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A design algorithm for an electric power system using wide-area interconnection of renewable energy
Journal Pre-proof A design algorithm for an electric power system using wide-area interconnection of renewable energy
Masaki Okada, Terumi Onishi, Shin’ya Obara PII:
S0360-5442(19)32333-3
DOI:
https://doi.org/10.1016/j.energy.2019.116638
Reference:
EGY 116638
To appear in:
Energy
Received Date:
28 May 2019
Accepted Date:
25 November 2019
Please cite this article as: Masaki Okada, Terumi Onishi, Shin’ya Obara, A design algorithm for an electric power system using wide-area interconnection of renewable energy, Energy (2019), https://doi.org/10.1016/j.energy.2019.116638
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Journal Pre-proof A design algorithm for an electric power system using wide-area interconnection of renewable energy
Masaki Okada Kitami Institute of Technology, Power Engineering Lab., Graduate School of Cold Regions, Environmental and Energy Engineering
Terumi Onishi Kitami Institute of Technology, Power Engineering Lab., Graduate School of Electrical and Electronic Engineering
Shin’ya Obara Kitami Institute of Technology, Power Engineering Lab., Applied Energy Course, Factory of Engineering, School of Earth, Energy and Environment Engineering Koen-cho 165, Kitami, Hokkaido 090-8507, Japan e-mail [email protected] phone/FAX +81-157-26-9262
Keywords: Renewable energy; Facility planning; Wide-area interconnection; Transmission line utilization factor; Genetic algorithm
Abstract1 This study aims to study the utilization factor of an electricity transmission network by determining the optimal installation of renewable energy technologies and heat pumps that store
1
Table of Abbreviations
COP
Coefficient of performance
GA
Genetic algorithm
Journal Pre-proof heat. The proposed model considers the electricity demand, heat load, and meteorological data with respect to each area of the transmission network and selects the type and capacity of renewable electricity sources in each area as well as the capacity of the required compensation electricity supply. The heat input and output of heat equipment and the amount of power supply of the transmission line were decided as each energy balance equilibrating. Therefore, an analysis method that uses genetic algorithm was introduced to achieve optimal operation planning. The proposed methodology is applied to the existing electric system in the island of Hokkaido, Japan, as a case study. Optimization of the arrangement and capacity of renewable electricity generation and transmission network increased the share of renewable energy from 11% to 33.8%. Furthermore, the transmission line utilization factor of the present transmission network improved from 14.5% to 41% when the installation location and capacity of renewable energy were optimized using the proposed methodology.
Keywords: Renewable energy; Facility planning; Wide-area interconnection; Transmission line utilization factor; Genetic algorithm
1. Introduction The amount of renewable energy that can be introduced into an area is limited by the capacity of the corresponding transmission network. Furthermore, a suitable electricity transmission network should be selected with respect to the operation budget when introducing renewable energy technologies The installation of photovoltaic systems and wind turbines over a wide area and interconnecting them using a functioning electrical power grid helps to reduce the power fluctuation of the transmission network, which can be attributed to the nature of the renewable sources. Therefore, establishing a suitable distribution of renewable energy technologies may reduce the overall cost of the energy storage facilities (e.g., heat pumps with a heat storage tank) and compensation electricity systems. However, this requires the determination of several decision variables, including the installation location, type, and capacity of each renewable energy technology
Journal Pre-proof and compensation electricity system. The installation planning of renewable energy, including the design of an isolated island microgrid [1], a multi period mixed-integer linear programming model for investment and design planning [2], and the installation of offshore renewable energy [3], has been previously investigated. With respect to an integrated electrical and thermal energy system, Qin et al. [4] examined the planning of an energy system based on electricity, heat, and gas, whereas Ahn et al. [5] reported the economic feasibility of the combined cooling, heating, and power systems. Further, Sandberg et al. [6] examined a grid tariff structure for obtaining flexible power-to-district heat. The optimal placement and sizing of heat pumps and heat-only boilers in coupled electricity and heating networks was studied by Ayele et al. [7], and the network-constrained economic dispatch of the integrated heat and electricity systems was studied by Huang et al. [8]. Furthermore, Schlachtberger et al. [9] and Pfeifer et al. [10] examined the cooperation of energy network with renewable energy, whereas Ayele et al. [11] investigated the capacity of the heating equipment of the electricity and heat networks and optimization of the installation location. However, the aforementioned researchers did not consider the transmission network during installation planning. Therefore, some studies, including a study of the transmission networks containing a high proportion of renewable energy [12] and of high-voltage direct-current transmission systems to support the integration of renewable energy [13], have been conducted. Generally, the cost performance is observed to be good when a high utilization factor is maintained with respect to the facilities. Although changing the transmission line utilization factor wildly is known, a very low value is observed depending on a time zone can be observed [14]. Therefore, this study aims to study the utilization factor of a transmission network by determining the optimal installation location of the renewable energy technologies and heat pumps used for heat storage that are distributed on the demand side. Further, an algorithm was developed based on the transmission network model for optimizing the placement and capacity of the electricity generation equipment. The adaption of district heating systems to produce a large amount of variable renewable electricity [15], power-to-heat production for peak shaving of renewable power [16], and cooperation system of wind power, heat pumps, and thermal energy storage [17] have been studied with respect to the
Journal Pre-proof operation of a heat pump system using renewable energy. Furthermore, Obara et al. [18] studied the electricity leveling of a wide-area energy network and the installation planning optimization of power supply in case of renewable energy. However, the improvement of the transmission network utilization factor by installation planning in case of renewable energy and the introduction of a heatstorage-type heat pump was not proved. Therefore, electricity and heat distribution networks that can achieve high utilization value
and
a high proportion of renewable energy are proposed in this study. Hence, the proposed renewable energy technologies have to be stabilized by controllable power sources (CPSs), including heatstorage-type heat pumps, oil-fired power plants, and hydroelectric power. A statement of economic efficiency was considered to be the objective function of the system for obtaining an optimal configuration and operating the proposed electric power system. Further, a genetic algorithm (GA), where the chromosomes simulate the terms of the overall system energy balance, was used to propose the optimal variety, capacity, and layout of the renewable energy technologies as well as the installation capacity of the CPSs used for compensation. A case study was conducted with respect to the facility planning of renewable energy technologies and compensation power supply for the Japanese island of Hokkaido by considering the existing transmission network and compensation power supply (thermal power station). Subsequently, the resulting economic efficiency, environmental performance, and transmission line utilization factor were compared with those of the previous electricity system in Hokkaido to analyze the performance of the proposed model.
2. System Outline This Section explains the details of a proposed system, electricity balance and heat balance, and economical efficiency calculation. 2.1. Distributed power supply network An example of distributed power supply via a transmission line is presented in Fig. 1; here, photovoltaic systems and wind turbines are installed in each area, and electricity is consumed on the demand side via a transmission line. When surplus electricity is produced using the renewable
Journal Pre-proof energy technology, electricity is exchanged among various areas via an interconnection device; however, the amount of electricity that can be exchanged is limited by the transmission line capacity. When the surplus electricity exceeds the total electricity and heat demand, the surplus electricity is stored in the form of heat in heat storage tanks using heat pumps installed on the demand side. The stored heat can be output at any arbitrary time.
Fig. 1 Distributed power network
2.2. Energy balance With respect to the electricity balance used in this study, denoted using Eq. (1), the left and right sides represent the power supply and electricity demand, respectively. Here, the electrical output of the photovoltaic system (𝑗) installed in area 𝑖 between sampling time 𝑡 and ∆𝑡 is 𝑃𝑝𝑣,𝑖,𝑗,𝑡. Similarly, 𝑃𝑤𝑝,𝑖,𝑘,𝑡 is the electricity generated from the wind turbine 𝑘 installed in area 𝑖, 𝑃𝑡𝑝,𝑖,𝑡 is the electricity obtained from other areas, 𝑃𝑐𝑝𝑠,𝑖,𝑡 is the electricity generated from the compensation power source installed in area 𝑖, and ∆𝑃𝑏𝑡,𝑐𝑔,𝑖,𝑡 is the electricity output of the storage battery. On the demand side, ∆𝑃𝑛𝑒𝑒𝑑,𝑖,𝑡 represents the electricity load of 𝑖, ∆𝑃ℎ𝑝,𝑖,𝑡 represents the electricity load of the heat pump, ∆𝑃𝑏𝑡,𝑐𝑔,𝑖,𝑡 represents the charge of the storage battery, and ∆𝑃𝑡𝑝,𝑖→ 𝑚,𝑡 represents the amount of electricity supplied to area 𝑚 from 𝑖 between sampling time 𝑡 and ∆𝑡. Furthermore, 𝑁𝑝𝑒𝑟𝑖𝑜𝑑 denotes the operation period, 𝑁𝑎𝑟𝑒𝑎 denotes the number of areas, and 𝑁𝑝𝑣 and 𝑁𝑤𝑝 represent the number of installed photovoltaic systems and wind turbines, respectively.
Journal Pre-proof
{ (
𝑁𝑝𝑒𝑟𝑖𝑜𝑑 𝑁𝑎𝑟𝑒𝑎 𝑁𝑝𝑣
∑ ∑ ∑𝑃
) }
𝑁𝑤𝑝
𝑝𝑣,𝑖,𝑗,𝑡
𝑡=0 𝑖=1 𝑗=1
+
∑𝑃
𝑤𝑝,𝑖,𝑘,𝑡
+ 𝑃𝑡𝑝,𝑖,𝑡 + 𝑃𝑐𝑝𝑠,𝑖,𝑡 + 𝑃𝑏𝑡,𝑑𝑐,𝑖,𝑡 ∙ ∆𝑡
𝑘=1
{ (
𝑁𝑝𝑒𝑟𝑖𝑜𝑑 𝑁𝑎𝑟𝑒𝑎
=
∑ ∑ ∆𝑃
𝑁𝑎𝑟𝑒𝑎
𝑛𝑒𝑒𝑑,𝑖,𝑡
+ ∆𝑃ℎ𝑝,𝑖,𝑡 + ∆𝑃𝑏𝑡,𝑐𝑔,𝑖,𝑡 +
𝑡=0 𝑖=1
∑ ∆𝑃
𝑚=1
(1)
) }
tp,i→m,t
∙ ∆𝑡
The thermal energy balance used in this study is shown in Eq. (2), where the left and right sides represent the supply and consumption, respectively. On the supply side, 𝐻ℎ𝑝,𝑖,𝑡 represents the thermal power supplied between 𝑡 and ∆𝑡 of a heat pump installed in area 𝑖 and 𝐻𝑠𝑡, 𝑜𝑢𝑡,𝑖,𝑡 represents the thermal power of a heat storage tank. On the consumption side, ∆𝐻𝑛𝑒𝑒𝑑,𝑖,𝑡 represents the heat demand between 𝑡 and ∆𝑡 in area 𝑖 and 𝐻𝑠𝑡,𝑖𝑛 ,𝑖,𝑡 represents the heat stored in the heat storage tank. Utilization of the exhaust heat of the compensation power supply was not considered in Eq. (2) because the existing hydraulic and thermal power stations were assumed to be compensation power supplies. In case of distributed power supply with the utilization of exhaust heat, the exhaust heat should be considered on the left side of Eq. (2). Additionally, 𝜂𝑠𝑡,𝑜𝑢𝑡,𝑖,𝑡 and 𝜂𝑠𝑡,𝑖𝑛,𝑖,𝑡 represent the efficiencies of the heat supply and heat storage in the heat storage tank, respectively. The heat storage quantity 𝐻𝑠𝑡 ,𝑖,𝑡 is calculated using Eq. (3) based on the heat loss 𝐻𝑠𝑡,𝑙𝑜𝑠𝑠,𝑖,𝑡 in each sampling time.
𝑁𝑝𝑒𝑟𝑖𝑜𝑑 𝑁𝑎𝑟𝑒𝑎
{
∑ ∑ (𝐻
𝑁𝑝𝑒𝑟𝑖𝑜𝑑 𝑁𝑎𝑟𝑒𝑎
ℎ𝑝,𝑖,𝑡
} ∑ {∑
+ 𝐻𝑠𝑡,𝑜𝑢𝑡,𝑖,𝑡 ∙ 𝜂𝑠𝑡,𝑜𝑢𝑡,𝑖,𝑡) ∙ ∆𝑡 =
𝑡=0 𝑖=1
𝑡=0 𝑖=1
𝐻𝑠𝑡,𝑖,𝑡 = 𝐻𝑠𝑡,𝑖,𝑡 ― 1 ― 𝐻𝑠𝑡,𝑙𝑜𝑠𝑠,𝑖,𝑡
2.3. Economic efficiency
}
(∆𝐻𝑛𝑒𝑒𝑑,𝑖,𝑡 + ∆𝐻𝑠𝑡,𝑖𝑛,𝑖,𝑡 ∙ 𝜂𝑠𝑡,𝑖𝑛,𝑖,𝑡) ∙ ∆𝑡
(2)
(3)
Journal Pre-proof Several costs, including maintenance costs, safety expenses, and personnel expenses, are incurred during the installation and operation of renewable and nonrenewable energy stations. The total cost 𝐶𝑔𝑒𝑛 of various electricity generation sources is calculated using Eq. (4) by multiplying the electricity
generation unit price with the photovoltaic, wind, and compensation power source electricity generation (i.e., 𝑢𝑝𝑣 𝑃𝑝𝑣,𝑖,𝑡, 𝑢𝑤𝑝 𝑃𝑤𝑝,𝑖,𝑡, and 𝑢𝑐𝑝𝑠 𝑃𝑐𝑝𝑠,𝑖,𝑡, respectively). 𝑁𝑎𝑟𝑒𝑎 represents the number of areas that have introduced the electric power system, whereas 𝑁𝑝𝑒𝑟𝑖𝑜𝑑 denotes the operation period.
𝑁𝑝𝑒𝑟𝑖𝑜𝑑𝑁𝑎𝑟𝑒𝑎
𝐶𝑔𝑒𝑛,𝑝𝑒𝑟𝑖𝑜𝑑 =
∑ ∑ (𝑢
𝑝𝑣
∙ 𝑃𝑝𝑣,𝑖,𝑡 + 𝑢𝑤𝑝 ∙ 𝑃𝑤𝑝,𝑖,𝑡 + 𝑢𝑐𝑝𝑠 ∙ 𝑃𝑐𝑝𝑠,𝑖,𝑡) ∙ ∆𝑡
(4)
𝑡=0 𝑖=0
Consignment costs are incurred and should be considered when transporting electricity to some areas. The electricity consignment cost 𝐶𝑡𝑝 of the entire transmission network can be obtained using Eq. (5) based on the consignment electric unit price 𝑢𝑡𝑝 that was decided by an Japanese electric supply company. In Eq. (5), ∆𝑃𝑡𝑝,𝑖→ 𝑚,𝑡 represents the electricity supplied to area 𝑚 from 𝑖 via a transmission line from time 𝑡 to ∆𝑡. If 𝑖 = 𝑚, then the 𝐶𝑡𝑝 of the area is zero. Subsequently, the total cost of the system can be calculated using Eq. (6).
𝑁𝑝𝑒𝑟𝑖𝑜𝑑𝑁𝑎𝑟𝑒𝑎 𝑁𝑎𝑟𝑒𝑎
𝐶𝑡𝑝,𝑝𝑒𝑟𝑖𝑜𝑑 =
(
∑ ∑ ∑𝑢
)
𝑡𝑝∆𝑃tp,i→m,t∆𝑡
𝑡=0 𝑖=1 𝑚=1
𝐹𝑠𝑦𝑠𝑡𝑒𝑚 = 𝐶𝑔𝑒𝑛,𝑝𝑒𝑟𝑖𝑜𝑑 + 𝐶𝑡𝑝,𝑝𝑒𝑟𝑖𝑜𝑑
(5)
(6)
3. System Operation and Introductory Equipment This Section explains the operating method of electricity and heat equipment, and the details of rated capacity. 3.1. Electricity exchange The electricity exchanged via the transmission line between areas 𝑙 and 𝑖 during the sampling time 𝑡, represented using Eq. (7), is calculated as the difference between the supplied electricity ∗ 𝑃𝑡𝑝,𝑙→ 𝑖,𝑡 and the transmission loss 𝑃𝑡𝑝,𝑙𝑜𝑠𝑠,𝑙→ 𝑖,𝑡 in area 𝑙.
Journal Pre-proof
∗ 𝑃tp,l→i,t = 𝑃tp,l→i,t ― 𝑃𝑡𝑝,loss,l→i,t
(7)
The loss accompanying the electrical transmission line of a three-phase three-wire system is represented using Eq. (8), where the line current 𝐼𝑡𝑝,𝑡 can be obtained by dividing the exchange power with the nominal voltage of the power transmission line and 𝑅𝑡𝑝 and 𝐿𝑡𝑝 denote the resistance and length of the transmission line, respectively. Furthermore, 𝐼𝑡𝑝,𝑡 is obtained from Eq. (9), 𝑉𝑟𝑒 is the receiving-end voltage of the load in area 𝑖, and cos ∅ is the power factor.
𝑃tp,loss,l→i,t = 3𝐼𝑡𝑝,𝑡2𝑅𝑡𝑝𝐿𝑡𝑝 𝐼𝑡𝑝,𝑡 =
∆𝑃𝑛𝑒𝑒𝑑,𝑖,𝑡 3 ∙ 𝑉𝑟𝑒cos ∅
(8) (9)
3.2. Electrical equipment The electrical output of the photovoltaic systems and wind turbines is obtained based on the solar radiation and wind speed in each introductory area. Further, the conversion efficiency of the used photovoltaic systems and the power curve for wind power generation are obtained for each analysis example. The electricity supplied via a transmission network was adjusted using thermal and hydraulic power stations. However, the output adjustment speed of the compensation power supply was not considered even though the energy balance of the transmission network was estimated at each sampling time. Therefore, strict stability of the electricity supply via the transmission network was not addressed. Generally, the maximum transmission line utilization factor is decided by the electric company by considering the stability and reliability of electricity supply. However, because the utilization factor design method used by the Japanese power company is unclear, the utilization factor 𝑢𝑡𝑙 ,𝑖,𝑡 of the power transmission line 𝑖 in sampling time 𝑡 is defined in Eq. (10) as the ratio of the previous value
Journal Pre-proof of power transmission (𝑃𝑡𝑙,𝑖,𝑡) to the maximum operation power (𝑃𝑚𝑎𝑥,𝑡𝑙,𝑖). The average of the integrated value of 𝑢𝑡𝑙 ,𝑖,𝑡, as shown in Eq. (11), represents the annual average utilization factor 𝑈𝑡𝑙,𝑖 in transmission line 𝑖 when 𝑡 = 1 h.
𝑢𝑡𝑙,𝑖,𝑡 =
𝑃𝑡𝑙,𝑖,𝑡
(10)
𝑃𝑚𝑎𝑥,𝑡𝑙,𝑖 𝑌𝑒𝑎𝑟
𝑈𝑡𝑙,𝑖 =
∑𝑡 = 1𝑢𝑡𝑙,𝑖,𝑡
(11)
365 ∙ 24
The charge and discharge efficiencies, cycle life, and maintenance cost must be considered when planning to use batteries for electrical energy storage. Here, these factors were considered for obtaining the electricity balance in Eq. (1), and the charge and discharge efficiencies are passed to 𝑃𝑏𝑡,𝑑𝑐,𝑖,𝑡 and ∆𝑃𝑏𝑡,𝑐𝑔,𝑖,𝑡, respectively. The specifications of the storage batteries are presented in Table 1 [19, 20]. In the analysis of this study, a lithium battery is used based on its performance in terms of the cost, cycle life, charge time, and charging efficiency.
Table 1 Performance comparison of various storage batteries [19, 20] Type Parameter Cost/kWh Cycle life (80% DoD) Energy density (Wh/Kg) Depth of discharge (approximate) Fast charge time Charging efficiency Maintenance required Hot climate Best application
3.3. Heat equipment
Lithium
Lead acid
Nickel
140 USD/kWh (2017)
120 USD/kWh
240 USD/kWh
500–1000 cycles
200–300 cycles
500–1000 cycles
120–160
30–50
60–120
20% of 2000 cycles
20% of 500 cycles
20% of 2500 cycles
2–4 h
8–16 h
2–4 h
99% at 4-hour rate
80% at 4-hour rate
95% at 4-hour rate
Moderate
High
Low
Great sustainability
Severe effect
Moderate effect
Portable devices
Renewable energy storage
Emergency lighting
Journal Pre-proof The power consumption ∆𝑃ℎ𝑝,𝑖,𝑡 that is required to obtain the thermal power 𝐻ℎ𝑝,𝑖,𝑡 of heat pump 𝑖 in sampling time 𝑡 and calculate the heat pump’s coefficient of performance 𝐶𝑂𝑃𝑖,𝑡 is obtained using Eq. (12).
(12)
∆𝑃ℎ𝑝,𝑖,𝑡 = 𝐻ℎ𝑝,𝑖,𝑡/𝐶𝑂𝑃𝑖,𝑡
The heat storage tank operated under the following conditions: (1) When the heat storage quantity is greater than the heat demand When the heat stored in the heat storage tank is greater than the heat demand, the operating method is determined using Eq. (13).
𝑁𝑝𝑒𝑟𝑖𝑜𝑑 𝑁𝑎𝑟𝑒𝑎
{
∑ ∑𝐻
𝑁𝑝𝑒𝑟𝑖𝑜𝑑 𝑁𝑎𝑟𝑒𝑎
} ∑ {∑
𝑠𝑡,𝑜𝑢𝑡,𝑖,𝑡𝜂𝑠𝑡,𝑜𝑢𝑡,𝑖,𝑡∆𝑡
𝑡=0 𝑖=1
=
}
(∆𝐻𝑛𝑒𝑒𝑑,𝑖,𝑡 + ∆𝐻𝑠𝑡,𝑖𝑛,𝑖,𝑡𝜂𝑠𝑡,𝑖𝑛,𝑖,𝑡) ∙ ∆𝑡
𝑡=0 𝑖=1
(13)
(2) When the heat storage quantity is lesser than the heat demand When the heat stored in the heat storage tank installed in an area is less than the demand, the operating method is determined based on Eq. (14), and the heat pump is observed to provide insufficient thermal storage.
𝑁𝑝𝑒𝑟𝑖𝑜𝑑 𝑁𝑎𝑟𝑒𝑎
{
∑ ∑ (𝐻
𝑁𝑝𝑒𝑟𝑖𝑜𝑑 𝑁𝑎𝑟𝑒𝑎
ℎ𝑝,𝑖,𝑡
} ∑ (∑
+ 𝐻𝑠𝑡,𝑜𝑢𝑡,𝑖,𝑡 ∙ 𝜂𝑠𝑡,𝑜𝑢𝑡,𝑖,𝑡) ∙ ∆𝑡 =
𝑡=0 𝑖=1
𝑡=0
𝑖=1
)
∆𝐻𝑛𝑒𝑒𝑑,𝑖,𝑡∆𝑡
(14)
4. Analysis Method This Section explains the details of optimization method of the proposed system by GA. 4.1. Search for an optimal operating method An optimal operating method that satisfies the objective function can be obtained based on the electricity equation (Eq. (1)) and the heat equation (Eq. (2)). The given meteorological data were used
Journal Pre-proof to estimate the electricity generation of the photovoltaic systems and wind turbines, whereas the electricity and heating demands of each area were estimated based on the number of households in the area. Subsequently, the installation capacity of the photovoltaic systems and wind turbines, the capacity of the compensation power supply, the amount of charging or discharging operations observed in case of a storage battery, and the amount of thermal power stored by the heat pump were determined as decision variables. The heat input and output of a heat storage tank and the amount of power supply of the transmission line were decided as each energy balance equilibrating. An analysis method using a GA was introduced to search for the optimal solution.
4.2. Chromosome model This study presents the installation capacity of the photovoltaic systems and wind power generation systems, the output of the power supply for compensation, the amount of charging and discharging operations in case of a storage battery, and the amount of thermal power stored by the heat pump using a GA chromosome model. An example chromosome that denotes the amount of produced photovoltaic (PV) and wind turbine (WP) electricity is shown in Fig. 2(a). Here, the energy balances of Eqs. (2) and (3) in area 𝑖 correspond to A to N in the figure and are a set of 18-bit binary numbers. When surplus of renewable electricity can be observed in two or more areas, the supply area for the surplus electricity should be determined based on the economic efficiency. Therefore, a supply ranking of surplus electricity is determined using the chromosome model. A chromosome model that denotes the supply ranking of surplus electricity for areas 0–13 is randomly presented in Fig. 2(b). Figure 2(d) shows a chromosome model group when setting a chromosome model organized by Figs. 2(a)–(c) with 1000 as the generation number and 1000 individuals.
Journal Pre-proof
Fig. 2 Chromosome model
4.3. Objective function The objective function of the system is the maximization of economic efficiency (minimization of cost) and is calculated using Eq. (15), where 𝐹𝑠𝑦𝑠𝑡𝑒𝑚 is defined using Eq. (6). The arrangement of distributed power supply, capacity of the compensation power supply, and operating method of each
Journal Pre-proof equipment are obtained based on the solution that satisfies the objective function most among the obtained chromosome model groups based on a random search using GA.
𝐹𝑠𝑦𝑠𝑡𝑒𝑚→minimize
(15)
Figure 3 shows the analysis flow of the optimization algorithm using GA. The analysis method of a wide-area interconnection energy network using GA has been previously presented [18].
Fig. 3 Analysis flow
5. Case Analysis
Journal Pre-proof The proposed algorithm was applied to the existing electric grid and district heating network of the island of Hokkaido, Japan, which is a cold snowy area in northern Japan with an area of 83,420 km2, a population of 5,350,000, a highest average temperature of 22.3 ℃ in August, and a minimum average temperature of −3.6 ℃ for a representative day in January.
5.1. Electricity system The existing transmission network of Hokkaido is presented in Fig. 4 and can be divided into seven areas, i.e., A–G [21]. In 2017, the electric supply scheme comprised coal-fired thermal (49%), oil-fired thermal (25%), renewable energy (11%), and hydroelectric (7%) generation facilities. The total electricity generation capacity was approximately 8.0 GW, and the maximum consumption of 5.34 GWh was observed in December 2016. These seven areas are classified according to climate condition.
Area F
Area D Area E Area A
Area G Area C
Area B 0
100km
Transformer substation Switching station 275,000 V 187,000 V 100,000-200,000 V Transmission line of the other company
Fig. 4 Transmission network of Hokkaido
Journal Pre-proof Subsequently, a transmission network was assumed over 14 cities (A–N) based on this information, as shown in Fig. 5. In cities D, E, H, and I, thermal power compensation supply was provided. Largescale photovoltaic systems and wind turbines were interconnected among cities A, C, E, H, I, K, and N. Only the electricity generated from wind turbines was interconnected among cities B, D, F, G, J, L, and M. Based on the climate and the existing electricity system, photovoltaic systems and wind turbines were added to the model in seven and 14 cities, respectively. There is no densely populated area equivalent to city J; hence, the electricity and heat demands in city J were assumed to be zero. The location of compensation power supply is set up similar to the position of the thermal power station of the present Hokkaido Electric Power Co. Inc. The setting capacity of each compensation power supply is optimized using GA. Furthermore, a storage battery is not installed in this example.
Area F A
C
B
Area D N Area A
D E
M
Area E
F
K I
Area C J
G
L Area G 110kV Transmission line 187kV Transmission line 275kV Transmission line City with power supply for compensation City with solar farms and wind farms
H
Area B
City with wind farms
Fig. 5 Setting the transmission network and electrical machinery and apparatus
Transmission line between cities
Journal Pre-proof L to M K to L J to K I to K I to J F to I E to I E to F C to E C to D 0
10
20
30 Utilization factor [%]
40
50
60
Fig. 6 Average utilization factor of the transmission line
5.2. Transmission line utilization factor The capacity of a transmission line corresponds to the electricity load expected based on the population of each area. Therefore, each transmission line utilization factor was calculated using Eq. (10) and Eq. (11) (see Section 3.2.3) based on the electric usage records in 2017 [22]; the resulting utilization factor of each intercity transmission line using this dataset is shown in Fig. 6. Here, the average utilization factor was less than 55%, and all the transmission lines were well below their capacity, as shown in Fig. 7 [23]. With respect to the average transmission line utilization factor on the range of annual electricity demand, approximately 68% availability could be observed in June (i.e., the average transmission line utilization factor in June is 32% in Fig. 7).
60
300
50
250
40
200 30
150
20
100
10
50 0 January March May February April
Average utilization factor [%]
Monthly electricity load [GW]
350
0 July September November June August October December Month
Fig. 7 Monthly electricity load and average utilization factor of the transmission network of Hokkaido
Journal Pre-proof
5.3. Energy demand characteristics The electricity and heat demands, predicted based on Hokkaido Electric Power Co. Inc.’s 2017 demand records and the heating demand characteristics of individual houses [24], are presented in Figs. 8 and 9, respectively. Because 70%–80% of the individual houses in the studied region used kerosene for heating and hot water supply, large load peaks could be observed in the morning and evening, and the time shift of heat supply by heat storage is effective. Therefore, the proposed system considers the introduction of a heat-storage-type electric heat pump in each house.
Electric power demand [GW]
4.0
January December
3.0
February
March
November
July
April Octobar 2.0
September
0
3
6
9
May
12 Time [Hour]
15
June
August
18
21
24
Fig. 8 Electricity demand of Hokkaido, 2017
30
Heat demand [GW]
25
March
20
November
15
October
10 September 5 0
0
3
February January December
April May June July, August 6 9
12 Time [Hour]
15
18
Fig. 9 Heat demand of Hokkaido, 2017
21
24
0.30
Electric power [kW/m2]
Electric power [kW/m2]
Journal Pre-proof 0.25 0.20 0.15 0.10 0.00 2
3
4
5
6
7
8
9
10
11
0.00 1
2
3
4
5
6
7
(c) Obihiro
0.15 0.10 0.00 4
0.10
Month
0.20
3
0.15
Month
0.25
2
0.20
(a) Wakkanai 0.30
1
0.25
12
Electric power [kW/m2]
Electric power [kW/m2]
1
0.30
5
6
7
8
9
10
11
12
8
9
10
11
12
8
9
10
11
12
0.30 0.25 0.20 0.15 0.10 0.00 1
2
3
4
5
6
7
Month
Month
(b) Asahikawa
(d) Muroran
Fig. 10 Electric power output of photovoltaics in each city
Soya cape Wakkanai
Rumoi
Asahikawa Higashikagura
Obihiro
Muroran Erimo cape
Fig. 11 Each city in Hokkaido
5.4. Renewable energy sources Figure 10 shows an output example of the photovoltaic systems in each city in Hokkaido obtained based on the amount of solar radiation [25]. The conversion efficiency of the photovoltaic systems is
Journal Pre-proof 18.5%, and each city is denoted in Fig. 11. With respect to the output characteristics of the photovoltaic systems, a difference is observed in each area in Fig. 10; when the photovoltaic systems in several different areas are interconnected, smoothing of the power fluctuations is expected during the cyclic period.
50
Wind speed [m/s]
Wind speed [m/s]
50 40 30 20 10
40 30 20 10 0
0 1
2
3
4
5
6 7 Month
8
9
10
11
12
1
2
3
4
(a) Soya cape Wind speed [m/s]
Wind speed [m/s]
40 30 20 10 0 2
3
4
5
6 7 Month
6 7 Month
8
9
10
11
12
9
10
11
12
(c) Higashikagura
50
1
5
8
9
10
11
50 40 30 20 10 0
12
(b) Rumoi
1
2
3
4
5
6 7 Month
8
(d) Erimo cape
Fig. 12 Measuring example of average annual wind speed at each location
The wind speed data [26] obtained from various places in Hokkaido were used to calculate the available electricity generation from wind turbines via Eq. (16); the results are shown in Fig. 12. Here, 𝑣𝑤𝑝 represents the wind speed at the hub height of the turbine 𝑧ℎ𝑢𝑏 and 𝑣𝑔𝑑 represents the wind velocity at ground height 𝑧𝑔𝑑. As documented by the New Energy and Industrial Technology Development Organization (NEDO) of Japan, n = 5 and n = 7 for the coastal and inland cities, respectively [27].
𝑣𝑤𝑝 = 𝑣𝑔𝑑(𝑧ℎ𝑢𝑏 𝑧𝑔𝑑)
1𝑛
(16)
Journal Pre-proof The power curve of the introduced wind turbine is shown in Fig. 13. The rotor diameter, sweep area, and hub height are 61.4 m, 2960 m2, and 68 m, respectively. The rated output of one turbine is 1 MW [28]. Upon analysis, the wind speed in each area is given in Fig. 13, and electricity is produced.
Electric power [MW]
1.2 1.0
Rated wind speed
0.8 0.6 0.4
Cut-in wind speed
0.2 0.0
0
5
Cut-out wind speed
10
15
20
25
30
Wind speed 𝑣𝑤𝑝 [m/s]
Fig. 13
Power curve of wind power generator
5.5. Heat pump The coefficient of performance (COP) of the introduced heat pump, a Queuton ESA301 series of Mitsubishi Heavy Industries, Ltd., is shown in Fig. 14 [29].
5.0 4.0
COP
3.0 2.0 1.0 0.0 -30
-20
-10
0
10
20
30
Temperature [℃ ]
Fig. 14 COP of heat pump [17] 5.6. Setup of cost A summary of the equipment and consignment costs of electricity production as released by the Ministry of Economy, Trade and Industry of Japan [30] and the Hokkaido Electric Power Co. Inc. [31]
Journal Pre-proof is presented in Table 2. Here, the capital costs (including the construction, fixed property tax, and decommissioning costs) and maintenance costs (including the personnel, repairing, and operating expenses) are included in the cost of photovoltaic systems and wind turbines.
Table 2 Cost of power generation, compensation power source, and consignment of electricity
Generator Photovoltaic Wind power generation
Power generation cost (USD/MWh) 218 194.6
Compensation power source 219 (coal- and oil-fired power ) Consignment cost of electricity
36.9
A heat pump accompanied by a heat storage tank can be installed in each area. The cost of the heat equipment facilities is obtained based on Ecoqute in 2017 [32]. The cost of the heat pump per MW for ensuring operation for 20 years is 32,880 USD. Furthermore, the heat storage tanks are 2080 USD per MWh. Equation (17) denotes the cost 𝐶ℎ𝑒𝑎𝑡 of the heat equipment facilities in Eq. (17), 𝑢ℎ𝑝 is the unit price of the heat pump facilities, and 𝑉ℎ𝑝 is the installed capacity. Moreover, 𝑢ℎ𝑠𝑡 is the unit price of the heat storage tank facilities, and 𝑉ℎ𝑠𝑡 is the installed capacity. The heat storage and heat output efficiencies of the heat storage tank were set to 90%.
𝐶ℎ𝑒𝑎𝑡 = (𝑢ℎ𝑝 ∙ 𝑉ℎ𝑝) + (𝑢ℎ𝑠𝑡 ∙ 𝑉ℎ𝑠𝑡)
(17)
The transfer of electricity between areas was assigned a cost according to the consignment fees obtained from the Hokkaido Electric Power Co. Inc. (36.94 USD/MW) [33].
Journal Pre-proof 5.7. GA parameters The GA parameters used in this case study are summarized in Table 3. The sampling time of analysis was set to 1 h. The values in Table 2 were decided by trial and error from the range of combination with good convergence characteristics. The chromosome model in Fig. 2 is applied to the analysis example. Photovoltaic systems are installed in seven areas, whereas wind power generation systems are installed in 14 areas.
Table 3 GA parameters Generation number
1000
Chromosomes number
1000
Sampling time
1h
Probably of cross-over
95%
Probably of mutation
0.2% Operation of selection
Selection rate of first rank Selection rate of second to 5th
15% in all the individuals 5% in all the individuals
6. Case Study Results This Section describes the analysis result of the introductory example to Hokkaido with the proposed system. 6.1. Capacity limitation of the transmission network and shortage of electricity supply The electricity system assumed in Fig. 5 was obtained based on a conventional power grid. A capacity shortage could be observed in part of the existing transmission line, as shown in Fig. 15, because the heat demand was added to the conventional electricity demand. Therefore, electricity shortage occurred in the northern (cities A, B, and C) and eastern areas (cities L, M, and N).
City
Journal Pre-proof N M L K J I H G F E D C B A 0
50
100
150
200
250
300
350
400
Shortage of electricity [GWh]
Fig. 15 Analysis result of insufficient electricity in 2017
14 Total electricity load
Electricity [GW]
12 10 8 6 4
Total supply (CPS and renewable energy)
2 0
Renewable energy 0
3
6
9
12
15
18
21
24
Time [Hour]
Fig. 16 Total electricity demand on January 18, 2017, and power supply by each power source
The greatest daily electricity shortage, corresponding to the data obtained from January 18, 2017, is presented in Fig. 16, where the total supply includes renewably sourced and compensated electricity. This day also corresponded to the lowest total renewable energy output in all the areas, requiring increased production from the compensation power supplies. However, the transmission network exhibits capacity shortage, and power failures occur in the northern and eastern areas of Hokkaido.
City
Journal Pre-proof N M L K J I H G F E D C B A 0.0
Solar farm Wind farm
0.2
0.4
0.6
0.8
1.0
1.2
1.4
Capacity [GW]
Fig. 17 Analysis result of introduction capacity of renewable energy
6.2. Dissolution of power failure with an increase in the capacity of the transmission network An optimal capacity of transmission lines was selected to compensate for the electricity shortage during winter. The resulting amount of introduced renewable electricity production for each city is shown in Fig. 17. The total installed capacities of the photovoltaic systems and wind turbines were 0.6 and 3.7 GW, respectively. Because the wind power generation had few daily ranges of output, the amount of introduction to the network increased. Figure 18 shows the results of the power demand amount for each city and the production of electricity using renewable energy. More than 60% of the electricity demand in cities A, B, and M could be met via the usage of renewable sources, whereas less than 30% of the electricity demand could be met in cities D, E, H, and I, where the compensation power supplies were installed. In a city that assumes installation of the compensation power supply, operation planning was conducted to ensure that the introduction amount of renewable energy may be reduced.
City
Journal Pre-proof N M L K J I H G F E D C B A 0
Electricity demand Electricity generation by renewable energy
2
4
6
8
10
12
14
16
18
Electricity [ × 103 GWh]
Fig. 18 Analysis result of electricity demand of each city, and sum total of production of electricity by renewable energy
6.3. Renewable vs nonrenewable electricity use After optimization, the renewable sources accounted for 33.8% of the electricity generation in Hokkaido. However, after the introduction of the heat storage-type heat pump and modification of the transmission network capacity, this proportion became 22.5%, which represents twice the amount of renewably sourced electricity generated in 2017, except for hydroelectric power generation. Subsequently, the renewable energy ratio of electricity is controlled in the areas in which the compensation power supply is controlled to restrict the consignment cost of renewable electricity. Thus, the power ratio of renewable energy to the whole system can be obtained. Furthermore, the analysis result of the total capacity of the compensation power supply was 15.8 GW; this value was 127% the maximum power loads of the proposed system (12.4 GW) because the loss observed in a transmission network was added to the capacity of the compensation power supply.
6.4. Installed capacity of heat equipment The resulting installed heat pump capacity in each city is shown in Fig. 19; the total installed capacity of the entire system was 8.6 GW. The installed capacity of heat storage tanks is shown in Fig. 20. The capacity of the heat pump in each city becomes less than the maximum heat load because
Journal Pre-proof the heat storage tanks are only installed in cities A, G, K, L, and N. The fluctuation in electricity generation that can be attributed to renewable sources was controlled by the consignment of
City
electricity from connected cities and by distributed heat-storage-type heat pumps.
N M L K J I H G F E D C B A 0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
Capacity [GW]
City
Fig. 19 Analysis result of introduction capacity of a heat pump
N M L K J I H G F E D C B A 0
0.5
1.0
1.5
2.0
Capacity [GWh]
Fig. 20 Analysis result of introduction capacity of a heat storage tank
The resulting total heat storage quantity of the heat storage tanks in each area is shown in Fig. 21, where the horizontal axis shows the day in each month that requires the greatest heat storage. Although all the surplus electricity is converted into heat and the capacity of the heat storage tank, the capacity of the heat storage tank will drastically decrease when the heat exceeding demand can
Journal Pre-proof be discarded. By introducing heat pumps to store the excess generated renewable electricity, the carbon dioxide emissions of 1,720,000 households, representing approximately 70% of the 2,440,000 households, were decreased [34, 35].
3.5
Capacity [GWh]
3.0 2.5 2.0 1.5 1.0 0.5 0.0
One day
January March May July September November February April June August October December Representative day of each month
Fig. 21 Analysis results of total capacity of a heat storage tank for a representative day with maximum heat storage in each month
6.5. Average transmission line utilization factor The resulting average annual and monthly transmission line utilization factors are shown in Figs. 22 and 23, respectively. Three cases (Cases A, B, and C) were assumed while performing this analysis. The electricity load of Fig. 8 was applied to the present transmission network (Case A); further, the distribution capacity of the transmission network was modified (the heat load of Fig. 9 was not applied, Case B), and the distribution capacity of the transmission network was modified (the heat load of Fig. 9 was applied, Case C). The average transmission line utilization factor was 41% when electricity was supplied using the present transmission network. However, the transmission line utilization factor in wide-area interconnection considerably decreases to 13.5% when the arrangement and capacity of renewable energy was optimized. When the renewable energy arrangement and capacity are optimized, the local supply and local consumption of electricity in the area are expected to considerably increase. Because renewable electricity is consumed in the area in which it is produced, the transmission line utilization
Journal Pre-proof factor considerably decreases, indicating that the capacity of the transmission line required for interconnection between areas can be decreased. The average transmission line utilization factor in Case C was 5.1%, indicating that the local supply and consumption of electricity were greater than those in Case B due to the introduction of a heat storage tank. Although the capacity of the transmission network was greater in Cases B and C than that in Case A, the introduction of a heatstorage-type heat pump distribution reduced the capacity of the transmission lines.
Utilization factor [%]
40
41 %
30
Case A: Electricity load (Fig. 8) is applied to present transmission network Case B: Distribution capacity of transmission network is modified (heat load of Fig. 9 is not applied) Case C: Distribution capacity of transmission network is modified (heat load of Fig. 9 is applied)
20 13.5 % 10 0
5.1 % Case A
Case B
Case C
Fig. 22 Analysis results of annual average of the utilization factor of a transmission network of the Hokkaido model
60
Utilization factor [%]
50 Case A 40 30 Case B
20 10
Case C
0 January March May July September November February April June August October December Representative day of each month
Fig. 23 Analysis results of monthly average of the utilization factor of a transmission network of the Hokkaido model
6.6. Cost analysis
Journal Pre-proof The resulting overall costs of the photovoltaic systems, wind turbines, compensation power supply, electricity consignment, and heating equipment are displayed in Fig. 24. The capital and operation costs were included in the costs of the photovoltaic systems and wind turbines, and the capital, operation, fuel, and carbon tax costs were included in the electricity costs of the compensation power supply. The total cost of the photovoltaic systems, wind turbines, and compensation supply accounted for approximately 90% of the total costs in Cases B and C. Compared with the photovoltaic systems, the introduction of wind power generation was planned during day and night. However, because the time zone of weather conditions appeared when the output of renewable energy of the Hokkaido whole area is hardly obtained, the capacity planning of compensation power supply increase.. Because Case C considered that the electricity load will satisfy the heating demands, the exchange of electricity increased, resulting in an increase in the electricity consignment cost. The costs of the heat storage tank and heat pump are almost similar to the cost of the electricity consignment; however, the cost obtained based on this analysis example with respect to the power supply was considerably large.
× 109
9 8
Cost [USD]
7 6
Case C
5 4
Case B
3 2 1 0
Total Solar farm Wind farm CPS
Delivery
Heat Heat pump storage tank
Fig. 24 Analysis results of cost
7. Conclusions To validate the model, a case study was performed on the existing electricity system in the island of Hokkaido, Japan. The following conclusions were obtained from this case study:
Journal Pre-proof
When renewable energy was distributed in large quantities, any transmission line denoting electric power shortage provided with electric power from an existing transmission network (the northern area and the eastern area of Hokkaido). Further, the cause of insufficient transmission line capacity can be verified using the proposed algorithm and by modifying some transmission networks; the proportion of renewable energy in the whole electric power system was observed to drastically increase.
Optimization of the arrangement and capacity of renewable electricity generation as well as the capacity of the transmission network increased the share of renewable energy, excluding hydroelectric power, from 11% to 33.8%.
Optimization of the electricity system allowed the reduction of carbon dioxide emissions from 70% of the households (i.e., 1,720,000 of 2,440,000 households in Hokkaido).
When the installation location and capacity of renewable energy were optimized using the proposed method, the transmission line utilization factor obtained using the present transmission network was 41% (the actual present transmission line utilization factor is approximately 14.5%). However, the transmission line utilization factor decreased to 13.5% when the installation location and capacity were optimized by assuming wide-area interconnection of renewable energy because the local supply and consumption of electricity were expected to increase; furthermore, the distribution of electricity to other areas will drastically decrease.
Nomenclature 𝐶 𝐶𝑂𝑃 cos ∅ 𝐹𝑠𝑦𝑠𝑡𝑒𝑚 𝐻
Cost [USD] Coefficient of performance Power-factor Objective function [USD] Heat [W]
Journal Pre-proof ∆𝐻
Heat load [W]
𝐼
Current [A]
𝐿
Length of transmission line [km]
𝑁
Number
𝑁𝑎𝑟𝑒𝑎 𝑁𝑝𝑒𝑟𝑖𝑜𝑑 𝑃
Number of area Period of operation [Hour] Electric power [W]
𝑃∗
Amount of electric power interchange [W]
∆𝑃
Electric power load [W]
𝑅
Resistant [Ω]
∆𝑡
Interval time [Hour]
𝑢
Unit price [USD/W]
𝑢𝑡𝑙,𝑖
Utilization factor of transmission line
𝑈𝑡𝑙,𝑖
Average annual utilization factor of transmission line
𝑉 𝑉𝑟𝑒
Capacity of equipment [W] Voltage of receiving end [V]
𝑣
Wind velocity [m/s]
𝑧
Height [m]
Greek characters 𝜂
Efficiency
𝑏𝑡
Battery
𝑐𝑔
Battery charge
Subscript
𝑐𝑝𝑠
Compensation power supply (Controllable Power Source)
𝑑𝑐
Battery discharge
𝑔𝑑
Ground
Journal Pre-proof 𝑔𝑒𝑛
Generation
ℎ𝑝
Heat pump
ℎ𝑢𝑏
Hub of wind turbine
𝑙𝑜𝑠𝑠
Energy loss
𝑛𝑒𝑒𝑑 𝑣 𝑃𝑣 𝑠𝑡 𝑡
Need Wind velocity [m/s] Photovoltaic Heat storage tank Sampling time
𝑡𝑙
Transmission line
𝑡𝑝
Electric power interchange
𝑤𝑝
Wind power generation
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Journal Pre-proof
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Author’s name Masaki Okada Terumi Onishi Shin’ya Obara
Affiliation Kitami Institute of Technology Kitami Institute of Technology Kitami Institute of Technology
Journal Pre-proof
Highlights ･ System modeling of existing electricity transmission network ･ Design of placement and capacity of electricity generation equipment was developed. ･ Installation location, type, and capacity of each renewable energy was optimized ･ Example of the electric power system of Hokkaido in Japan was studied
Masaki Okada, Terumi Onishi, Shin’ya Obara PII:
S0360-5442(19)32333-3
DOI:
https://doi.org/10.1016/j.energy.2019.116638
Reference:
EGY 116638
To appear in:
Energy
Received Date:
28 May 2019
Accepted Date:
25 November 2019
Please cite this article as: Masaki Okada, Terumi Onishi, Shin’ya Obara, A design algorithm for an electric power system using wide-area interconnection of renewable energy, Energy (2019), https://doi.org/10.1016/j.energy.2019.116638
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier.
Journal Pre-proof A design algorithm for an electric power system using wide-area interconnection of renewable energy
Masaki Okada Kitami Institute of Technology, Power Engineering Lab., Graduate School of Cold Regions, Environmental and Energy Engineering
Terumi Onishi Kitami Institute of Technology, Power Engineering Lab., Graduate School of Electrical and Electronic Engineering
Shin’ya Obara Kitami Institute of Technology, Power Engineering Lab., Applied Energy Course, Factory of Engineering, School of Earth, Energy and Environment Engineering Koen-cho 165, Kitami, Hokkaido 090-8507, Japan e-mail [email protected] phone/FAX +81-157-26-9262
Keywords: Renewable energy; Facility planning; Wide-area interconnection; Transmission line utilization factor; Genetic algorithm
Abstract1 This study aims to study the utilization factor of an electricity transmission network by determining the optimal installation of renewable energy technologies and heat pumps that store
1
Table of Abbreviations
COP
Coefficient of performance
GA
Genetic algorithm
Journal Pre-proof heat. The proposed model considers the electricity demand, heat load, and meteorological data with respect to each area of the transmission network and selects the type and capacity of renewable electricity sources in each area as well as the capacity of the required compensation electricity supply. The heat input and output of heat equipment and the amount of power supply of the transmission line were decided as each energy balance equilibrating. Therefore, an analysis method that uses genetic algorithm was introduced to achieve optimal operation planning. The proposed methodology is applied to the existing electric system in the island of Hokkaido, Japan, as a case study. Optimization of the arrangement and capacity of renewable electricity generation and transmission network increased the share of renewable energy from 11% to 33.8%. Furthermore, the transmission line utilization factor of the present transmission network improved from 14.5% to 41% when the installation location and capacity of renewable energy were optimized using the proposed methodology.
Keywords: Renewable energy; Facility planning; Wide-area interconnection; Transmission line utilization factor; Genetic algorithm
1. Introduction The amount of renewable energy that can be introduced into an area is limited by the capacity of the corresponding transmission network. Furthermore, a suitable electricity transmission network should be selected with respect to the operation budget when introducing renewable energy technologies The installation of photovoltaic systems and wind turbines over a wide area and interconnecting them using a functioning electrical power grid helps to reduce the power fluctuation of the transmission network, which can be attributed to the nature of the renewable sources. Therefore, establishing a suitable distribution of renewable energy technologies may reduce the overall cost of the energy storage facilities (e.g., heat pumps with a heat storage tank) and compensation electricity systems. However, this requires the determination of several decision variables, including the installation location, type, and capacity of each renewable energy technology
Journal Pre-proof and compensation electricity system. The installation planning of renewable energy, including the design of an isolated island microgrid [1], a multi period mixed-integer linear programming model for investment and design planning [2], and the installation of offshore renewable energy [3], has been previously investigated. With respect to an integrated electrical and thermal energy system, Qin et al. [4] examined the planning of an energy system based on electricity, heat, and gas, whereas Ahn et al. [5] reported the economic feasibility of the combined cooling, heating, and power systems. Further, Sandberg et al. [6] examined a grid tariff structure for obtaining flexible power-to-district heat. The optimal placement and sizing of heat pumps and heat-only boilers in coupled electricity and heating networks was studied by Ayele et al. [7], and the network-constrained economic dispatch of the integrated heat and electricity systems was studied by Huang et al. [8]. Furthermore, Schlachtberger et al. [9] and Pfeifer et al. [10] examined the cooperation of energy network with renewable energy, whereas Ayele et al. [11] investigated the capacity of the heating equipment of the electricity and heat networks and optimization of the installation location. However, the aforementioned researchers did not consider the transmission network during installation planning. Therefore, some studies, including a study of the transmission networks containing a high proportion of renewable energy [12] and of high-voltage direct-current transmission systems to support the integration of renewable energy [13], have been conducted. Generally, the cost performance is observed to be good when a high utilization factor is maintained with respect to the facilities. Although changing the transmission line utilization factor wildly is known, a very low value is observed depending on a time zone can be observed [14]. Therefore, this study aims to study the utilization factor of a transmission network by determining the optimal installation location of the renewable energy technologies and heat pumps used for heat storage that are distributed on the demand side. Further, an algorithm was developed based on the transmission network model for optimizing the placement and capacity of the electricity generation equipment. The adaption of district heating systems to produce a large amount of variable renewable electricity [15], power-to-heat production for peak shaving of renewable power [16], and cooperation system of wind power, heat pumps, and thermal energy storage [17] have been studied with respect to the
Journal Pre-proof operation of a heat pump system using renewable energy. Furthermore, Obara et al. [18] studied the electricity leveling of a wide-area energy network and the installation planning optimization of power supply in case of renewable energy. However, the improvement of the transmission network utilization factor by installation planning in case of renewable energy and the introduction of a heatstorage-type heat pump was not proved. Therefore, electricity and heat distribution networks that can achieve high utilization value
and
a high proportion of renewable energy are proposed in this study. Hence, the proposed renewable energy technologies have to be stabilized by controllable power sources (CPSs), including heatstorage-type heat pumps, oil-fired power plants, and hydroelectric power. A statement of economic efficiency was considered to be the objective function of the system for obtaining an optimal configuration and operating the proposed electric power system. Further, a genetic algorithm (GA), where the chromosomes simulate the terms of the overall system energy balance, was used to propose the optimal variety, capacity, and layout of the renewable energy technologies as well as the installation capacity of the CPSs used for compensation. A case study was conducted with respect to the facility planning of renewable energy technologies and compensation power supply for the Japanese island of Hokkaido by considering the existing transmission network and compensation power supply (thermal power station). Subsequently, the resulting economic efficiency, environmental performance, and transmission line utilization factor were compared with those of the previous electricity system in Hokkaido to analyze the performance of the proposed model.
2. System Outline This Section explains the details of a proposed system, electricity balance and heat balance, and economical efficiency calculation. 2.1. Distributed power supply network An example of distributed power supply via a transmission line is presented in Fig. 1; here, photovoltaic systems and wind turbines are installed in each area, and electricity is consumed on the demand side via a transmission line. When surplus electricity is produced using the renewable
Journal Pre-proof energy technology, electricity is exchanged among various areas via an interconnection device; however, the amount of electricity that can be exchanged is limited by the transmission line capacity. When the surplus electricity exceeds the total electricity and heat demand, the surplus electricity is stored in the form of heat in heat storage tanks using heat pumps installed on the demand side. The stored heat can be output at any arbitrary time.
Fig. 1 Distributed power network
2.2. Energy balance With respect to the electricity balance used in this study, denoted using Eq. (1), the left and right sides represent the power supply and electricity demand, respectively. Here, the electrical output of the photovoltaic system (𝑗) installed in area 𝑖 between sampling time 𝑡 and ∆𝑡 is 𝑃𝑝𝑣,𝑖,𝑗,𝑡. Similarly, 𝑃𝑤𝑝,𝑖,𝑘,𝑡 is the electricity generated from the wind turbine 𝑘 installed in area 𝑖, 𝑃𝑡𝑝,𝑖,𝑡 is the electricity obtained from other areas, 𝑃𝑐𝑝𝑠,𝑖,𝑡 is the electricity generated from the compensation power source installed in area 𝑖, and ∆𝑃𝑏𝑡,𝑐𝑔,𝑖,𝑡 is the electricity output of the storage battery. On the demand side, ∆𝑃𝑛𝑒𝑒𝑑,𝑖,𝑡 represents the electricity load of 𝑖, ∆𝑃ℎ𝑝,𝑖,𝑡 represents the electricity load of the heat pump, ∆𝑃𝑏𝑡,𝑐𝑔,𝑖,𝑡 represents the charge of the storage battery, and ∆𝑃𝑡𝑝,𝑖→ 𝑚,𝑡 represents the amount of electricity supplied to area 𝑚 from 𝑖 between sampling time 𝑡 and ∆𝑡. Furthermore, 𝑁𝑝𝑒𝑟𝑖𝑜𝑑 denotes the operation period, 𝑁𝑎𝑟𝑒𝑎 denotes the number of areas, and 𝑁𝑝𝑣 and 𝑁𝑤𝑝 represent the number of installed photovoltaic systems and wind turbines, respectively.
Journal Pre-proof
{ (
𝑁𝑝𝑒𝑟𝑖𝑜𝑑 𝑁𝑎𝑟𝑒𝑎 𝑁𝑝𝑣
∑ ∑ ∑𝑃
) }
𝑁𝑤𝑝
𝑝𝑣,𝑖,𝑗,𝑡
𝑡=0 𝑖=1 𝑗=1
+
∑𝑃
𝑤𝑝,𝑖,𝑘,𝑡
+ 𝑃𝑡𝑝,𝑖,𝑡 + 𝑃𝑐𝑝𝑠,𝑖,𝑡 + 𝑃𝑏𝑡,𝑑𝑐,𝑖,𝑡 ∙ ∆𝑡
𝑘=1
{ (
𝑁𝑝𝑒𝑟𝑖𝑜𝑑 𝑁𝑎𝑟𝑒𝑎
=
∑ ∑ ∆𝑃
𝑁𝑎𝑟𝑒𝑎
𝑛𝑒𝑒𝑑,𝑖,𝑡
+ ∆𝑃ℎ𝑝,𝑖,𝑡 + ∆𝑃𝑏𝑡,𝑐𝑔,𝑖,𝑡 +
𝑡=0 𝑖=1
∑ ∆𝑃
𝑚=1
(1)
) }
tp,i→m,t
∙ ∆𝑡
The thermal energy balance used in this study is shown in Eq. (2), where the left and right sides represent the supply and consumption, respectively. On the supply side, 𝐻ℎ𝑝,𝑖,𝑡 represents the thermal power supplied between 𝑡 and ∆𝑡 of a heat pump installed in area 𝑖 and 𝐻𝑠𝑡, 𝑜𝑢𝑡,𝑖,𝑡 represents the thermal power of a heat storage tank. On the consumption side, ∆𝐻𝑛𝑒𝑒𝑑,𝑖,𝑡 represents the heat demand between 𝑡 and ∆𝑡 in area 𝑖 and 𝐻𝑠𝑡,𝑖𝑛 ,𝑖,𝑡 represents the heat stored in the heat storage tank. Utilization of the exhaust heat of the compensation power supply was not considered in Eq. (2) because the existing hydraulic and thermal power stations were assumed to be compensation power supplies. In case of distributed power supply with the utilization of exhaust heat, the exhaust heat should be considered on the left side of Eq. (2). Additionally, 𝜂𝑠𝑡,𝑜𝑢𝑡,𝑖,𝑡 and 𝜂𝑠𝑡,𝑖𝑛,𝑖,𝑡 represent the efficiencies of the heat supply and heat storage in the heat storage tank, respectively. The heat storage quantity 𝐻𝑠𝑡 ,𝑖,𝑡 is calculated using Eq. (3) based on the heat loss 𝐻𝑠𝑡,𝑙𝑜𝑠𝑠,𝑖,𝑡 in each sampling time.
𝑁𝑝𝑒𝑟𝑖𝑜𝑑 𝑁𝑎𝑟𝑒𝑎
{
∑ ∑ (𝐻
𝑁𝑝𝑒𝑟𝑖𝑜𝑑 𝑁𝑎𝑟𝑒𝑎
ℎ𝑝,𝑖,𝑡
} ∑ {∑
+ 𝐻𝑠𝑡,𝑜𝑢𝑡,𝑖,𝑡 ∙ 𝜂𝑠𝑡,𝑜𝑢𝑡,𝑖,𝑡) ∙ ∆𝑡 =
𝑡=0 𝑖=1
𝑡=0 𝑖=1
𝐻𝑠𝑡,𝑖,𝑡 = 𝐻𝑠𝑡,𝑖,𝑡 ― 1 ― 𝐻𝑠𝑡,𝑙𝑜𝑠𝑠,𝑖,𝑡
2.3. Economic efficiency
}
(∆𝐻𝑛𝑒𝑒𝑑,𝑖,𝑡 + ∆𝐻𝑠𝑡,𝑖𝑛,𝑖,𝑡 ∙ 𝜂𝑠𝑡,𝑖𝑛,𝑖,𝑡) ∙ ∆𝑡
(2)
(3)
Journal Pre-proof Several costs, including maintenance costs, safety expenses, and personnel expenses, are incurred during the installation and operation of renewable and nonrenewable energy stations. The total cost 𝐶𝑔𝑒𝑛 of various electricity generation sources is calculated using Eq. (4) by multiplying the electricity
generation unit price with the photovoltaic, wind, and compensation power source electricity generation (i.e., 𝑢𝑝𝑣 𝑃𝑝𝑣,𝑖,𝑡, 𝑢𝑤𝑝 𝑃𝑤𝑝,𝑖,𝑡, and 𝑢𝑐𝑝𝑠 𝑃𝑐𝑝𝑠,𝑖,𝑡, respectively). 𝑁𝑎𝑟𝑒𝑎 represents the number of areas that have introduced the electric power system, whereas 𝑁𝑝𝑒𝑟𝑖𝑜𝑑 denotes the operation period.
𝑁𝑝𝑒𝑟𝑖𝑜𝑑𝑁𝑎𝑟𝑒𝑎
𝐶𝑔𝑒𝑛,𝑝𝑒𝑟𝑖𝑜𝑑 =
∑ ∑ (𝑢
𝑝𝑣
∙ 𝑃𝑝𝑣,𝑖,𝑡 + 𝑢𝑤𝑝 ∙ 𝑃𝑤𝑝,𝑖,𝑡 + 𝑢𝑐𝑝𝑠 ∙ 𝑃𝑐𝑝𝑠,𝑖,𝑡) ∙ ∆𝑡
(4)
𝑡=0 𝑖=0
Consignment costs are incurred and should be considered when transporting electricity to some areas. The electricity consignment cost 𝐶𝑡𝑝 of the entire transmission network can be obtained using Eq. (5) based on the consignment electric unit price 𝑢𝑡𝑝 that was decided by an Japanese electric supply company. In Eq. (5), ∆𝑃𝑡𝑝,𝑖→ 𝑚,𝑡 represents the electricity supplied to area 𝑚 from 𝑖 via a transmission line from time 𝑡 to ∆𝑡. If 𝑖 = 𝑚, then the 𝐶𝑡𝑝 of the area is zero. Subsequently, the total cost of the system can be calculated using Eq. (6).
𝑁𝑝𝑒𝑟𝑖𝑜𝑑𝑁𝑎𝑟𝑒𝑎 𝑁𝑎𝑟𝑒𝑎
𝐶𝑡𝑝,𝑝𝑒𝑟𝑖𝑜𝑑 =
(
∑ ∑ ∑𝑢
)
𝑡𝑝∆𝑃tp,i→m,t∆𝑡
𝑡=0 𝑖=1 𝑚=1
𝐹𝑠𝑦𝑠𝑡𝑒𝑚 = 𝐶𝑔𝑒𝑛,𝑝𝑒𝑟𝑖𝑜𝑑 + 𝐶𝑡𝑝,𝑝𝑒𝑟𝑖𝑜𝑑
(5)
(6)
3. System Operation and Introductory Equipment This Section explains the operating method of electricity and heat equipment, and the details of rated capacity. 3.1. Electricity exchange The electricity exchanged via the transmission line between areas 𝑙 and 𝑖 during the sampling time 𝑡, represented using Eq. (7), is calculated as the difference between the supplied electricity ∗ 𝑃𝑡𝑝,𝑙→ 𝑖,𝑡 and the transmission loss 𝑃𝑡𝑝,𝑙𝑜𝑠𝑠,𝑙→ 𝑖,𝑡 in area 𝑙.
Journal Pre-proof
∗ 𝑃tp,l→i,t = 𝑃tp,l→i,t ― 𝑃𝑡𝑝,loss,l→i,t
(7)
The loss accompanying the electrical transmission line of a three-phase three-wire system is represented using Eq. (8), where the line current 𝐼𝑡𝑝,𝑡 can be obtained by dividing the exchange power with the nominal voltage of the power transmission line and 𝑅𝑡𝑝 and 𝐿𝑡𝑝 denote the resistance and length of the transmission line, respectively. Furthermore, 𝐼𝑡𝑝,𝑡 is obtained from Eq. (9), 𝑉𝑟𝑒 is the receiving-end voltage of the load in area 𝑖, and cos ∅ is the power factor.
𝑃tp,loss,l→i,t = 3𝐼𝑡𝑝,𝑡2𝑅𝑡𝑝𝐿𝑡𝑝 𝐼𝑡𝑝,𝑡 =
∆𝑃𝑛𝑒𝑒𝑑,𝑖,𝑡 3 ∙ 𝑉𝑟𝑒cos ∅
(8) (9)
3.2. Electrical equipment The electrical output of the photovoltaic systems and wind turbines is obtained based on the solar radiation and wind speed in each introductory area. Further, the conversion efficiency of the used photovoltaic systems and the power curve for wind power generation are obtained for each analysis example. The electricity supplied via a transmission network was adjusted using thermal and hydraulic power stations. However, the output adjustment speed of the compensation power supply was not considered even though the energy balance of the transmission network was estimated at each sampling time. Therefore, strict stability of the electricity supply via the transmission network was not addressed. Generally, the maximum transmission line utilization factor is decided by the electric company by considering the stability and reliability of electricity supply. However, because the utilization factor design method used by the Japanese power company is unclear, the utilization factor 𝑢𝑡𝑙 ,𝑖,𝑡 of the power transmission line 𝑖 in sampling time 𝑡 is defined in Eq. (10) as the ratio of the previous value
Journal Pre-proof of power transmission (𝑃𝑡𝑙,𝑖,𝑡) to the maximum operation power (𝑃𝑚𝑎𝑥,𝑡𝑙,𝑖). The average of the integrated value of 𝑢𝑡𝑙 ,𝑖,𝑡, as shown in Eq. (11), represents the annual average utilization factor 𝑈𝑡𝑙,𝑖 in transmission line 𝑖 when 𝑡 = 1 h.
𝑢𝑡𝑙,𝑖,𝑡 =
𝑃𝑡𝑙,𝑖,𝑡
(10)
𝑃𝑚𝑎𝑥,𝑡𝑙,𝑖 𝑌𝑒𝑎𝑟
𝑈𝑡𝑙,𝑖 =
∑𝑡 = 1𝑢𝑡𝑙,𝑖,𝑡
(11)
365 ∙ 24
The charge and discharge efficiencies, cycle life, and maintenance cost must be considered when planning to use batteries for electrical energy storage. Here, these factors were considered for obtaining the electricity balance in Eq. (1), and the charge and discharge efficiencies are passed to 𝑃𝑏𝑡,𝑑𝑐,𝑖,𝑡 and ∆𝑃𝑏𝑡,𝑐𝑔,𝑖,𝑡, respectively. The specifications of the storage batteries are presented in Table 1 [19, 20]. In the analysis of this study, a lithium battery is used based on its performance in terms of the cost, cycle life, charge time, and charging efficiency.
Table 1 Performance comparison of various storage batteries [19, 20] Type Parameter Cost/kWh Cycle life (80% DoD) Energy density (Wh/Kg) Depth of discharge (approximate) Fast charge time Charging efficiency Maintenance required Hot climate Best application
3.3. Heat equipment
Lithium
Lead acid
Nickel
140 USD/kWh (2017)
120 USD/kWh
240 USD/kWh
500–1000 cycles
200–300 cycles
500–1000 cycles
120–160
30–50
60–120
20% of 2000 cycles
20% of 500 cycles
20% of 2500 cycles
2–4 h
8–16 h
2–4 h
99% at 4-hour rate
80% at 4-hour rate
95% at 4-hour rate
Moderate
High
Low
Great sustainability
Severe effect
Moderate effect
Portable devices
Renewable energy storage
Emergency lighting
Journal Pre-proof The power consumption ∆𝑃ℎ𝑝,𝑖,𝑡 that is required to obtain the thermal power 𝐻ℎ𝑝,𝑖,𝑡 of heat pump 𝑖 in sampling time 𝑡 and calculate the heat pump’s coefficient of performance 𝐶𝑂𝑃𝑖,𝑡 is obtained using Eq. (12).
(12)
∆𝑃ℎ𝑝,𝑖,𝑡 = 𝐻ℎ𝑝,𝑖,𝑡/𝐶𝑂𝑃𝑖,𝑡
The heat storage tank operated under the following conditions: (1) When the heat storage quantity is greater than the heat demand When the heat stored in the heat storage tank is greater than the heat demand, the operating method is determined using Eq. (13).
𝑁𝑝𝑒𝑟𝑖𝑜𝑑 𝑁𝑎𝑟𝑒𝑎
{
∑ ∑𝐻
𝑁𝑝𝑒𝑟𝑖𝑜𝑑 𝑁𝑎𝑟𝑒𝑎
} ∑ {∑
𝑠𝑡,𝑜𝑢𝑡,𝑖,𝑡𝜂𝑠𝑡,𝑜𝑢𝑡,𝑖,𝑡∆𝑡
𝑡=0 𝑖=1
=
}
(∆𝐻𝑛𝑒𝑒𝑑,𝑖,𝑡 + ∆𝐻𝑠𝑡,𝑖𝑛,𝑖,𝑡𝜂𝑠𝑡,𝑖𝑛,𝑖,𝑡) ∙ ∆𝑡
𝑡=0 𝑖=1
(13)
(2) When the heat storage quantity is lesser than the heat demand When the heat stored in the heat storage tank installed in an area is less than the demand, the operating method is determined based on Eq. (14), and the heat pump is observed to provide insufficient thermal storage.
𝑁𝑝𝑒𝑟𝑖𝑜𝑑 𝑁𝑎𝑟𝑒𝑎
{
∑ ∑ (𝐻
𝑁𝑝𝑒𝑟𝑖𝑜𝑑 𝑁𝑎𝑟𝑒𝑎
ℎ𝑝,𝑖,𝑡
} ∑ (∑
+ 𝐻𝑠𝑡,𝑜𝑢𝑡,𝑖,𝑡 ∙ 𝜂𝑠𝑡,𝑜𝑢𝑡,𝑖,𝑡) ∙ ∆𝑡 =
𝑡=0 𝑖=1
𝑡=0
𝑖=1
)
∆𝐻𝑛𝑒𝑒𝑑,𝑖,𝑡∆𝑡
(14)
4. Analysis Method This Section explains the details of optimization method of the proposed system by GA. 4.1. Search for an optimal operating method An optimal operating method that satisfies the objective function can be obtained based on the electricity equation (Eq. (1)) and the heat equation (Eq. (2)). The given meteorological data were used
Journal Pre-proof to estimate the electricity generation of the photovoltaic systems and wind turbines, whereas the electricity and heating demands of each area were estimated based on the number of households in the area. Subsequently, the installation capacity of the photovoltaic systems and wind turbines, the capacity of the compensation power supply, the amount of charging or discharging operations observed in case of a storage battery, and the amount of thermal power stored by the heat pump were determined as decision variables. The heat input and output of a heat storage tank and the amount of power supply of the transmission line were decided as each energy balance equilibrating. An analysis method using a GA was introduced to search for the optimal solution.
4.2. Chromosome model This study presents the installation capacity of the photovoltaic systems and wind power generation systems, the output of the power supply for compensation, the amount of charging and discharging operations in case of a storage battery, and the amount of thermal power stored by the heat pump using a GA chromosome model. An example chromosome that denotes the amount of produced photovoltaic (PV) and wind turbine (WP) electricity is shown in Fig. 2(a). Here, the energy balances of Eqs. (2) and (3) in area 𝑖 correspond to A to N in the figure and are a set of 18-bit binary numbers. When surplus of renewable electricity can be observed in two or more areas, the supply area for the surplus electricity should be determined based on the economic efficiency. Therefore, a supply ranking of surplus electricity is determined using the chromosome model. A chromosome model that denotes the supply ranking of surplus electricity for areas 0–13 is randomly presented in Fig. 2(b). Figure 2(d) shows a chromosome model group when setting a chromosome model organized by Figs. 2(a)–(c) with 1000 as the generation number and 1000 individuals.
Journal Pre-proof
Fig. 2 Chromosome model
4.3. Objective function The objective function of the system is the maximization of economic efficiency (minimization of cost) and is calculated using Eq. (15), where 𝐹𝑠𝑦𝑠𝑡𝑒𝑚 is defined using Eq. (6). The arrangement of distributed power supply, capacity of the compensation power supply, and operating method of each
Journal Pre-proof equipment are obtained based on the solution that satisfies the objective function most among the obtained chromosome model groups based on a random search using GA.
𝐹𝑠𝑦𝑠𝑡𝑒𝑚→minimize
(15)
Figure 3 shows the analysis flow of the optimization algorithm using GA. The analysis method of a wide-area interconnection energy network using GA has been previously presented [18].
Fig. 3 Analysis flow
5. Case Analysis
Journal Pre-proof The proposed algorithm was applied to the existing electric grid and district heating network of the island of Hokkaido, Japan, which is a cold snowy area in northern Japan with an area of 83,420 km2, a population of 5,350,000, a highest average temperature of 22.3 ℃ in August, and a minimum average temperature of −3.6 ℃ for a representative day in January.
5.1. Electricity system The existing transmission network of Hokkaido is presented in Fig. 4 and can be divided into seven areas, i.e., A–G [21]. In 2017, the electric supply scheme comprised coal-fired thermal (49%), oil-fired thermal (25%), renewable energy (11%), and hydroelectric (7%) generation facilities. The total electricity generation capacity was approximately 8.0 GW, and the maximum consumption of 5.34 GWh was observed in December 2016. These seven areas are classified according to climate condition.
Area F
Area D Area E Area A
Area G Area C
Area B 0
100km
Transformer substation Switching station 275,000 V 187,000 V 100,000-200,000 V Transmission line of the other company
Fig. 4 Transmission network of Hokkaido
Journal Pre-proof Subsequently, a transmission network was assumed over 14 cities (A–N) based on this information, as shown in Fig. 5. In cities D, E, H, and I, thermal power compensation supply was provided. Largescale photovoltaic systems and wind turbines were interconnected among cities A, C, E, H, I, K, and N. Only the electricity generated from wind turbines was interconnected among cities B, D, F, G, J, L, and M. Based on the climate and the existing electricity system, photovoltaic systems and wind turbines were added to the model in seven and 14 cities, respectively. There is no densely populated area equivalent to city J; hence, the electricity and heat demands in city J were assumed to be zero. The location of compensation power supply is set up similar to the position of the thermal power station of the present Hokkaido Electric Power Co. Inc. The setting capacity of each compensation power supply is optimized using GA. Furthermore, a storage battery is not installed in this example.
Area F A
C
B
Area D N Area A
D E
M
Area E
F
K I
Area C J
G
L Area G 110kV Transmission line 187kV Transmission line 275kV Transmission line City with power supply for compensation City with solar farms and wind farms
H
Area B
City with wind farms
Fig. 5 Setting the transmission network and electrical machinery and apparatus
Transmission line between cities
Journal Pre-proof L to M K to L J to K I to K I to J F to I E to I E to F C to E C to D 0
10
20
30 Utilization factor [%]
40
50
60
Fig. 6 Average utilization factor of the transmission line
5.2. Transmission line utilization factor The capacity of a transmission line corresponds to the electricity load expected based on the population of each area. Therefore, each transmission line utilization factor was calculated using Eq. (10) and Eq. (11) (see Section 3.2.3) based on the electric usage records in 2017 [22]; the resulting utilization factor of each intercity transmission line using this dataset is shown in Fig. 6. Here, the average utilization factor was less than 55%, and all the transmission lines were well below their capacity, as shown in Fig. 7 [23]. With respect to the average transmission line utilization factor on the range of annual electricity demand, approximately 68% availability could be observed in June (i.e., the average transmission line utilization factor in June is 32% in Fig. 7).
60
300
50
250
40
200 30
150
20
100
10
50 0 January March May February April
Average utilization factor [%]
Monthly electricity load [GW]
350
0 July September November June August October December Month
Fig. 7 Monthly electricity load and average utilization factor of the transmission network of Hokkaido
Journal Pre-proof
5.3. Energy demand characteristics The electricity and heat demands, predicted based on Hokkaido Electric Power Co. Inc.’s 2017 demand records and the heating demand characteristics of individual houses [24], are presented in Figs. 8 and 9, respectively. Because 70%–80% of the individual houses in the studied region used kerosene for heating and hot water supply, large load peaks could be observed in the morning and evening, and the time shift of heat supply by heat storage is effective. Therefore, the proposed system considers the introduction of a heat-storage-type electric heat pump in each house.
Electric power demand [GW]
4.0
January December
3.0
February
March
November
July
April Octobar 2.0
September
0
3
6
9
May
12 Time [Hour]
15
June
August
18
21
24
Fig. 8 Electricity demand of Hokkaido, 2017
30
Heat demand [GW]
25
March
20
November
15
October
10 September 5 0
0
3
February January December
April May June July, August 6 9
12 Time [Hour]
15
18
Fig. 9 Heat demand of Hokkaido, 2017
21
24
0.30
Electric power [kW/m2]
Electric power [kW/m2]
Journal Pre-proof 0.25 0.20 0.15 0.10 0.00 2
3
4
5
6
7
8
9
10
11
0.00 1
2
3
4
5
6
7
(c) Obihiro
0.15 0.10 0.00 4
0.10
Month
0.20
3
0.15
Month
0.25
2
0.20
(a) Wakkanai 0.30
1
0.25
12
Electric power [kW/m2]
Electric power [kW/m2]
1
0.30
5
6
7
8
9
10
11
12
8
9
10
11
12
8
9
10
11
12
0.30 0.25 0.20 0.15 0.10 0.00 1
2
3
4
5
6
7
Month
Month
(b) Asahikawa
(d) Muroran
Fig. 10 Electric power output of photovoltaics in each city
Soya cape Wakkanai
Rumoi
Asahikawa Higashikagura
Obihiro
Muroran Erimo cape
Fig. 11 Each city in Hokkaido
5.4. Renewable energy sources Figure 10 shows an output example of the photovoltaic systems in each city in Hokkaido obtained based on the amount of solar radiation [25]. The conversion efficiency of the photovoltaic systems is
Journal Pre-proof 18.5%, and each city is denoted in Fig. 11. With respect to the output characteristics of the photovoltaic systems, a difference is observed in each area in Fig. 10; when the photovoltaic systems in several different areas are interconnected, smoothing of the power fluctuations is expected during the cyclic period.
50
Wind speed [m/s]
Wind speed [m/s]
50 40 30 20 10
40 30 20 10 0
0 1
2
3
4
5
6 7 Month
8
9
10
11
12
1
2
3
4
(a) Soya cape Wind speed [m/s]
Wind speed [m/s]
40 30 20 10 0 2
3
4
5
6 7 Month
6 7 Month
8
9
10
11
12
9
10
11
12
(c) Higashikagura
50
1
5
8
9
10
11
50 40 30 20 10 0
12
(b) Rumoi
1
2
3
4
5
6 7 Month
8
(d) Erimo cape
Fig. 12 Measuring example of average annual wind speed at each location
The wind speed data [26] obtained from various places in Hokkaido were used to calculate the available electricity generation from wind turbines via Eq. (16); the results are shown in Fig. 12. Here, 𝑣𝑤𝑝 represents the wind speed at the hub height of the turbine 𝑧ℎ𝑢𝑏 and 𝑣𝑔𝑑 represents the wind velocity at ground height 𝑧𝑔𝑑. As documented by the New Energy and Industrial Technology Development Organization (NEDO) of Japan, n = 5 and n = 7 for the coastal and inland cities, respectively [27].
𝑣𝑤𝑝 = 𝑣𝑔𝑑(𝑧ℎ𝑢𝑏 𝑧𝑔𝑑)
1𝑛
(16)
Journal Pre-proof The power curve of the introduced wind turbine is shown in Fig. 13. The rotor diameter, sweep area, and hub height are 61.4 m, 2960 m2, and 68 m, respectively. The rated output of one turbine is 1 MW [28]. Upon analysis, the wind speed in each area is given in Fig. 13, and electricity is produced.
Electric power [MW]
1.2 1.0
Rated wind speed
0.8 0.6 0.4
Cut-in wind speed
0.2 0.0
0
5
Cut-out wind speed
10
15
20
25
30
Wind speed 𝑣𝑤𝑝 [m/s]
Fig. 13
Power curve of wind power generator
5.5. Heat pump The coefficient of performance (COP) of the introduced heat pump, a Queuton ESA301 series of Mitsubishi Heavy Industries, Ltd., is shown in Fig. 14 [29].
5.0 4.0
COP
3.0 2.0 1.0 0.0 -30
-20
-10
0
10
20
30
Temperature [℃ ]
Fig. 14 COP of heat pump [17] 5.6. Setup of cost A summary of the equipment and consignment costs of electricity production as released by the Ministry of Economy, Trade and Industry of Japan [30] and the Hokkaido Electric Power Co. Inc. [31]
Journal Pre-proof is presented in Table 2. Here, the capital costs (including the construction, fixed property tax, and decommissioning costs) and maintenance costs (including the personnel, repairing, and operating expenses) are included in the cost of photovoltaic systems and wind turbines.
Table 2 Cost of power generation, compensation power source, and consignment of electricity
Generator Photovoltaic Wind power generation
Power generation cost (USD/MWh) 218 194.6
Compensation power source 219 (coal- and oil-fired power ) Consignment cost of electricity
36.9
A heat pump accompanied by a heat storage tank can be installed in each area. The cost of the heat equipment facilities is obtained based on Ecoqute in 2017 [32]. The cost of the heat pump per MW for ensuring operation for 20 years is 32,880 USD. Furthermore, the heat storage tanks are 2080 USD per MWh. Equation (17) denotes the cost 𝐶ℎ𝑒𝑎𝑡 of the heat equipment facilities in Eq. (17), 𝑢ℎ𝑝 is the unit price of the heat pump facilities, and 𝑉ℎ𝑝 is the installed capacity. Moreover, 𝑢ℎ𝑠𝑡 is the unit price of the heat storage tank facilities, and 𝑉ℎ𝑠𝑡 is the installed capacity. The heat storage and heat output efficiencies of the heat storage tank were set to 90%.
𝐶ℎ𝑒𝑎𝑡 = (𝑢ℎ𝑝 ∙ 𝑉ℎ𝑝) + (𝑢ℎ𝑠𝑡 ∙ 𝑉ℎ𝑠𝑡)
(17)
The transfer of electricity between areas was assigned a cost according to the consignment fees obtained from the Hokkaido Electric Power Co. Inc. (36.94 USD/MW) [33].
Journal Pre-proof 5.7. GA parameters The GA parameters used in this case study are summarized in Table 3. The sampling time of analysis was set to 1 h. The values in Table 2 were decided by trial and error from the range of combination with good convergence characteristics. The chromosome model in Fig. 2 is applied to the analysis example. Photovoltaic systems are installed in seven areas, whereas wind power generation systems are installed in 14 areas.
Table 3 GA parameters Generation number
1000
Chromosomes number
1000
Sampling time
1h
Probably of cross-over
95%
Probably of mutation
0.2% Operation of selection
Selection rate of first rank Selection rate of second to 5th
15% in all the individuals 5% in all the individuals
6. Case Study Results This Section describes the analysis result of the introductory example to Hokkaido with the proposed system. 6.1. Capacity limitation of the transmission network and shortage of electricity supply The electricity system assumed in Fig. 5 was obtained based on a conventional power grid. A capacity shortage could be observed in part of the existing transmission line, as shown in Fig. 15, because the heat demand was added to the conventional electricity demand. Therefore, electricity shortage occurred in the northern (cities A, B, and C) and eastern areas (cities L, M, and N).
City
Journal Pre-proof N M L K J I H G F E D C B A 0
50
100
150
200
250
300
350
400
Shortage of electricity [GWh]
Fig. 15 Analysis result of insufficient electricity in 2017
14 Total electricity load
Electricity [GW]
12 10 8 6 4
Total supply (CPS and renewable energy)
2 0
Renewable energy 0
3
6
9
12
15
18
21
24
Time [Hour]
Fig. 16 Total electricity demand on January 18, 2017, and power supply by each power source
The greatest daily electricity shortage, corresponding to the data obtained from January 18, 2017, is presented in Fig. 16, where the total supply includes renewably sourced and compensated electricity. This day also corresponded to the lowest total renewable energy output in all the areas, requiring increased production from the compensation power supplies. However, the transmission network exhibits capacity shortage, and power failures occur in the northern and eastern areas of Hokkaido.
City
Journal Pre-proof N M L K J I H G F E D C B A 0.0
Solar farm Wind farm
0.2
0.4
0.6
0.8
1.0
1.2
1.4
Capacity [GW]
Fig. 17 Analysis result of introduction capacity of renewable energy
6.2. Dissolution of power failure with an increase in the capacity of the transmission network An optimal capacity of transmission lines was selected to compensate for the electricity shortage during winter. The resulting amount of introduced renewable electricity production for each city is shown in Fig. 17. The total installed capacities of the photovoltaic systems and wind turbines were 0.6 and 3.7 GW, respectively. Because the wind power generation had few daily ranges of output, the amount of introduction to the network increased. Figure 18 shows the results of the power demand amount for each city and the production of electricity using renewable energy. More than 60% of the electricity demand in cities A, B, and M could be met via the usage of renewable sources, whereas less than 30% of the electricity demand could be met in cities D, E, H, and I, where the compensation power supplies were installed. In a city that assumes installation of the compensation power supply, operation planning was conducted to ensure that the introduction amount of renewable energy may be reduced.
City
Journal Pre-proof N M L K J I H G F E D C B A 0
Electricity demand Electricity generation by renewable energy
2
4
6
8
10
12
14
16
18
Electricity [ × 103 GWh]
Fig. 18 Analysis result of electricity demand of each city, and sum total of production of electricity by renewable energy
6.3. Renewable vs nonrenewable electricity use After optimization, the renewable sources accounted for 33.8% of the electricity generation in Hokkaido. However, after the introduction of the heat storage-type heat pump and modification of the transmission network capacity, this proportion became 22.5%, which represents twice the amount of renewably sourced electricity generated in 2017, except for hydroelectric power generation. Subsequently, the renewable energy ratio of electricity is controlled in the areas in which the compensation power supply is controlled to restrict the consignment cost of renewable electricity. Thus, the power ratio of renewable energy to the whole system can be obtained. Furthermore, the analysis result of the total capacity of the compensation power supply was 15.8 GW; this value was 127% the maximum power loads of the proposed system (12.4 GW) because the loss observed in a transmission network was added to the capacity of the compensation power supply.
6.4. Installed capacity of heat equipment The resulting installed heat pump capacity in each city is shown in Fig. 19; the total installed capacity of the entire system was 8.6 GW. The installed capacity of heat storage tanks is shown in Fig. 20. The capacity of the heat pump in each city becomes less than the maximum heat load because
Journal Pre-proof the heat storage tanks are only installed in cities A, G, K, L, and N. The fluctuation in electricity generation that can be attributed to renewable sources was controlled by the consignment of
City
electricity from connected cities and by distributed heat-storage-type heat pumps.
N M L K J I H G F E D C B A 0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
Capacity [GW]
City
Fig. 19 Analysis result of introduction capacity of a heat pump
N M L K J I H G F E D C B A 0
0.5
1.0
1.5
2.0
Capacity [GWh]
Fig. 20 Analysis result of introduction capacity of a heat storage tank
The resulting total heat storage quantity of the heat storage tanks in each area is shown in Fig. 21, where the horizontal axis shows the day in each month that requires the greatest heat storage. Although all the surplus electricity is converted into heat and the capacity of the heat storage tank, the capacity of the heat storage tank will drastically decrease when the heat exceeding demand can
Journal Pre-proof be discarded. By introducing heat pumps to store the excess generated renewable electricity, the carbon dioxide emissions of 1,720,000 households, representing approximately 70% of the 2,440,000 households, were decreased [34, 35].
3.5
Capacity [GWh]
3.0 2.5 2.0 1.5 1.0 0.5 0.0
One day
January March May July September November February April June August October December Representative day of each month
Fig. 21 Analysis results of total capacity of a heat storage tank for a representative day with maximum heat storage in each month
6.5. Average transmission line utilization factor The resulting average annual and monthly transmission line utilization factors are shown in Figs. 22 and 23, respectively. Three cases (Cases A, B, and C) were assumed while performing this analysis. The electricity load of Fig. 8 was applied to the present transmission network (Case A); further, the distribution capacity of the transmission network was modified (the heat load of Fig. 9 was not applied, Case B), and the distribution capacity of the transmission network was modified (the heat load of Fig. 9 was applied, Case C). The average transmission line utilization factor was 41% when electricity was supplied using the present transmission network. However, the transmission line utilization factor in wide-area interconnection considerably decreases to 13.5% when the arrangement and capacity of renewable energy was optimized. When the renewable energy arrangement and capacity are optimized, the local supply and local consumption of electricity in the area are expected to considerably increase. Because renewable electricity is consumed in the area in which it is produced, the transmission line utilization
Journal Pre-proof factor considerably decreases, indicating that the capacity of the transmission line required for interconnection between areas can be decreased. The average transmission line utilization factor in Case C was 5.1%, indicating that the local supply and consumption of electricity were greater than those in Case B due to the introduction of a heat storage tank. Although the capacity of the transmission network was greater in Cases B and C than that in Case A, the introduction of a heatstorage-type heat pump distribution reduced the capacity of the transmission lines.
Utilization factor [%]
40
41 %
30
Case A: Electricity load (Fig. 8) is applied to present transmission network Case B: Distribution capacity of transmission network is modified (heat load of Fig. 9 is not applied) Case C: Distribution capacity of transmission network is modified (heat load of Fig. 9 is applied)
20 13.5 % 10 0
5.1 % Case A
Case B
Case C
Fig. 22 Analysis results of annual average of the utilization factor of a transmission network of the Hokkaido model
60
Utilization factor [%]
50 Case A 40 30 Case B
20 10
Case C
0 January March May July September November February April June August October December Representative day of each month
Fig. 23 Analysis results of monthly average of the utilization factor of a transmission network of the Hokkaido model
6.6. Cost analysis
Journal Pre-proof The resulting overall costs of the photovoltaic systems, wind turbines, compensation power supply, electricity consignment, and heating equipment are displayed in Fig. 24. The capital and operation costs were included in the costs of the photovoltaic systems and wind turbines, and the capital, operation, fuel, and carbon tax costs were included in the electricity costs of the compensation power supply. The total cost of the photovoltaic systems, wind turbines, and compensation supply accounted for approximately 90% of the total costs in Cases B and C. Compared with the photovoltaic systems, the introduction of wind power generation was planned during day and night. However, because the time zone of weather conditions appeared when the output of renewable energy of the Hokkaido whole area is hardly obtained, the capacity planning of compensation power supply increase.. Because Case C considered that the electricity load will satisfy the heating demands, the exchange of electricity increased, resulting in an increase in the electricity consignment cost. The costs of the heat storage tank and heat pump are almost similar to the cost of the electricity consignment; however, the cost obtained based on this analysis example with respect to the power supply was considerably large.
× 109
9 8
Cost [USD]
7 6
Case C
5 4
Case B
3 2 1 0
Total Solar farm Wind farm CPS
Delivery
Heat Heat pump storage tank
Fig. 24 Analysis results of cost
7. Conclusions To validate the model, a case study was performed on the existing electricity system in the island of Hokkaido, Japan. The following conclusions were obtained from this case study:
Journal Pre-proof
When renewable energy was distributed in large quantities, any transmission line denoting electric power shortage provided with electric power from an existing transmission network (the northern area and the eastern area of Hokkaido). Further, the cause of insufficient transmission line capacity can be verified using the proposed algorithm and by modifying some transmission networks; the proportion of renewable energy in the whole electric power system was observed to drastically increase.
Optimization of the arrangement and capacity of renewable electricity generation as well as the capacity of the transmission network increased the share of renewable energy, excluding hydroelectric power, from 11% to 33.8%.
Optimization of the electricity system allowed the reduction of carbon dioxide emissions from 70% of the households (i.e., 1,720,000 of 2,440,000 households in Hokkaido).
When the installation location and capacity of renewable energy were optimized using the proposed method, the transmission line utilization factor obtained using the present transmission network was 41% (the actual present transmission line utilization factor is approximately 14.5%). However, the transmission line utilization factor decreased to 13.5% when the installation location and capacity were optimized by assuming wide-area interconnection of renewable energy because the local supply and consumption of electricity were expected to increase; furthermore, the distribution of electricity to other areas will drastically decrease.
Nomenclature 𝐶 𝐶𝑂𝑃 cos ∅ 𝐹𝑠𝑦𝑠𝑡𝑒𝑚 𝐻
Cost [USD] Coefficient of performance Power-factor Objective function [USD] Heat [W]
Journal Pre-proof ∆𝐻
Heat load [W]
𝐼
Current [A]
𝐿
Length of transmission line [km]
𝑁
Number
𝑁𝑎𝑟𝑒𝑎 𝑁𝑝𝑒𝑟𝑖𝑜𝑑 𝑃
Number of area Period of operation [Hour] Electric power [W]
𝑃∗
Amount of electric power interchange [W]
∆𝑃
Electric power load [W]
𝑅
Resistant [Ω]
∆𝑡
Interval time [Hour]
𝑢
Unit price [USD/W]
𝑢𝑡𝑙,𝑖
Utilization factor of transmission line
𝑈𝑡𝑙,𝑖
Average annual utilization factor of transmission line
𝑉 𝑉𝑟𝑒
Capacity of equipment [W] Voltage of receiving end [V]
𝑣
Wind velocity [m/s]
𝑧
Height [m]
Greek characters 𝜂
Efficiency
𝑏𝑡
Battery
𝑐𝑔
Battery charge
Subscript
𝑐𝑝𝑠
Compensation power supply (Controllable Power Source)
𝑑𝑐
Battery discharge
𝑔𝑑
Ground
Journal Pre-proof 𝑔𝑒𝑛
Generation
ℎ𝑝
Heat pump
ℎ𝑢𝑏
Hub of wind turbine
𝑙𝑜𝑠𝑠
Energy loss
𝑛𝑒𝑒𝑑 𝑣 𝑃𝑣 𝑠𝑡 𝑡
Need Wind velocity [m/s] Photovoltaic Heat storage tank Sampling time
𝑡𝑙
Transmission line
𝑡𝑝
Electric power interchange
𝑤𝑝
Wind power generation
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Conflict of Interest and Authorship Conformation Form Please check the following as appropriate: o
All authors have participated in (a) conception and design, or analysis and interpretation of the data; (b) drafting the article or revising it critically for important intellectual content; and (c) approval of the final version.
o
This manuscript has not been submitted to, nor is under review at, another journal or other publishing venue.
o
The authors have no affiliation with any organization with a direct or indirect financial interest in the subject matter discussed in the manuscript
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The following authors have affiliations with organizations with direct or indirect financial interest in the subject matter discussed in the manuscript:
Author’s name Masaki Okada Terumi Onishi Shin’ya Obara
Affiliation Kitami Institute of Technology Kitami Institute of Technology Kitami Institute of Technology
Journal Pre-proof
Highlights ･ System modeling of existing electricity transmission network ･ Design of placement and capacity of electricity generation equipment was developed. ･ Installation location, type, and capacity of each renewable energy was optimized ･ Example of the electric power system of Hokkaido in Japan was studied