An integrated strategic and tactical decision of installing renewable ...

The International Conference on Sustainable Practices in Engineering (ICSPE), August 2015, USA

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An integrated strategic and tactical decision of installing renewable energy sources to meet energy demands considering sustainability Mohammad Mahdi Nasiri*, Iman Shokr * Corresponding Author: [email protected] School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran

Abstract Exploitation of the renewable energy sources such as hydro power, solar, wind, and geothermal, is a strategy to meet energy demand especially in rural and remote areas. Advantages of renewable energy sources include less greenhouse gas emission and less pollution. This study has two aims. The first is to find optimal location and renewable energy sources as a strategic decision. The second is allocating demand points to the determined locations of RES and determining the amount of energy which should be produced for each of demand points in time horizon as tactical decision. These decisions should be made with considering sustainability issues such as job creation and CO2 emissions. In order to address the above mentioned problem, a new mixed integer programming is proposed and a numerical example is investigated. Keywords: renewable energy resources, location, allocation, sustainability

1. Introduction Renewable energy sources (RES) are powerful tools for supplying the energy demand, especially in rural and remote areas. Renewable energy sources include hydro power, solar, wind, geothermal, biomass, and marine energies. Advantages of using RES are less greenhouse gas emission and less pollution. In spite of the advantages of renewable energy source, there is a lack of exploitation of renewable sources due to high installation and operation costs and despite of the limited amount of fossil fuels, the needed energy of the world mostly provided by fossil fuels.


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The goal of energy planning is to utilize sources of energies in an optimal way. There are two different levels of energy planning: Centralized and Decentralized level [1]. Centralized energy planning (CEP) is a conventional energy planning in urban. The amount of energy produced will be distributed in urban areas using an integrated network. Conversely, decentralized energy planning (DEP) meets the demand of rural and remote areas where natural sources of energy exist. Decentralized energy planning is a concept of planning the limited energy sources to meet the demand of energies. Rural areas are influenced by using DEP, caused to saving, cost reduction and increasing supply reliability. Also, decentralized electrification can reduce disparity in rural areas [2] because there are much more opportunities than urban areas to use RES. Because of the increasing emissions and air pollution in addition to global climate change and increasing energy consumption, renewable energy sources are considered as an alternative for fossil fuels [3]. Liu (2014) states RES as a solution for mitigating the climate changes and environmental pollution [4]. San Cristóbal (2012) presented a goal programming in order to find the optimal location of renewable energy systems. Criteria like the amount of power generated, investment, CO2 emissions, job creation, operation and maintenance costs and social acceptability are considered in their study [5]. Chang (2015) improved the usefulness of Cristóbal (2012) model with the multi-choice goal programming and a capacity expansion planning problem is solved to demonstrate the efficiency of the proposed model [15]. Rentizelas et al. (2010) studied the problem of finding the optimum location of bio-energy generation facilities. The problem is modeled with the non-linear optimization method. Genetic algorithm is exploited to find the solutions [9]. Saavedra et al. (2011) investigated on the positioning of wind turbines in wind farms. A novel evolutionary algorithm is proposed to solve the model. The other aspect of their studying is introducing a greedy heuristic algorithm in order to obtain an initial solution for the problem [10]. Cundiff et al (1997) developed a linear optimization model to minimize a cost function with considering of biomass logistic activities, construction and expansion costs of storage facility, and cost of violating storage capacity [11]. Tatsiopoulos and Tolis (2003) modeled the problem of a cotton-stalk supply chain (the biomass) with linear programming optimization. The centralized and decentralized scenarios are studied during their model application [12]. Voivontas et al. (2001) investigated in a GIS-based model to find the locations of bio-energy conversion facility by developing a two-stage optimization model.


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The aim of the study was to reduce the biomass logistic costs and eliminating biomass warehousing needs [13]. Johnson et al. (2012) summarized recent studies focused to find optimal locations a forest biomass-to-biofuel facility. They concluded that optimization using mixed integer programming is a preferred method in location concepts of biofuel site [14]. Multi criteria decision analysis (MCDA) is a popular approach in related decision making problems in sustainable energy [2] because of considering different sustainability goals in decision making processes. Aras et al. (2004) exploited the AHP method due to find the optimal location for wind power stations. Wind-speed and wind-path are stated as important criteria when it is desirable to locate wind stations [6]. Mladineo et al. (1987) studied the small scale hydro plants with the aim of selecting locations for constructing them. Their studied criteria include of technical, social, environmental, and economical for using with PROMETHEE method [7]. Kaya and Kahraman (2010) investigated on renewable energies with the aim of selecting the best alternatives among energy technologies and find the optimal energy production sites with the VIKOR-AHP method [8]. Choudhary and Shankar (2012) proposed a fuzzy AHP-TOPSIS framework to find the thermal power plant location. They stated social, technical, economical, environmental and political (STEEP) considerations to identify the potential locations. Fuzz concept is used to face with vague and incomplete knowledge, also [16]. Azadeh et al. (2008) exploited from data envelopment analysis (DEA), principal component analysis (PCA) and numerical taxonomy (NT) in order to find best-possible locations for construction solar plant [17]. Kazemi and Rabbani (2013) proposed an integrated decentralized energy planning model considering demand-side management and environmental measures [18]. Gap analysis: Renewable energy planning and decentralized energy planning have a vast literature, but location and allocation problems in RES are not considered sufficiently by past researchers and there is a need to study this area. Through this study the aim is covering this gap. Therefore, an integrated location allocation model considering different periods and focusing on minimizing the costs will be presented.


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Problem Description: The problem is to find optimal locations among available potential locations as supply centers. A RES can be installed in each of these supply centers to meet energy demands of a specific area. We assumed the area is divided into some neighbors as demand points and it is desired to cover all the demand points with supply centers in the time horizon. Thus, the problem faces with strategic and tactical decisions. Strategic issue is to find optimal locations, as supply centers, to install RES there and tactical issue is covering demand points during a time horizon by that supply center. Assumptions: 

One RES can be installed in each supply center.



Sustainability limitations should be considered.



Each unit of the unmet demands faces with a penalty cost.

Indices: i: denotes the renewable energy sources (RES) j: denotes the available locations for installing RES (supply centers). t: denotes time periods. k: demand points of energy Parameters: ci: Cost of producing energy by i-th RES Ii: Cost of installation of i-th RES fjk: Cost of transmission energy from supply center j to demand point k. JOBmin: The minimum job creation. Ji: Job creation when implementing i-th RES. TCO2max: The maximum allowed tons of CO2 emissions. Ti: Tons of CO2 emissions for i-th RES. CAPi: Capacity of producing energy for i-th RES

Dkt : Energy demand of point k at period t. γ: Penalty cost of the unmet demands. η: Maximum coverage number of the demand points for each supply center.


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Variables: δij: 1 if i-the RES installed at j-th location; otherwise 0.

 jk : 1 if k-th demand point is connected to j-th location (supply center).

xijtk : The amount of the energy produced at j-th location in t-th time period by i-th RES for demand point k. ztk : Unmet demand of energy at k-th demand point in t-th time period.

Min  I i  ij   f jk  jk i

j

j

k

  c i x ijtk    z tk i



j

ij

t

k

k

t

 1 j

(1)

i

 J 

i ij

i

T 

i ij

i

 JOBmin

(2)

 TCO2max

(3)

j

j



jk

 1 k

(4)

j

 x i

 M  jk

t , k

(5)

 CAPi ij i, j, t

(6)

k ijt

j

x

k ijt

k

 x

k ijt

i



 ztk  Dkt k , t

j

(7)



(8)

 ij {0,1} i, j

(9)

 jk {0,1} j, k

(10)

xijtk  0 i, j, t , k

(11)

ztk  0 t , k

(12)

jk

k


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The objective function includes four parts. The first part is the sum of the installation costs of RES in potential locations as supply centers. The second part is the cost of connecting the demand points to the supply centers. The third part is the total cost of producing energies in supply centers for demand points in time horizon. Finally, the last part of the objective function is the sum of the penalty costs for the unmet demands in time horizon and in demand points. Constraint (1) states that in each potential location at most one RES can be installed. Constraints (2) & (3) satisfy job creation and CO2 emissions limitations, respectively. Constraint (4) ensures that each demand points covers at least with one of the supply centers. Constraint (5) guarantees when a supply center j can produce energy for demand point k (in the whole time horizon) if it is connected to supply center j. Constraint (6) is related to the capacity limitation of producing energy by the installed RES in supply center j and in each period. Constraint (7) is set to meet energy demands of demand points in each period. Constraint (8) limits the maximum number of covered demand points for each supply center. Constraints (9)-(12) are the feasibility conditions of the model. 2. Numerical example It is assumed there are four RES including. Also, there are five potential supply centers. It is desired to create at least 85 jobs. The time horizon is assumed to be 5 years, also. All the parameters in this section are generated with uniform distribution. Installation cost is U(300,450), the cost of energy transmission from supply centers to demand points is U(100,150), the cost of producing energy is U(10,15), job creations for RES is U(30,40), capacity of producing energy is U(1000,1500), tons of Co2 emissions is U(100E+02,200e+02) and demands in demand points in each period is U(2500,3500). The maximum allowed tons of CO2 emissions is 70e+03, also. Table 1 represents the optimal RES and locations for implementing them. According to Table 1, location #1, #3, and #4 are selected as supply centers with implementing second RES. Table 2 shows the linkages of these three locations to the demand points. The amount of the produced energy is summarized in Table 3. Table 1. Optimal RES and locations for implementing RES RES #1 #2 #3 #4

#1

Determined supply center locations #2 #3 #4







#5


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Table 2. Covering demand points with supply centers Supply centers #1 #3 #4

Demand points #1 

#2

#3 

#4

#5 



#6

#7

#8









#9 #10 #11 #12 #13 #14 #15 #16 #17 #18 #19 #20            

Table 3. Producing energy for each demand point Supply centers

#1

#1 2601 2883 2824 2692 2612

#2

#3 3096 3107 2862 3094 3170

#4

3006 2659 3156 3023 2624

#3

Demand points #5 #6 3486 2728 3175 3276 3432 2701 2797 2697 2746 3146

3096 3011 2545 3283 3445

#4

#7

#8

#9

#10

2587 3040 2626 3233 2613 3234 2585 2650 2934 2686

3192 3262 2654 2889 3195

3345 3112 3475 2526 2687

Table 3 (Continue). Producing energy for each demand point Supply centers

#1

#11 2988 3295 2992 3033 2510

#12 3043 2951 3475 2683 2663

#13 2524 2677 2561 2516 3335

#14

#3

#4

3101 2527 2696 3450 2835

Demand points #15 #16

#17

3094 2759 3140 2655 2960

3432 2848 2508 3448 3071 2893 3305 3040 2890 3057

#18

2833 3483 3266 2610 3494

#19 3080 2666 3143 2844 3412

#20 3400 2516 2868 3164 3093


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3. Conclusion Renewable energy sources such as solar, hydro, geothermal, and wind has well applicability, especially in rural areas and have advantages like less pollution. In this paper the problem is to meet the energy demand of an area with RES and in a time horizon. According to this problem we encounter with strategic and tactical decisions. Strategic decisions are to find the optimal location (supply center) and sort of RES to implement them. Also, we assume the area is divided into demand points which each of them should be covered by a supply center. So the tactical problem is to allocate the demand points to the supply centers and determine the amount of energies which should be produced for demand points by each supply center. For this purpose, a mixed integer programming is proposed and a numerical example is presented also and the results are analyzed.

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