A process integration targeting method for hybrid power systems

Energy 44 (2012) 6e10

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A process integration targeting method for hybrid power systems Sharifah Rafidah Wan Alwi a, *, Nor Erniza Mohammad Rozali a, Zainuddin Abdul-Manan a, Jirí Jaromír Klemes b a

Process Systems Engineering Centre (PROSPECT), Faculty of Chemical Engineering, Universiti Teknologi Malaysia, 81310 Johor Bahru, Malaysia Centre for Process Integration and Intensification e CPI2, Research Institute of Chemical and Process Engineering, Faculty of Information Technology, University of Pannonia, Egyetem u. 10, H-8200 Veszprém, Hungary

b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 21 November 2011 Received in revised form 20 December 2011 Accepted 2 January 2012 Available online 28 January 2012

Pinch Analysis is a well-established methodology of Process Integration for designing optimal networks for recovery and conservation of resources such as heat, mass, water, carbon, gas, properties and solid materials for more than four decades. However its application to power systems analysis still needs development. This paper extends the Pinch Analysis concept used in Process Integration to determine the minimum electricity targets for systems comprising hybrid renewable energy sources. PoPA (Power Pinch Analysis) tools described in this paper include graphical techniques to determine the minimum target for outsourced electricity and the amount of excess electricity for storage during start up and normal operations. The PoPA tools can be used by energy managers, electrical and power engineers and decision makers involved in the design of hybrid power systems. Ó 2012 Elsevier Ltd. All rights reserved.

Keywords: PoPA (Power pinch analysis) Process integration Hybrid systems Renewable energy Minimum outsourced electricity supply Minimum electricity targets

1. Introduction Nowadays, hybrid power generation system is becoming more popular due to the advances in renewable energy technologies. Solar and wind are the most widely utilised renewable energy sources in generating the electric power. This paper extends the Pinch Analysis concept used in Process Integration to establish the minimum outsourced electricity targets in an off-grid HPS (Hybrid Power Systems) by using PoPA (Power Pinch Analysis). This entails a basic understanding of how the power generated from renewable sources is used to satisfy the electricity demands in an HPS. An HPS combines the use of intermittent electrical energy from various sources including the RE (renewable energy) (RE as solar photovoltaic, wind, wave, biomass and geothermal) with “purchased” or outsourced power that are either supplied from the grid or selfgenerated onsite using fuel in the case of power deficit. This makes the HPS more suitable for industrial applications compared to using individual RE that can either be too dependent on one RE source (e.g. in the case of biomass) only, or cater for small loads (i.e. in the case of solar) only. In an HPS, power generated by RE is

* Corresponding author. E-mail address: [email protected] (S.R. Wan Alwi). 0360-5442/$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved. doi:10.1016/j.energy.2012.01.005

simultaneously used to satisfy the demand load. Any excess power can be stored continuously in batteries and discharged when power is needed. The battery, RE system and electricity from the grid or generator are connected to a controller regulator to ensure an optimal operation of the entire HPS. Usually the intermittent power generated from RE and batteries use DC (direct current) and for this reason a DC/DC or a DC/AC (Alternating current) converter may be needed. 2. A review on graphical pinch analysis targeting tools for resource conservation Pinch Analysis has so far been applied for the design of optimal recovery networks involving various resources including heat [1], combined heat and power [2], mass [3], water [4], carbon [5], properties [6] and gas [7]. The basis of Pinch Technology was first established in the design of optimal HEN (heat exchange networks). Hohmann [8] and Linnhoff and Flower [9] introduced the temperature versus enthalpy diagram, which provided the basis for later development of CCs (Composite Curves) for utility targeting. The CCs, which have been widely used for many years and proven as a basic tool of Process Integration, can however be further developed and extended. For example, Wan Alwi and Manan [10] introduced the STEP (Streams Temperature vs. Enthalpy Plot)

S.R. Wan Alwi et al. / Energy 44 (2012) 6e10

technique which represents continuous individual hot and cold streams on a shifted temperature versus enthalpy diagram. Abbood et al. [11] recently combined the numerical and visualisation advantages of CCs and PTA (Problem Table Algorithm) into a single graphical tool known as the GDT (Grid Diagram Table). The GDT has been based on the CCs geometry and is represented on a stream interval temperature scale. Another interesting extension is the CPA (Chemical Pinch Analysis) by Lavric et al. [12]. The authors addressed the importance of CPA in the synthesis of chemical reactors or separation trains for a whole plant. The heat pinch analysis approach was later extended to optimal power networks design. Linnhoff et al. [1] introduced the GCC (Grand Composite Curve) as a tool for heat and power systems integration. Dhole and Linnhoff [13] modified the GCC to construct SSSP (Site Source-sink Profiles) for site-wide targeting of fuel, cogeneration, emissions and cooling. Klemes et al. [2] use the Carnot factor versus enthalpy plot known as SUGCC (Site Utility Grand Composite Curves) to determine the different potentials for implementing combined heat and power systems. They later extended the technique for site-wide heat and power integration. Perry et al. [14] applied Total Site targeting methodology in the LIES (Locally Integrated Energy Sector) design that involves both heat and power. A new tool that aids data extraction when conducting Pinch Analysis study called WinGEMS (Windows General Energy and Material balance System) was developed by the University of Idaho, which later applied by Atkins et al. [15] to model a PM (paper machine). The stream data for a number of PM operating states generated from WinGEMS is used by the authors to perform total site analysis using pinch techniques. Varbanov and Klemes [16] integrated renewables into the corresponding total site CHP energy systems with Site Profiles and Site Composite Curves for a set of time intervals, referred to as “Time Slices”. Recently Varbanov and Klemes [17] extended the heat cascade principle with inclusion of heat storage to significantly reduce the targets for fossil-fuel in total sites. Another main study that applied pinch analysis targeting in total site is done by Botros and Brisson [18]. The authors improve the targeting by including sensible heating of steam in the Composite Curves. Further advances in Pinch Analysis have been made in energysector planning under carbon emission constraints by Tan and Foo [5]. The minimum amount of carbon energy needed to meet the specified emission limits was determined using a plot of energy demand versus carbon emission limit. Crilly and Zhelev [19] extended the graphical optimisation concept to the Irish electricity generation sector. Foo et al. [20] later applied the Water Cascade Analysis (WCA) algebraic targeting technique to carbon management. Sadiq et al. [21] recently presented a method that used CC to target the minimum carbon requirement as well as emissions and to design the maximum carbon heat exchange network. Kang et al. [22] presented an integrated energy system model which was subjected to a CO2 emission constraint. The developed approach is applicable to a wide range of energy systems. The synthesis of MENs (Mass Exchange Networks) based on Pinch Analysis technique was introduced by El-Halwagi and Manousiouthakis [3]. The authors proposed the cumulative mass exchanged versus composition plot and the Mass-Exchange Pinch Diagram for design of a maximum mass exchange network. ElHalwagi et al. [23] presented a more advanced graphical targeting technique known as MRPD (Material Recovery Pinch Diagram). The MRPD is a plot of material loads versus flow rates of each process sink and source to determine the minimum feed needed for fresh resources and minimum waste discharge from a network. WPA (Water Pinch Analysis) technique initiated by Wang and Smith [4] is a special case of mass integration. Central to the WPA

7

development is the LCC (Limiting Composite Curve) that was used to establish the minimum water and wastewater targets. The LCC is a plot of water-using processes on a flow rate versus contaminant concentration diagram. Dhole et al. [24] overcame the limitations of the LCC by introducing the Water Source and Demand Composite Curves. Hallale [25] extended the work of Dhole et al. [24] by proposing the WSD (Water Surplus Diagram) as an alternative graphical method for water targeting. The WSD is a plot of water purity versus flow rate of water sources and demands. The approach published by Hallale [25] provides targets that are unique and independent of any assumed mixing arrangement, however it still requires some trial-and-error steps during water targeting. The pinch concept is also addressed in the simultaneous water and energy management technique by Savulescu et al. [26,27]. The authors considered both systems with no water reuse and maximum reuse of water. Shelley and El-Halwagi [6] applied the Pinch Analysis technique to track the properties or functionalities of process streams that cannot be addressed by conventional mass integration techniques. The authors constructed the Source-Sink Mapping Diagram that represents sources, sinks, and mixing on a ternary cluster diagram for tracking the functionalities of streams. Application of Pinch Analysis in the design and management of utility gas networks began with the work of Alves [7] who proposed a graphical technique to target the minimum hydrogen utilities. CC representing the mass balance of hydrogen sources and sinks in a hydrogen network were mapped on a plot of total gas flow rate versus purity. Application of pinch analysis for solid systems was conceptualised by Soh et al. [28] who introduced the SSCC (SourceSink Composites Curves), SSAC (Source-Sink Allocation Curves) and NAD (Network Allocation Diagram) for simultaneous targeting and design of a maximum paper recycling network. The defining and differentiating concept behind PoPA introduced in this work is a graphical representation of composite electricity variation with time, and that electricity can be cascaded to a future, but not to the previous time. This is analogous to the Heat Pinch Analysis concept based on the thermodynamic principles that heat can only be naturally transferred from a higher to a lower temperature. There is a fundamental difference between PoPA and CHP (Combined Heat and Power) systems, which is similar to the Heat Pinch Analysis tool and it is as follows: While the CHP system is based on Heat Pinch Analysis temperature (
C) versus enthalpy (H) relation, the PoPA extends it to the analysis of time (h) versus electricity usage (kWh). 3. Methodology This section describes the methodology for Power Pinch Analysis which consists of two main steps, i.e. (1) Data extraction, and (2) Targeting the MOES (minimum outsourced electricity supply) and the AEEND (available excess electricity for next day). 3.1. Step 1: data extraction The first step of a Pinch Analysis application for resource conservation typically involves data extraction of a system’s sources (resource availability) and demands (resource requirements). In the case of hybrid power systems, power sources are the instantaneous onsite electricity generation from the available renewable energy sources such as solar photovoltaic, wind or biomass. The power demands represent equipment electricity consumption that can be determined from equipment power ratings. The power sources and power demands are recorded at the time they are available. Tables 1 and 2 show the power sources and demands for the illustrative Case Study 1 that represents the case of an off-grid

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S.R. Wan Alwi et al. / Energy 44 (2012) 6e10

Power source

Solar Wind Biomass

Time, h From

To

8 2 0

18 10 24

Time interval, h

Power rating generated, kW

Electricity generation, kWh

10 8 24

5 5 7

50 40 168

hybrid power system. In this case, excess power will be stored in a battery system. Electricity generated and consumed are calculated using Eqs. (1) and (2). Initially, the demands consumed 268 kWh of outsourced electricity supply daily. The maximum power demand of 19 kW occurs between 8 and 18 h.

Electricity generationðkWhÞ ¼ Power generatedðkWÞ  Time intervalðhÞ

(1)

Electricity consumptionðkWhÞ ¼ Power ratingðkWÞ  Time intervalðhÞ

(2)

To better illustrate the PoPA method using the illustrative case study, the average value of the generated renewable energy is considered for the specified time range. For example, solar is assumed to be available from 8 am to 6 pm at an average power rating of 5 kW. In real situations, the solar power rating may change from one time interval to another due to the weather and solar intensity fluctuations throughout the day. For a more accurate determination of the minimum electricity targets using PoPA method, it is recommended that the designer use the hourly average power generated by an RE source if such data is available. 3.2. Step 2: targeting the minimum outsourced electricity supply and available excess electricity for the next day This section introduces a new technique known as the PCC (Power Composite Curves) to determine the maximum power transfer from power sources to power demands, the MOES and AEEND for storage. The PCC is a graphical approach that is based on PACC (Pinch Analysis Composite Curves) [1]. 3.2.1. Power composite curves The PCC can be constructed as follows:

Time, h

Table 1 Power sources for illustrative Case Study 1.

Power demand appliances

Time, h From

To

Appliance Appliance Appliance Appliance Appliance

0 8 0 8 8

24 18 24 18 20

1 2 3 4 5

Power rating, kW

Electricity consumption, kWh

24 10 24 10 12

3 5 2 5 4

72 50 48 50 48

D4

D2

D5

S1

S3

D3 S2

0

100

200

300

400

500

600

Fig. 1. Individual power source and demand lines for illustrative Case Study 1.

a given time interval. The same procedure is repeated for the rest of the system time intervals in order to yield the source and demand composite curves for the entire system. 4) The pinch point can be determined by shifting the source composite curve to the left hand side until it touches the Demand Composite Curve. Note that the Source Composite Curve has to be on the right hand side of Demand Composite Curve since energy can only be transferred from a current, to a later time interval. A complete PCC is shown in Fig. 2. 5) The excess demand line below the pinch point gives the MOES needed to be purchased during a system start up, and the excess electricity source above the pinch gives the AEEND. MOES and AEEND targets are 18 kWh and 8 kWh in illustrative Case Study 1. 6) Now it is possible to determine the amount of MOES needed during daily operation. This can be done by merging the PCC from Day 1 with the next day’s PCC by linking the end of the SCC from the current-day PCC to the beginning of the source composite of the next day’s PCC. Merging the current-day PCC with the next day PCC gives the CPCC (Continuous PCC) e see Fig. 3. They are two possible scenarios for daily operations assumed: a. Scenario 1 is when AEEND (8 kWh) is less than or equal to MOES (18 kWh) for Day 1. It indicates that the amount of excess electricity from previous day can be used to reduce the amount of outsourced electricity needed for the next day. Illustrative Case study 1 is classified as Scenario 1. The excess demand line from the second PCC onwards will give the daily outsourced electricity supply needed to be purchased during normal operations. The CPCC reduces the

AEEND

Time, h

Time interval, h

D1

Electricity, kWh

1) Y-axis represents the time scale from ‘0’ to ‘24’ h while x-axis represents the electricity generation or consumption in kWh. 2) An individual source or demand line is plotted as shown in Fig. 1. Note that the gradient of the line can be computed from the inverse of power rating for the corresponding demand or source. 3) The composite source (or demand) line is obtained from the sum of electricity sources (or electricity demands) within

Table 2 Power demands for illustrative Case Study 1.

24 22 20 18 16 14 12 10 8 6 4 2 0

24 22 20 18 16 14 12 10 8 6 4 2 0

Pinch

0 50 100 150 200 MOES needed during start up Electricity, kWh

Source Composite

Demand Composite

250

300

Fig. 2. PCC (Power Composite Curves) for a 24 h operation (illustrative Case Study 1).

S.R. Wan Alwi et al. / Energy 44 (2012) 6e10

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Fig. 3. CPCC (Continuous Power Composite Curves) for illustrative Case Study 1.

Fig. 4. CPCC (Continuous Power Composite Curves) for illustrative Case Study 2.

MOES to 10 kWh from 268 kWh. The off-grid hybrid power system with storage reduces the need for conventional electricity by 96.3%. b. Scenario 2 is a case where AEEND is more than MOES. It indicates that the amount of excess electricity sources from the previous day is larger than the amount of outsourced electricity needed for the next day. For Scenario 2, if AEEND is continuously cascaded to the next day, it will cause accumulation of energy inside the storage system. Scenario 2 is explained using illustrative Case Study 2. In this case, the wind power source rating between times 2e10 h in illustrative Case Study 1 is increased from 5 kW to 6.5 kW. Fig. 4 shows the CPCC for Scenario 2. The AEEND and MOES are 8 kWh and 6 kWh respectively for the previous day. Since the system only requires 6 kWh instead of 8 kWh of MOES for the next day, the excess 2 kWh of electricity generated from the previous day cannot be stored and is therefore wasted. Fig. 4 clearly shows the CPCC with the wasted electricity source represented by the excess source

line after integration with the power demand curve for the next day. In this case, the owner may want to consider buying a smaller-capacity hybrid system.

4. Conclusion and future work A new method to determine the minimum electricity targets called PoPA has been established for hybrid power systems comprising of renewable energy sources. The PoPA can be used to determine the minimum amount of outsourced electricity and the excess electricity source in a day during start up and normal operations. The PoPA provides useful visualisation tools for energy managers, electrical and power engineers as well as decision makers to better understand the profile of hybrid power systems. Decision makers can conveniently determine the electricity targets during start up and normal operations and manipulate the profile of electricity sources and demands to cater for various scenarios such as demand inconsistency as well as low or maximum RE

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availabilities. However, there is a limitation in the presented method since it assumes 100% power transfer and battery storage efficiency. This cannot be achieved in actual situations. The work has been in progress to include the effect of power losses to enhance the PoPA accuracy. Detailed studies on the effect of equipment load-shifting on the amount of outsourced electricity needed, the battery capacity and the maximum power demand are also in progress. Acknowledgements The authors would like to thank the Universiti Teknologi Malaysia (UTM) and Ministry of Higher Education (MOHE) of Malaysia for providing the financial support through the Research University Grant to complete this research. Abbreviations

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