Techno-economic analysis and design of hybrid renewable energy microgrid for rural electrification

Grid connected power system

The electrical grid is a complex system, designed to provide electricity to the customers in a secure, reliable and economic way. A grid is classified into generation, transmission, and distribution. Grid extension is used for connecting non electrified regions to the existing grid network and spread the electrical connectivity. It requires constructing the required and associated infrastructure between the grid and the region to be included in the grid. During grid extension, there is no compulsion for new generation or storage systems to be installed in the remote region.

BED is the distance at which the total Net Present Cost (NPC) of grid extension and installing a stand-alone system are equal (Walker; Azimoh et al. 2016) and calculated using Equation (1).

Where C_NPC:total net present cost power system ($)

• E_l: total annual electrical demand, (kWh/yr)

• C_p: cost of power from the grid, ($/kWh)

• C_omG: O & M cost of grid extension, ($/yr/km)

• C_capG: capital cost of grid extension, ($/km)

• i: interest rate per year, (%)

• n: project life time (yr).

Off-grid/standalone MG power supply system

The most common power supply sources for off-grid power system are the RES that are available in the vicinity of the proposed area. In Ethiopia, Solar and Wind power generations are the most commonly available RES for a standalone MG system. The RES also require certain specific equipment’s and energy storage systems to establish and install a complete standalone system.

Solar PV power

Solar power can be derived through solar panels. It can be utilized either for thermal loads or direct electricity generation.

The power output of the solar PV system (P_pv) is computed for every hour as given in Equation (2) (Sawle, Gupta, and Bohre 2018b).

Where Pr: PV panel rated power (W).

• R: radiation factor

• Rcr: certain radiation point, usually set as 150 W/m² (Maleki, Ameri, and Keynia 2015)

• Rsr: standard solar radiation at 1000 W/m²

• n: number of PV panels.

Harvesting of solar irradiation is optimized by using Mechanical and Electrical Maximum Power Point Tracking (Alharthi et al., 2018; Pakkiraiah and Sukumar 2016). Different techniques can be employed to implement the above methods.

Battery storage

Storage of electric power is a crucial factor especially for a standalone system. The energy storage system helps in improving the reliability of a system against the backdrop of intermittent renewable energy sources. For the case study being presented, Battery Storage (BS) is considered. It also improves the system stability and resource utilization rate (Zhu, Guo, and Zhao 2021). Battery State of Charge (SOC) is used to measure the level of charge present on an electric battery with respect to its capacity. It is calculated by using Equation (4).

Where SOC(t): charge level of BS at a given time ‘t’

• SOC(t−1): charge level of BS at a given time ‘t−1’

• σ: self-discharge rate for 1 h (Maleki, Ameri, and Keynia 2015), 0.0002

• Eg(t): generated energy, (kWh).

An accurate estimation of SOC helps to maintain the battery operation within specified limits. This avoids the chances of battery being fully discharged or overcharged.

Converters

Converters play an important role in integrating the renewable energy sources into the standalone MG. Power coming from the renewable source can be AC or DC. Also the BS can be used for storing excess energy, when generation is more than demand. The power generated from these sources have to be converted such that the its parameters are suitable for consumption or storage. For this purpose the converters are used.

Converter rating (pcon) is selected depending on the peak load (Ppeak(t)) and its efficiency (ηcon), which is presented in Equation (5).

Charge controller

The charge controller is used to regulate the excessive charging or discharging of BS by limiting the charging or discharging current. Charge controller capacity (I_cc) is obtained through the ratio of maximum generation capacity of the resource (P_max) and battery voltage (V_batt) as formulated in Equation (6).

There might be a situation in which there is excess generation and the batteries are charged to their maximum capacity. A dump load can be used in such situations, in addition to the charge controller (Salman, Al-Ismail, and Khalid 2020).

Reliability

Power system reliability refers to the ability of a power system to provide a stable and adequate supply. Electrical energy sources such as solar and wind introduce unpredictability in the system due to their intermittent characteristics. The issue of reliability is a matter of great concern for the emerging power systems which employ intermittent sources.

To analyse the off-grid power system reliability: Unmet load and capacity shortage parameters are widely used. Unmet load is load demand that the power supply is unable to satisfy. This usually occurs when supply is quite less than the demand (kWh/year).

The Loss of Power Supply Probability (LPSP) is also widely implemented to assess the system reliability as discussed in (Ma and Javed 2019). LPSP is calculated for a time interval of 0 to T as seen in Equation (7).

where P_L: total load demand, (W)

• P_S: total generated power, (W)

Lower value of LPSP indicates a highly reliable system. The objective of reliability analysis is to design a standalone MG with LPSP as zero, thus making system 100% reliable.

Economic cost

The economics involved in power generation employs different methods to determine the per-unit cost of electrical energy produced. In a power system network, Levelized Cost of Energy (LCOE) ($/kWh/yr) is considered for analyzing the economic performance of various available resources and is formulated as in Equation (8).

where E_L: annual energy produced, (kWh/yr),

• TAC: Total Annualized Cost of the system, calculated by using Equation (Nekkache et al. 2018).

• N_cop: number of components/units used

• C_om: O & M cost, ($/yr/km)

• C_cap: total capital cost, ($/km)

• C_Rep: total replacement cost, ($/km)

• n: project lifetime, (yr)

• LF: components lifetime, (yr)

• i: interest rate per year, (%)

In the case study presented, the model formulation for the given site is a multi-objective function consisting of reliability and cost of energy. The optimal result should have minimized cost, but the reliability should be maximized or LPSP should be minimized. Thus the over all analysis is to minimize the objectives while satisfying the constraints as shown in Equations (10)–(14). The overall objective function is represented as:

Subject to

where PPVmax: maximum power that can be generated from solar PV

• Pwtmax: maximum power that can be generated from wind turbines

• P_bat: battery capacity at time ten

• SOC_min: minimum allowable state of charge for BS

• SOC_max: maximum allowable state of charge for BS

• P_L: Peak Load

The constraints are enlisted in Equations (11)–(14). The ± sign in Equation (14) is to imply that the battery may be charging or discharging. The individual resources should together supply the load at all times, thus their total capacity is required to be more than the peak load.

Methodology

The methodology applied for obtaining the optimal solution for the electrification of Jarre region in Ethiopia involves evaluation and comparison of costs related to grid extension and installation of standalone system. The optimized specification of standalone MG is obtained through the implementation of Particle Swarm Optimization (PSO). The methodology used to determine the size of power generating units to be installed in the MG is shown in Figure (1).

Figure 1:

Flowchart of proposed methodology.

For a standalone MG system, the demand requirement at all times should be known. In case, there is excess generation, the demand is supplied and the batteries are charged, simultaneously. On the other hand, in case of generation being less than the demand will require the battery to discharge. The charging or discharging action of batteries shall depend on the state of charge and the generation of the standalone system in that hour. Load shedding can be resorted to as a final solution when renewable generation and battery storage fail to supply the loads. However, presence of load shedding would infer that the reliability of the system is not 100%.

In the analysis of providing electricity to the remote region of Jarre, two options are to be evaluated. In the first option, grid extension is considered. The costs of grid extension can be evaluated directly, based on the distance be the region being considered and the nearest electrified point. In the second option, a standalone MG is proposed to be installed within the given region. The costs related to such a proposed standalone MG shall depend on the number of PV panels, wind turbines and batteries, that shall be a part of it. Optimization technique of Particle Swarm Optimization is employed to find the optimal combination of these components in case of standalone system.

Particle Swarm Optimization (PSO)

PSO is motivated with the movements of school of fishes or flock of birds. It was developed by Kennedy and Eberhart (1995). PSO is a global optimum search algorithm (Ding et al. 2019), that combines the process of exploitation and exploration. It is a robust method and has been implemented for different variations, like (Badar, Umre, and Junghare 2012). During each iteration of the search problem, particle position is updated as:

1. Particle velocity

Particle velocity gives the difference by which a particle moves from one position to the other in between two iterations. New particle velocity (V(t+1)) for ith particle is calculated by using Equation (15).

1. New particle position

The new particle position (X(t+1)) for ith particle is calculated by using Equation (16).

where

• Vi(t): current velocity of particle

• c₁: personal influence coefficient

• c₂: social influence coefficient

• w: inertia

• pbest: particle best position

• gbest: swarm best position

• Xi(t): particle current position

The flowchart for designing the MG model at Jarre is presented in Figure (2). The initial data including constraints is read first and random particles are generated. The feasibility of the particles is checked in each iteration. Feasibility in this case relates to the combination of renewable sources and batteries being able to satisfy the load at the selected site at any given time. The particles are also checked for their constraint limits. The individual and global best positions are updated and the particles are moved to a new position. In case the stopping criteria is satisfied, the optimal results are printed.

Figure 2:

Flowchart for PSO.

The optimization problem being discussed in this research paper deals with finding the optimal combination of the number of PV panels, wind turbines and battery packs for the standalone microgrid system. The objectives of the optimization process is to find an economic and reliable solution. However, the value of LPSP is considered to be 0, to make system 100% reliable. This converts the problem from a multi objective problem to a feasibility problem, since there are very few solutions that can have LPSP as 0.

PSO method is employed due to ease in programming, gives high-quality solutions with stable convergence, adaptability and robustness. As discussed in (Nekkache et al. 2018; Ghorbani et al. 2018), PSO provides credible results for optimization of reliability and operation cost.

Load profile

Electricity demand of connected households mainly relates to micro-economic theory; income, energy prices and preferences of the household (Louw et al. 2008). At the selected site, there are about 150 households having a daily average load of 3173 kWh/day with 285.5 kW of peak load and a minimum load of 8.25 kW as shown in Figure 3. The common loads used by different households are lighting lamps, TV and radio, mobile charger, refrigerator, ventilation, cooking stoves, etc. Primary school, health post, flour mills and water pumps for drinking and irrigation that operate at morning and afternoon are other loads present in the village.

Figure 3:

Hourly load variation for Jarre village.

Solar resource

In the power generation from solar PV, solar irradiation and temperature are the main factors that determine the output power of a PV cell. The hourly average solar irradiation of the site is taken from Global Solar Atlas database. From the data base, the least solar irradiation is observed for the month of July and these values are considered during the design process to minimize the chances of energy shortage and is shown in Figure 4. Jarre has an average daily solar irradiation of 6.15 kWh/m². From the figure it can be observed that the peak occurs at 12 noon.

Figure 4:

Hourly direct solar irradiation for Jarre village on Earth surface.

Wind resource

As a standard, there are classes of power potential that can be harvested from different wind speed and at different hub height. The classes are mainly influenced by site’s wind speed as summarised in Table 1, taken from world wind speed standards (Wei Tong 2010). Accordingly, Jarre is categorized under class 4 (good) having average wind speed of 5.8 m/s and it is feasible to consider harvesting power from wind turbines.

The daily wind speed of the site is taken as shown in Figure (5). It represents the wind profile of 9th April, 2021 at the selected site.

Figure 5:

Wind speed in Jarre village with hub height of 10 m from (World weather online (2021).

Grid extension cost

In the extension of grid, the cost of MV transmission line is only considered along with its accessories. The costs related to Low Voltage (LV) line construction is neglected as it would be required for the grid extension as well as the standalone system. The total cost of constructing the MV transmission line for 1 km is summarized in Table 2. The data is presented in dollars converted from Ethiopian Birr.

The distance from the village to the nearest substation connected to the grid is approximately 50 km. Considering the costs from Table 2, the total costs involved in extension of the grid to Jarre is $ 1,148,264 and the COE is 0.362 $/kWh.

Standalone system

The cost of standalone hybrid resource is taken from the present cost of components in the market and listed in Table 3. The load curve for the selected site is presented in Figure 3. It can be observed that there are multiple peaks in the curve. However, the peak at noon is the highest and is considered for setting the limits for the number of solar panels, wind turbines and batteries that can be installed in the standalone MG system.

The PSO is executed for obtaining the optimal combination for the number of PV panels, Wind turbines and batteries with an objective of minimizing the cost. The output is also required to satisfy the constraint of LPSP = 0 i.e., the load should be satisfied at all times. PSO is executed 40 times for compilation of the results and in each execution it considers the performance of the system for 48 h. The first 24 h of the simulation is used to find the initial state of charge of the batteries at the beginning of the day. MATLAB is used to apply the PSO technique.

For analysis purpose two cases are assumed: 1) Ideal case – SOC_min is taken as zero while SOC_max is 100%. The efficiency of battery and converter is considered as 100% with zero self-discharge rate in Equation (4) and 2) Practical case – efficiency of battery and converter is taken as 90%, while self-discharge rate is considered as 10⁻⁴. As age of the batteries increase, their SOC capacity declines, resulting in a narrower range of SOC (Raj, Rodrigues, and Abraham 2020). Thus, in the second case, for the analysis of standalone system, SOC_max is taken as 90% and SOC_min as 20%.

Ideal system

Figure 6 shows the variation of cost with respect to variations in the number of panels, turbines and batteries for 40 executions in a sorted order. The minimum cost of standalone system is found to be 0.10368$/kWh with 164 solar panels, 57 wind turbines and 128 battery packs, required to cover the given load, for the ideal case. The total energy balance during the day between the load, energy generated by RES and the charging and discharging of batteries is presented in Figure (8a). It can be observed that the battery is discharged thrice within the day corresponding to the peaks in the load curve.

Figure 6:

Variation of total costs for ideal case results.

Practical system

The number of PV panels, wind turbines and storage batteries obtained through PSO for the practical case are 215, 55 and 182, respectively. The variation of costs with respect to number of PV panels, wind turbines and battery packs for 40 iterations of PSO is shown in Figure 7 in a sorted order. The optimal COE for the system is obtained as 0.11025 $/kWh in this case. Similar to the Ideal case, the total energy available through renewable resources and batteries against the demand of the day is given in Figure 8b. In the practical case, the battery discharge is more for the first load peak and lesser for the second peak as compared to the ideal case.

Figure 7:

Variation of total costs for practical case results. (a) Power demand and supply characteristic for ideal case. (b) Power demand and supply characteristic for practical case.

Figure 8:

Comparison of energy and demand in different cases.

The optimal results are summarized in Table 4, for both the cases. In the table, N_PV is the number of PV panels, N_WT is the number of wind turbines and N_BAT is the number of battery packs. The objective function is minimized while considering all constraints. The combination of RES and battery packs is so formed that the value of LPSP is 0. Thus, the reliability of the system is also satisfied with no scarcity of supply.

Table 4:

Optimal results for different cases.

Cases	NPV	NWT	NBAT	Converter	COE ($/kWh)	LPSP
Ideal	164	57	128	1	0.10368	0
Practical	215	55	182	1	0.11025	0

The COE for grid extension at Jarre village is found to be 0.362 $/kWh whereas the cost of installing a standalone system is 0.10368 $/kWh for ideal battery operating conditions and 0.11025 $/kWh for practical conditions.

Another method to compare the feasibility of the off-grid power supply system for the site, with respect to grid extension is through BED. BED is considered by taking present cost of stand alone system and grid extension costs as listed in Tables 2 and 3. As shown in Figure 9, BED is found to be 31.6 km. As the village is at a more distance from the grid, application of off-grid power supply system is more feasible for the selected site.

Figure 9:

Grid extension cost for break even distance analysis.