operating load Air density (kg/m3)

Shuffled Frog Leaping Algorithm-Differential Evolution (MSFLADE), to test on a wider distribution network consisting different DEG units while optimizing in term of technical, environmental and economic aspects. Another optimization model by Chang and Lin [17] involves power transmission and allocation between power generators and demand stations. Wu et al. [18] proposed a nonlinear optimization model to optimize the energy-saving by determining appropriate neighbourhood design and the arrangement of neighbourhood-scale distributed energy system. Meanwhile, a study by Sultana et al. [19] enhances the energy loss reduction and voltage stability factor through optimal allocation of battery swapping stations and distributed energy generation using Grasshopper Optimizer Algorithm. Based on the studies above, sharing of energy between electrical network that take into consideration of other variables such as changing of heat rate due to changes in operating load, and differences in type of energy demand profile is not taken into consideration. This study therefore proposes a novel integrated energy system in which more than one DEG are interconnected to form a grid network at the distribution level. These DEGs could share the energy supply among themselves; however, their power supply may be fluctuating according to the weather or the availability of the local resources affected by seasonal factor. In order to strengthen the reliability of the power supply, these DEGs are integrated to the existing CEG (which has a more stable electricity supply) at the transmission level. The energy generation can be supply to other grid network: i) between DEG(s) or ii) through the CEG. Changing of operating load that leads to changes in heat rate were also considered in this study. In addition, considering that the DEG and CEG are different system, energy storage system that operate on a different network are also included in this new model as an improvement to the scheduling of the network. Through a case study demonstration, a mathematical model is developed for designing and targeting the optimal capacities of the proposed integrated energy system, based on the economic and energy efficiency aspects. For a larger power network like this, it is essential to incorporate energy losses due to power transmission and distribution into the model. Both optimization results are compared to identify which of these aspects is the most significant to be considered in the design of the integrated energy system. 2. Methodology Fig. 1 outlines the general flowchart of research methodology employed in this study. To address the problem of the study, a case scenario was set up (Section 3.0). Relevant data needed to construct the model of the case study was collected (Section 3.1). A superstructure displaying all the possible routes and interconnection between the units studied was then developed (Section 2.2). Based on the superstructure, a mathematical model was formulated

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arranged forms a closed-loop energy network in which the energy are shared among the power plants at different locations [21]. Distribution loss (DL) occurs when power is transferred and distributed across DEGs (at the same distribution level) while transmission loss (TL) and DL occurs when power is transmitted between DEGs and CEG (passing through transmission and distribution levels) [21]. Each DEG or CEG constitutes mainly the power plant, ES system and the demand. Fig. 3 shows the implicit system configuration for each individual DEG and CEG system. Depending on the source available at DEGs and CEG, different generators which produce different current types are deployed. It is important to differentiate the power flow within the system into their own respective buses, i.e. alternating current (AC) bus and direct current (DC) bus. A converter is installed to aid the current conversion within the energy system. Besides, an ES system is installed to regulate the power flow during peak and low-peak demand periods. 2.2. Superstructure of energy model

Fig. 1. The general flowchart of research methodology employed in this study.

Fig. 4 illustrates the superstructure constructed in this work in order to develop the model of an integrated energy system consisting of multiple DEGs and CEG as described in Section 2.1. As shown in Fig. 4, the supply side (on the left) are matched with the demand side (on the right) through time intervals measured by an hourly basis. The process and operations are continued for 24 h and repeated daily.

(Section 2.3) and the formulations were programmed into an established commercialized modelling software, General Algebraic Modelling System (GAMS) [20]. The model was solved using suitable optimization solver and the optimized results were analysed and discussed (Section 4.0). 2.1. Energy system configuration A hypothetical grid network is designed as shown in Fig. 2, representing the integrated CEG and DEGs system model. DEG power plants situated at different regions generate electricity onsite by harnessing locally abundant, renewable resources (such as solar, wind and biomass energy) for the local demand. Connected to these DEGs is a CEG power plant, running on fossil fuels (such as natural gas) and which acts as a central power control, regulating and management site [21]. As illustrated in Fig. 2, the integrated energy system when

Fig. 3. General energy system configuration for an individual power system (modified from Ref. [22].

Fig. 2. Illustrative energy system integrating CEG and multiple DEGs (modified from Ref. [21].

W.H. Liu et al. / Energy 183 (2019) 617e629

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Fig. 4. Superstructure of the integrated energy system model.

Six power plants (denoted as P1 to P5 and C1) with energy storage (ES) embedded are designed to supply electricity to five demands (denoted as Q1 to Q5). These power plants and demands are located at distinctive regions, labelled as DEG or CEG with own colour code (see Fig. 4). For instance, P1 (together with its own ES) and Q1 are located in the same region labelled as “DEG 1”. Power transfer could happen at the distribution level from plant P to demand Q of the same DEG (e.g. P1 to Q1) or cross DEG (e.g. P1 to Q2). Power could also be transferred at the transmission level (e.g. C1 to Q1). Respective energy losses are imposed based on which route the model choose to transfer the power. On the supply side, energy sources are classified into two types: (a) intermittent which includes solar and wind energy and (b) nonintermittent which includes natural gas and biomass energy. For solar and wind energies, their generation are depending on the local weather conditions and the installation of their generators are also restricted by logistical constraints such as rootftop area (for solar PV) and rotor area (for wind turbines). While for biomass and natural gas, since their generation involves fuel combustion into energy, heat rate variation according to the timely plant operating load is taken into account [23]. In term of operation, a power plant could choose to supply its generated power directly to the demand, or charged into ES for later use. On the demand side, the demand profile from each DEG region comes from three major energy sectors, i.e. residential, industrial and commercial. 2.3. Model formulations The formulation for optimization of the designated energy system model mainly consists of governing equations including objective function, equality (energy balances, mass balances, conversions etc.) and non-equality (constraints). Assumptions, limitations and algorithms of the system operation are mentioned along with the formulas.

CCp ¼

2.3.1. Objective function In this study, it is intended to investigate how the system would perform in term of energy conservation when cost is prioritized, or vice versa. The model was constructed such that two different objective functions involving system cost and operational efficiency can be executed using the same coding. (a) Objective Function 1: Minimizing overall system cost The first objective function is expressed in Eq. (1) where the total system costing, TSC (adding the cost of all individual plant p as Cost) is minimized. This study presents Cost as an annualized value, attributed from two types of cost: (a) capital cost, CC and (b) operating and maintenance cost, OM (see Eq. (2)). Capital cost refers to the equipment installation cost based on the capacities of power generators as well as the ES system. As shown in Eq. (3), the thousand factor is for converting the unit from MW to kW whereas AF, the capital recovery factor, is to annualize the cost of equipment installed at every plant. A general equation computing AF is expressed in Eq. (4) where n represents lifetime of equipment (y) and j represents the annual interest rate (%) [24]. Operating and maintenance cost is mainly accounted for utility and fuel expended in the power generating process, as well as machinery repair and labour. The formula for calculating OM is expressed in Eq. (5). Z, the number of operation days of the system, is assumed to be 365 d/y.

TSC ¼

X

Costp

(1)

p

Costp ¼ CCp þ OMp

cp

 i h SPVCapp :CCPV þ WTCapp :CCWT þ BGenCapp :CCBM þ NGGenCapp :CCNG  1000  AFGen  i h c p þ BERCactual p :CCERC þ BPRCp :CCPRC  1000  AFES

(2)

(3)

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AF ¼

jð1 þ jÞn ð1 þ jÞn  1

OMp ¼

(4)

h

SPVCapp :FOMPV þ WTCapp :FOMWT  i þ BGenCapp :FOMBM þ NGGenCapp :FOMNG  1000 X X þ TBGenp;t  Z  VOMBM þ TNGGenp;t  Z  VOMNG t

t

þ BPRCp  1000  OMES

c p (5)

(b) Objective Function 2: Maximizing system's operational efficiency Eq. (6) expresses the second objective function which is to maximize the overall operational efficiency of the system, TSOE. TSOE is the percentage of the total energy output delivered to the demand over the total energy input generated by the power plants. Selected for this study are four power generation technologies,

LDemq;t

2 3 X GTDp;q;t  1  DL:DLF p;q   1  TL:TLF p;q      4 5c ¼ þ STDp;q;t :Ef f ES p :V 1 p :STF p;q  1  DL:DLF p;q  1  TL:TLF p;q p

namely solar PV system, wind turbine, biomass and natural gas combustion plants. Eq. (7) sums up their energy input as TSysGen. Note that when the energy demand data, LDem is given, Eq. (6) forms a non-linear equation between the variables TSOE and TSysGen. Therefore, a linearized equation is derived as shown in Eq. (8).

PP TSOE ¼

t

TSysGen ¼

q LDemq;t

TSysGen

 100 %

(6)

X X X SoGenp;t þ WGenp;t þ BGenp;t;s p;t

þ

X NGGenp;t;s

p;t

p;t;s

(7)

p;t;s

XX LDemq;t  100  m ¼ TSysGen t

The hourly demand at each DEG, LDem is met by the simultaneous power supply directly from power plants, GTD and from the storage systems, STD, as illustrated in Fig. 4. As described in Eq. (10), power can be delivered from any available power plants or ES systems to the insufficient demands. However, binary factors (i.e. TLF, DLF and STF) entail which route exists and the consequent losses are imposed to power undertaking that particular route. Take TLF and DLF for example. If TLF and DLF are both 1 for a particular power plant to a particular demand, it means that power can be transferred in the transmission and distribution level. As a result, DL and TL are imposed. If DLF is 1 and TLF is 0, the power can be transferred via the distribution level only and therefore, total power is lost due to DL imposed. STF is a binary factor entitled for storage transfer to demand. If STF is 1, it indicates that power transfer is allowed from a particular ES to a particular demand. If STF is 0, no power transfer is permitted. There are two other factors affecting the final value of STD. One of them is EffES which refers to the ES power discharging efficiency towards STD. Sometimes, current conversion is required, thus a customized parameter, V is included in the formula. If there is a change of current type (from AC to DC, or vice versa), V equals the converter efficiency factor, Effiv. Otherwise, V equals to 1, indicating no current is converted.

(8)

q

In fact, Eq. (8) is an inverse relation derived from Eq. (6), with m equals to the inverse of TSOE (see Eq. (9)) and m has a minimum value of 0.01. When Eq. (8) is coded in the model (for linearization purpose), the objective function changes from maximizing TSOE to minimizing m.

1 m¼ TSOE

Eqs. (11)e(14) represent the energy balances for each type of power generator: solar PV system, wind turbine, biomass generator and natural gas generator. Generally, the hourly generation is the summation of the power distributed as direct supply to the demand and as stored power into the ES (as shown in Fig. 4). Eq. (15) depicts the total power generated by the existing power generator(s) within an individual plant. Eq. (16) defines GTD as a summation of all direct AC power from the generator(s), ready to be supplied to the AC demand (designed in the case study). Note that the solargenerated power is converted from DC to AC by Effiv.

SoGenp;t ¼ SOTGp;t þ SOTSp;t

c p; t

(11)

WGenp;t ¼ WITGp;t þ WITSp;t

c p; t

(12)

TBGenp;t ¼

X BGenp;t;s ¼ BMTGp;t þ BMTS p;t

(13)

X NGGenp;t;s NGTGp;t þ NGTSp;t

c p; t

(14)

s

(9)

X X X SoGenp;t þ WGenp;t þ BGenp;t;s t

(a) Demand

c p; t

s

TPGenp ¼ 2.3.2. Energy balance

(10)

(b) Generator Operation

TNGGenp;t ¼ ðm  0:01Þ

q; t

X þ NGGenp;t;s c p t;s

t

t;s

(15)

W.H. Liu et al. / Energy 183 (2019) 617e629

X GTDp;q;t ¼ SOTGp;t :Effiv þ WITGp;t þ BMTGp;t q

þ NGTGp;t

c p; t

(16)

The energy conversion for each power generating technology is described next. First, in Eq. (17), power generated by the solar PV system, SoGen at any given time is a product of area covered by solar PV system (e.g. rooftop), solar PV module efficiency and the hourly solar radiation [25]. For the wind turbine, its hourly power generation, WGen, given in Eq. (18), is determined by the rotor blade area of wind turbines, air density, maximum power coefficient of the wind turbine and the wind speed [25]. The thousand factor is for the conversion from MW to kW. Eq. (19) and Eq. (20) are for converting fuels to electricity in biomass and natural gas power plants. Both equations involve heat rate, HR and fuel heating value, HV. Eq. (21) and Eq. (22) are used to calculate the total amount of fuel consumed in each type of combustion plant.

SoGenp;t :1000 ¼ PVAp :EffPV : Radt

c p; t

WGenp;t :1000 ¼ 0:5 RBp :r:Cp:Speed3t BGenp;t;s :HRBM s ¼ BFuelp;t;s :HVBM

c p; t; s

NGGenp;t;s :HRNG s ¼ NGFuelp;t;s :HVNG TBFuelp ¼

X BFuelp;t;s

c p

c p; t

c p; t; s

(17) (18) (19) (20) (21)

t;s

X TNGFuelp ¼ NGFuelp;t;s

c p

(22)

t;s

In this model, heat rate factor is considered for thermal generators (i.e. biomass and natural gas power plants) as to allow generation flexibility in the operation [23]. To incorporate this, index s is introduced to the variable, HRs in Eq. (19) and Eq. (20). This is further explained in Section 2.3.3 (part (a)). (c) Energy Storage Operation Eq. (23) depicts the amount of generated power readily to be

623

stored into ES, GTS. To accommodate different current output of generators into different ES of selection, current conversion factor, V is inserted into the formula. From Eq. (24), the ES system operates such that the new energy content accumulated inside the storage at time t þ 1, Stored, is resulted from the cumulated energy in the storage plus the input (power charged into ES) minus the output (power discharged from the ES) at time t.

GTS p;t ¼ SOTS p;t :V 2 p   þ WITS p;t þ BMTS p;t þ NGTS p;t V 1 p Storedp;

tþ1

¼ Storedp;t þ GTSp;t :EffES

p



c p; t

X STDp;q;t

(23) c p; t

q

(24)

2.3.3. Constraints (a) Power Generator Operation Generally, the output of a system or the content within a system must not exceed its installed capacity. In the context of a power plant, the hourly generation must be less than or equal to its maximum capacity. Eqs. (25)e(28) are dedicated to solar PV system, wind turbines, biomass and natural gas generators in order to satisfy this rule.

SoGenp;t  SPVCapp

c p; t

(25)

WGenp;t  WTCapp

c p; t

(26)

TBGenp;t  BGenCapp

c p; t

TNGGenp;t  NGGenCapp

c p; t

(27) (28)

The curve in Fig. 5 is used to study how the heat rate changes with operating load. In order to incorporate this non-linear curve into the mathematical model, some modifications have been done. As aforementioned, the s index is introduced to classify the range of power plant's operating loads. From Fig. 5 and 12 ranges of

Fig. 5. Change of operating load from 30 to 100% with the corresponding heat rate increase [26].

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Table 1 Operating load range and the corresponding heat rate increment extracted from Fig. 5 with an accuracy up to 0.01% [23]. Range, s

Load Factor, LF (%)

1 2 3 4 5 6 7 8 9 10 11 12

Heat rate increase, r (% fraction)

Lower Load Factor,

Upper Load Factor,

LLF

ULF

30.00 31.00 33.00 35.00 38.00 43.00 47.00 54.00 60.00 70.00 85.00 100.00

30.99 32.99 34.99 37.99 42.99 46.99 53.99 59.99 69.99 84.99 99.99 100.00

operating load are segmented (indicated as s1 to s12) and are arranged in Table 1 with their lower and upper load factors, as well as the corresponding heat rate increment. 30% is the minimum LF (in percentage) that the plant can operate before it is shutdown (known as turndown). According to Table 1, at a fully loaded generator (i.e. 100% LF), there is no heat rate increase. When partially operated between 85.0% and 99.99%, the heat rate of generator increases by 1.0%. Given HRo is the heat rate value at 100% LF and r is the increment (in % fraction), new heat rate for power plant after increment, HRs is calculated using Eq. (29) and is input into Eq. (19) and Eq. (20).

HRs ¼ HRo ð1 þ rÞ

(29)

Eq. (30) and Eq. (31) are derived to give generation limits (upper and lower limits) to both biomass and natural gas power generators [23]. The hundred factor is to ensure the units on each side of inequalities are parallel. The term x is a binary variable defined to let the model choose the optimal load factor that the plant should operate. The sum of all x has to be equal to 1, as depicted in Eq. (32) [23].

 BGenCapp :ULFs :xp;t;s c p; t; s

energy is accumulated in the ES. Eq. (37) bounds the operation of the ES such that only one state (either charging or discharging) can happen at one time. Binary variable Ch indicates the charging status (1 for charging, 0 otherwise) whereas Dch, another binary variable, indicates the discharging status (1 for discharging, 0 otherwise). Eq. (38) and Eq. (39) are for omitting non-linear terms in Eq. (37) by introducing a large value, LN.

Storedp;t  BERC actual p :DoD GTS p;t :Ef f ES p  BPRC p STDp;q;t  BPRC p Storedp;t¼1 ¼ IConp Chp;t þ Dchp;t  1 GTSp;t :EffES

BGenCapp :LLFs :xp;t;s  BGenp;t;s :100 (30)

0.11 0.10 0.09 0.08 0.07 0.06 0.05 0.04 0.03 0.02 0.01 0.00

p

c p; t

c p; t

(33) (34)

c p; q; t

(35)

c p

(36)

c p; t

(37)

 Chp;t :LN

c p; t

X STDp;q;t  Dchp;t :LN

c p; t

(38) (39)

q

NGGenCapp :LFs :xp;t;s  NGGenp;t;s :100  NGGenCapp :UFs :xp;t;s X xp;t;s ¼ 1

c p; t; s

c p; t

(31) (32)

s

(b) Energy Storage Operation With regards to ES operation constraints, Eq. (33) restricts that the cumulated energy in the storage at every hour, Stored must not exceed its maximum energy-related capacity, i.e. the product of depth of discharge of ES, DoD and the actual installed energyrelated capacity of storage, BERCactual. DoD is factorized into the equation as to avoid battery degradation due to charging and discharging cycles. Eq. (34) and Eq. (35) constrain both the power charged into and discharged from ES must not exceed its powerrelated capacity, BPRC. With the assumption that ES operates in a continuous daily cycle, Eq. (36) is defined so that ES has the same initial energy content, ICon at t ¼ 1. Meanwhile, this also indicates that no excess

(c) Space and Resource Availability The deployment of power generators requires ample space or resources. This model considers the space adequacy at local DEGs as limitations to installing solar PV modules and wind turbines, as described in Eq. (40) and Eq. (41). The local supply of fuel resources for biomass and natural gas power generators are also bounded by their local availabilities on an annual basis, as described in Eq. (42) and Eq. (43).

PVAp  LAp RBp  RAp

c p

(40)

c p

TBFuelp :Z  BSAp

(41) c p

TNGFuelp :Z  NGSAp

c p

(42) (43)

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Table 2 Descriptions of DEGs and CEG for the illustrative case study. Energy System

Main sector demand

Type of energy source available

Type of energy storage installed

Net energy

DEG DEG DEG DEG

Commercial (AC) Residential (AC) Industrial (AC) Industrial (AC)

Solar (DC) Solar (DC) Wind and biomass (AC) Solar (DC); Wind and biomass (AC) Solar (DC); Biomass (AC) Natural gas (AC)

Lead-acid battery (DC)

Deficit Deficit Deficit Surplus

1 2 3 4

DEG 5

Residential (AC)

CEG

Net demand from all DEGs (AC)

Surplus Compressed air energy storage (AC)

-

3. Case study An illustrative case study is designated for a chosen tropical region. The integrated energy system is modelled such that five DEGs are situated at different locations and are interconnected at the distribution network. These DEGs are linked to a single CEG at the transmission network. Three potential RE sources, i.e. solar, wind and biomass energy are harnessed at DEGs while natural gas is chosen to fuel CEG system via a natural gas combined cycle (NGCC) generator. The types of power source, power demand and technologies deployed in each energy system are described in Table 2. Note that the integrated energy system is designed such that three DEGs have net deficit energy and the other two have net surplus energy, resulting in a total net deficit which is to be addressed as power demand for the CEG. For this analysis, a DC battery storage i.e. lead-acid battery is used to support the DEG system. According to Roberts et al. [27]; lead-acid battery is commonly used in stand-alone power systems from its superior properties such as technological maturity, low cost, long lifecycle, prompt response, and low rate of self-discharge. Whereas for CEG, a larger AC energy storage system i.e. compressed air energy storage (CAES) is installed. Traditionally, CAES is designed next to gas-fired power plants (i.e. natural gas) which provide heat during the expansion phase of the CAES system [28]. 3.1. Data of analysis Fig. 6 showcases the average daily solar radiation pattern and wind speed in a tropical region. As aforementioned, the supply sources are restricted not only by the intermittency issue as portrayed in Fig. 6, but also bounded by the logistic factor where there are limited area to build the generators at one DEG site. Table 3 lists the hypothetical availability data (in term of space and fuel supply) used in this case study. As shown in Fig. 7, the demand profiles input into the model are adapted from the typical energy consumption pattern of three major sectors in the tropical region. For this study, it is assumed that only AC load exists. Each DEG is designated such that its major sector consumes about 846 MWh/d and that the consumption pattern repeats daily.

Fig. 6. Profiles of solar radiation, Rad (adapted from Hashim et al. [11] and wind speed, Speed (adapted from Ref. [29] on a typical sunny day.

Several efficiency data input for the model are listed in Table 4 while the costs of equipment used in this model are listed in Table 5. The equipment lifetime, n is standardized to 25 y for all generators and 10 y for all ES system. The interest rate, j is set as 10% for the economic analysis in this study. Tables 6 and 7 shows the binary factors, DLF and TLF entitled for power transfer from a power plant to a demand while Table 8 is for STF entitled for storage power transfer for this case study. In short, the binary values reflects the rule of thumb in the grid network operation such that: (a) DLF and TLF are both 0 for power transfer within the local DEG itself (b) DLF is 1 and TLF is 0 for power transfer between DEGs which involves only the distribution level. (c) DLF and TLF are both 1 for power transfer between DEG(s) and CEG which involves the transmission and distribution levels. (d) STF is 1 indicates that power transfer to local demand are from ES at local DEG or CEG only. Cross DEGs are not allowed.

Table 3 Availability data input (hypothetical) for the model. Plant Rooftop area available, LA (105 m2)

Rotor swept area, RA (103 m2)

Annual biomass fuel available, BSA (105 tonnes/ Annual natural gas fuel available, NGSA (107 m3/ y) y)

P1 P2 P3 P4 P5 C1

1.63 1.63 -

3.06 3.27 4.36 -

6.7 7.4 0.8 1.0 -

9.30

626

W.H. Liu et al. / Energy 183 (2019) 617e629

Fig. 7. Power demand profiles, LDem from (a) commercial buildings (modified from Ponniran et al., [30]; (b) factories (continuous process and manufacturing, modified from the [31] and (c) residential community (Ho et al. [32].

4. Results and discussion General Algebraic Modelling System (GAMS, version 24.4.1) was used to perform optimization for the integrated energy system modelled. Some formulations derived in this model are non-linear (i.e. Eqs. (19), (20), (30) and (31)); the model was solved as mixed integer non-linear programming (MINLP) via the built-in BARON solver (version 14.4.0) [42] in GAMS. The program was run in Window 10 Home Single Language (version 1803), 64-bit Operating System with an Intel(R) Core(TM) i5-4210U CPU @ 1.70 GHz

2.40 GHz processor, with an installed RAM of 8.00 GB. The model was run twice, each with a different objective function: (1) minimizing total system cost (minimizing TSC) and (2) maximizing system's operational efficiency (minimizing m). The optimized results generated by GAMS for each optimization run are arranged such that Table 9 presents the costing and efficiency parameters while Table 10 tabulates the capacities of components at power plants in order to justify the cost displayed in Table 9. The program, GAMS took 400.37 s for minimizing TSC and 630.92 s for minimizing m. In view of the design of the integrated energy system, it is observed that every generator in each DEG and CEG is opted for operation by the model. To highlight, the solar PV and wind turbines have been sized to their maximum capacities in both runs of optimization (Table 10). The model chose to prioritize the energy conversion from the intermittent sources that are available locally, while constant supply of biomass and natural gas energy are opted as the back-up sources when the system is at high demand (low supply). This infers that the integration of multiple DEGs and CEG as a new form of power system network is capable to minimize the power transmission and distribution losses because more power is generated and distributed locally. The coupling of different power sources and demand in the case study also reveals an interesting and similar result in either optimization function where, P3 is the cheapest while P2 is the most expensive when the individual DEG power plant costs are compared (Table 9). This implies that the combination of wind and biomass energy to supply electricity to industrial users are the most cost-effective DEG design (about MYR 120e140 million/y). Meanwhile, deploying a single powered DEG from solar energy that serves residential users imposes the highest cost (about MYR 218e288 million/y). The design at CEG is seen different in both sets of the result. When operational efficiency is maximized (objective function (2)), an NGCC of 67 MW operates by itself at the CEG and facilities the other DEGs in the grid network. When cost is minimized (objective function (1)), CAES (122.31 MWh and 31.01 MW) is installed to support the NGCC (75.97 MW) at CEG. Compared to other power plants, the technologies at the CEG (C1) is the cheapest (refer to the cost of equipment in Table 5). Therefore, to satisfy a minimum system cost, a larger NGCC paired with CAES is designed by the model regardless of the losses that may impose on power charging and discharging at the ES system. Larger CEG means more power can be delivered to the DEGs; the sizing of biomass power plants at P4 and P5 (most expensive generator) is reduced (see Table 10). The results clearly indicate a trade-off between the cost and efficiency factor of the integrated energy system. Under the same case scenario, the costing of the integrated energy system is minimized to an approximate MYR 885 million/y with an efficiency of 77%. With a total of 2,022.957 GWh/year of electricity generated, the levelized cost is revealed as MYR 0.44/kWh. On the other side when efficiency is concerned over the cost, the 80% efficient integrated system costs around MYR 1,110 million/y. Since there is only a small variance (about 3%) in the efficiency values, the integrated energy system is suggested to size and operate following the system configuration results in objective (1), where a 75.97 MW NGCC coupled with CAES (122.31 MWh and 31.01 MW) are installed at the CEG to support the entire grid network. An approximate MYR 225 million/y can be saved. 5. Conclusion An MINLP model was successfully developed in this research to study and understand the integrated energy system from the economic and energy conservation perspectives. The flow of electricity

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627

Table 4 Efficiency data input for model. Parameter Energy storage system Charging/discharging efficiency, EffES Lead acid battery Compressed air energy storage (CAES) Depth of discharge, DoD Converter Converter efficiency, Effiv Power generator Solar PV modulea efficiency, EffPV Maximum power coefficient of wind turbine, Cp Air densityb, r Heat rate, HRo Biomass bubbling fluidized bed (BBFB), HRBM,o Natural gas combined cycle (NGCC), HRNG,o Net calorific value Biomassc, HVBM Natural gasd, HVNG Grid network efficiency loss Distribution loss, DL Transmission loss, TL a b c d

Value (Unit)

Source

88% 85% 80%

Mohammad Rozali et al. [33]

90%

Ho et al. [34]

15% 0.35 1.225 kg/mc

Ho et al. [34] WINDExchange [35] Helmenstine [36]

14,242.5 MJ/MWh

U.S. EIA [37]

Ho et al. [34]

6,963 MJ/MWh

11,450 MJ/tonne 36.6 MJ/mc

Hassan [38] Engineering Toolbox [39]

50% 17%

Parmar J. [40]. Bamigbola et al. [41]

Mono-crystalline type. According to International Standard Atmosphere (ISA) at sea level, 15  C. Agriculture residues e.g. corn stover, rice straw. U.S. Standard.

Table 5 Equipment cost data input for the model. Type of generator

Lifetime, nGen Capital cost, CC (MYR/kW) (y)

Fixed O&M cost, FOM (MYR/ kW.y)

Variable O&M cost, VOM (MYR/MWh)

Solar PV module (fixed)a Wind turbine (onshore)a Biomass bubbling fluidized bed (BBFB)a Natural gas combined cycle (NGCC)a Type of energy storage

25

11,138.07 7,827.09 20,787.45 4,078.26 Capital cost (energy-related) (MYR/kWh)

97.58 165.55 458.70 45.87 Capital cost (power-related) (MYR/kW)

17.51 14.6 O&M cost (MYR/kW.y)

1,332 125.1

1,998 1,459.50

222 20.85

Lifetime, nES (y)

Lead acid batteryb Compressed air energy storage (CAES) with salt as the mediumc

10

Note: USD 1 ¼ MYR 4.17 (as of October 2018); Sources. a U S. EIA [37]. b Theo et al. [14]. c IRENA [28].

Table 6 Binary value of DLF for each distribution route.

Table 8 Binary value of STF for each route of power transfer between ES and demand.

Distribution Route

Q1

Q2

Q3

Q4

Q5

Storage power transfer route

Q1

Q2

Q3

Q4

Q5

P1 P2 P3 P4 P5 C1

0 1 1 1 1 1

1 0 1 1 1 1

1 1 0 1 1 1

1 1 1 0 1 1

1 1 1 1 0 1

P1 P2 P3 P4 P5 C1

1 0 0 0 0 1

0 1 0 0 0 1

0 0 1 0 0 1

0 0 0 1 0 1

0 0 0 0 1 1

Table 7 Binary value of TLF for each transmission route. Transmission Route

Q1

Q2

Q3

Q4

Q5

P1 P2 P3 P4 P5 C1

0 0 0 0 0 1

0 0 0 0 0 1

0 0 0 0 0 1

0 0 0 0 0 1

0 0 0 0 0 1

in the new form of integrated energy system has been transitioned from uni-directional to multi-directional in which the sharing of power between DEGs or DEG-CEG at different regions are made possible. The model was compiled such that two different objective functions involving system cost and operational efficiency can be executed using the same coding. Based on the result, the integrated energy system is suggested to design based on cost-effectiveness. When comparing between the two optimized parameters, the

628

W.H. Liu et al. / Energy 183 (2019) 617e629

Table 9 GAMS results for optimization objective: (1) minimizing TSC and (2) minimizing m. Objective (1): Minimizing total system cost (minimizing TSC) Power plant

P1

P2

P3

P4

P5

C1

Annualized cost of power plant, Cost (million MYR/y) 159.29 Total annualized system cost, TSC (million MYR/y) 884.45 Overall system operational efficiency, TSOE (%) 76.92 Objective (2): Maximizing system operational efficiency (minimizing m) Power plant P1

218.53

120.72

137.85

192.91

55.14

P2

P3

P4

P5

C1

Annualized cost of power plant, Cost (million MYR/y) Total annualized system cost, TSC (million MYR/y) Overall system operational efficiency, TSOE (%)

288.21

140.22

195.97

261.92

38.94

184.32 1,109.60 80.25

Table 10 Comparison of optimal capacities of power plants for optimization objective: (1) minimizing TSC and (1) minimizing m. Power plant Objective function Generator Solar PV, SPVCap (MWp) Wind turbine, WTCap (MW) BBFB, BGenCap (MW) NGCC, NGGenCap (MW) Energy storage Energy-related capacity, BERCactual (MWh) Power-related capacity, BPRC (MW)

P1 (1)

(2)

100.5 -

100.5

73.79 18.59

116.45 45.13

P2 (1)

(2)

111.0 -

111.0

208.71 47.98

413.03 93.30

system cost shows a higher sensitivity than that of energy efficiency. For the designated case study, the best optimal integrated energy system is designed to have 76 MW NGCC coupled with a CAES of 122.31 MWh and 31.01 MW capacities as CEG to support the five DEGs of different energy sources and demand. With the current market price and the maturity of technologies available, the integrated energy system is able to achieve an operational efficiency up to 77% with an annual cost of about MYR 885 million. The levelized cost is calculated as MYR 0.44/kWh. In this research, deterministic data were used in the case study. Nevertheless, electricity demands are not always consistent each and every day and the source availabilities may be low or high depending on the seasonal period throughout the year. Recommended as one of the future work, a stochastic model should be performed to reflect the real scenario of energy supply-demand in the grid power network. It also is suggested that the current energy model can be extended into a multi-period model. The results would be more accurate with a longer time of analysis (e.g. on a monthly or yearly basis) and with more time variation (e.g. between weekdays or weekend, seasons of the year). In addition to that, the model illustration the impact of sharing energy between network by considering distribution and transmission losses. However, a more accurate representation of the losses often depends on the distance between the network, the demand, and power plants. It is therefore to consider topology and spatial information in the model. The energy demand that was presented for each CEG was hypothetical and an actual energy demand profile would have a mix of demand from the residential, commercial, and industries. These limitations will be taken into consideration in a future studies. Nevertheless, this model that considers different operation of the DEG and CEG will be beneficial to energy engineers, town planners, policymakers in creating a more sustainable and energysecured living vicinity while promoting more shares of renewable energy in the generation fuel mix. Acknowledgement The authors acknowledge the Ministry of Higher Education (MOHE) and Universiti Teknologi Malaysia (UTM) for their financial

P3 (1)

(2)

22.36 33.87 -

22.36 33.93

0.87 0.35

49.24 14.13

P4 (1)

(2)

12.0 22.36 30.74 -

12.0 22.36 52.99

27.02 7.32

8.97 7.17

P5 (1) 12.0 46.57 83.78 37.95

(2) 12.0 81.61

21.12 12.57

C1 (1)

(2)

75.97

66.66

122.31 31.01

-

support through research grant number R.J130000.7351.4J362, Q. J130000.2546.19H05, and Q. J130000.3551.06G47. This research has also been supported by EU project Sustainable Process Integration Laboratory e SPIL, funded as project No. CZ.02.1.01/0.0/0.0/15_003/ 0000456, by Czech Republic Operational Programme Research and Development, Education, Priority 1: Strengthening capacity for quality research in a joint collaboration agreement with UTM, Malaysia. References [1] Momoh JA, Meliopoulos S, Saint R. Centralized and distributed generated power systems e a Comparison approach. Arizona: Howard Universitiy and Power System Engineering Research Center (PSERC); 2012. [2] Ogunjuyigbe ASO, Ayodele TR, Akinola OO. Impact of distributed generators on the power loss and voltage profile of sub-transmission network. Journal of Electrical Systems and Information Technology 2016;3(1):94e107. [3] Allan G, Eromenko I, Gilmartin M, Kockar I, McGregor P. The economics of distributed energy generation: a literature review. Renew Sustain Energy Rev 2015;42:543e56. [4] Sreeraj ES, Chatterjee K, Bandyopadhyay S. Design of isolated renewable hybrid power systems. Sol Energy 2010;84:1124e36. [5] Khatib T, Mohamed A, Sopian K. Optimization of a PV/wind micro-grid for rural housing electrification using a hybrid iterative/genetic algorithm: case study of Kuala Terengganu, Malaysia. Energy Build 2012;47:321e31. [6] Lee J, Chen C, Chen H. A mathematical technique for hybrid power system design with energy loss considerations. Energy Convers Manag 2014;82: 301e7. [7] Wan Alwi SR, Mohammad Rozali NE, Abdul-Manan Z, Klemes JJ. A process integration targeting method for hybrid power systems. Energy 2012;44: 6e10. [8] Mohammad Rozali NE, Wan Alwi SR, Abdul Manan Z, Klemes JJ, Hassan MY. Process integration of hybrid power systems with energy losses considerations. Energy 2013;55:38e45. [9] Sharafi M, ElMekkawy TY. Stochastic optimization of hybrid renewable energy systems using sampling average method. Renew Sustain Energy Rev 2015;52: 1668e79. [10] Ho WS, Khor CS, Hashim H, Lim JS, Ashina S, Herran DS. Optimal operation of a distributed energy generation system for a sustainable palm oil-based ecocommunity. Clean Technol Environ Policy 2015a;17:1597e617. [11] Hashim H, Ho WS, Lim JS, Macchietto S. Integrated biomass and solar town: incorporation of load shifting and energy storage. Energy 2014;75:31e9. [12] Kumaravel S, Ashok S. An optimal stand-alone biomass/solar-PV/Pico-hydel hybrid energy system for remote rural area electrification of isolated village in Western-Ghats region of India. Int J Green Energy 2012;9:398e408. [13] Fadaeenejad M, Radzi MAM, AbKadir MZA, Hizam H. Assessment of hybrid renewable power sources for rural electrification in Malaysia. Renew Sustain Energy Rev 2014;30:299e305. [14] Theo WL, Lim JS, Wan Alwi SR, Mohammad Rozali NE, Ho WS, Abdul-Manan Z.

W.H. Liu et al. / Energy 183 (2019) 617e629

[15]

[16]

[17] [18]

[19]

[20]

[21]

[22]

[23]

[24] [25]

[26]

[27]

[28]

An MILP model for cost-optimal planning of an on-grid hybrid power system for an eco-industrial park. Energy 2016;116:1423e41. Gao J, Kang J, Zhang C, Gang W. Energy performance and operation characteristics of distributed energy systems with district cooling systems in subtropical areas under different control strategies. Energy 2018;153:849e60 {, #2@@hidden}. Doagou-Mojarrad H, Gharehpetian GB, Rastegar H, Olamaei J. Optimal placement and sizing of DG (distributed generation) units in distribution networks by novel hybrid evolutionary algorithm. Energy 2013;54:129e38. Chang KH, Lin G. Optimal design of hybrid renewable energy systems using simulation optimization. Simulat Model Pract Theor 2015;52:40e51. Wu Q, Ren H, Gao W, Weng P, Ren J. Coupling optimization of urban spatial structure and neighborhood-scale distributed energy systems. Energy 2018;144:472e81. Sultana U, Khairuddin AB, Sultana B, Rasheed N, Qazi SH, Malik NR. Placement and sizing of multiple distributed generation and battery swapping stations using grasshopper optimizer algorithm. Energy 2018;165:408e21. GAMS Development Corporation. An introduction to GAMS [online]. Available at: https://www.gams.com/products/introduction/. [Accessed 28 October 2018]. Liu WH, Wan Alwi SR, Hashim H, Muis ZA, Klemes JJ, Mohammad Rozali NE, Lim JS, Ho WS. Optimal design and sizing of integrated centralized and decentralized energy systems. Energy Procedia 2017;105:3733e40. Ho WS, Hashim H, Hassim MH, Muis ZA, Shamsuddin NLM. Design of distributed energy system through electric system cascade analysis (ESCA). Appl Energy 2012;99:309e15. Liu WH, Ho WS, Lee MY, Hashim H, Lim JS, Hoo PY, Klemes JJ. Optimising thermal power plant with generation flexibility and heat rate factor. Chemical Engineering Transactions 2018;70:1981e6. Blank L, Tarquin A. Engineering economy. seventh ed. New York: McGrawHill; 2012. Norbu S, Bandyopadhyay S. Power Pinch Analysis for optimal sizing of renewable-based isolated system with uncertainties. Energy 2017;135: 466e75. International Energy Agency (IEA). Power generation from coal: measuring and reporting efficiency performance and CO2 emissions. Paris: International Energy Agency; 2010. Roberts JJ, Marotta Cassula A, Silveira JL, da Costa Bortoni E, Mendiburu AZ. Robust multi-objective optimization of a renewable based hybrid power system. Appl Energy 2018;223:52e68. International Renewable Energy Agency (IRENA). Electricity storage and renewables: costs and markets to 2030. Abu Dhabi: International Renewable Energy Agency; 2017.

629

[29] Teh C. Possibility of electricity from wind energy in Malaysia: some rough calculations [online]. Available at: http://www.christopherteh.com/blog/ 2010/11/wind-energy/. [Accessed 2 October 2018]. [30] Ponniran A, Mamat NA, Joret A. Electricity profile study for domestic and commercial sectors. International Journal of Integrated Engineering 2012;4(3):8e12. [31] University of Bath and Element Energy. Final report for western power distribution - modelling demand profiles in the I&C sector. 2015 [United Kingdom: Bath and London]. [32] Ho WS, Hashim H, Mohammad Rozali NE, Muis ZA, Lim JS. Electric system cascade analysis (ESCA): cost and efficiency trade-off and optimization. Chemical Engineering Transactions 2015b;45:517e22. [33] Mohammad Rozali NE, Wan Alwi SR, Abdul Manan Z, Klemes JJ, Hassan MY. A process integration approach for design of hybrid power systems with energy storage. Clean Technol Environ Policy 2015;17:2055e72. [34] Ho WS, Mohd Tohiq MZW, Hashim H, Muis ZA. Electric system cascade analysis (ESCA): solar PV system. Electrical Power and Energy Systems 2014;54:481e6. [35] WINDExchange. Small wind guidebook. U.S. Department of Energy; 2018 [online]. Available at: https://windexchange.energy.gov/small-windguidebook. [Accessed 23 October 2018]. [36] Helmenstine AM. What is the density of air at STP? [online]. Available at: https://www.thoughtco.com/density-of-air-at-stp-607546. [Accessed 23 October 2018]. [37] U. S. Energy Information Administration (EIA). Capital cost Estimates for utility scale electricity generating plants. Washington DC: Department of Energy; 2016. [38] Hassan MNA. GHG emissions and costs of developing biomass energy in Malaysia: implications on energy security in the transportation and electricity sector. Ph.D. Thesis. Pittsburgh, PA: Carnegie Mellon University; 2012. [39] Engineering ToolBox. Fuels - higher and lower calorific values [online]. Available at: https://www.engineeringtoolbox.com/fuels-higher-calorificvalues-d_169.html. [Accessed 23 October 2018]. [40] Parmar J. Transmission and distribution: total losses in power distribution and transmission lines [online]. Available at: http://www.electrical-engineeringportal.com/total-losses-in-power-distribution-and-transmission-lines-1. [Accessed 17 May 2016]. [41] Bamigbola OM, Ali MM, Oke MO. Mathematical modelling of electric power flow and the minimization of power losses on transmission lines. Appl Math Comput 2014;241:214e21. [42] Tawarmalani M, Sahinidis NV. A polyhedral branch-and-cut approach to global optimization. Math Program 2005;103(2):225e49.