Residential Microgrids and Rural Electrifications

Residential Microgrids and Rural Electrifications contains an overview of microgrids' architecture, load assessments, designing of microgrids for residential systems, and rural electrifications to help readers understand the fundamentals. Including many new topics in the field of home automation and the application of IoT for microgrids monitoring and control, the book includes sections on the infrastructure necessary for charging Electric Vehicles in residential systems and rural electrifications and how to estimate the energy and cost of various combinations of energy resources. Many examples and practical case studies are included to enhance and reinforce learning objective goals.

Those in engineering research and technical professions will be able to perform energy and cost analyses of various combinations of energy sources by using advanced, real simulation tools.

• Features methods for adopting and applying artificial intelligent techniques in microgrids for improving reliability
• Addresses the role of battery energy storage systems, the reliable operation of microgrids, international standards such as IEC and IEEE standards, and safe handling techniques
• Covers IoT for the monitoring and control of microgrids and the adoption of recent technologies

• Language: English
• Publisher: Academic Press
• Release date: Dec 3, 2021
• ISBN: 9780323904506

Book preview

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Microgrids planning for residential electrification in rural areas

Rahmat Khezri¹, Amin Mahmoudi¹ and Mohammad Hassan Khooban²,    ¹College of Science and Engineering, Flinders University, Adelaide, SA, Australia,    ²Department of Engineering, Aarhus University, Aarhus, Denmark

Abstract

Microgrids are a valuable option for residential electrification in rural areas. Diversity of electricity generation technologies, application of renewable energy resources, and advancements in energy storage technologies have granted more flexibility to integrate microgrids in rural areas. An appropriate microgrid with a suitable generation storage system can be designed on the basis of the geographical specifications of the rural area and the availability of the system components in the region. However, owing to the high cost of the new technologies, the optimal planning of residential microgrids has attracted more attention. Optimal planning or design of microgrids is accomplished to achieve the minimum price with the highest reliability and lowest environmental emission. In this chapter the projection of residential microgrids is discussed. The system structure and relevant components are introduced. The residential microgrid’s optimal planning procedure with required input data, objective functions, and design constraints are explained. The application of HOMER software as powerful software for optimal residential microgrids is presented.

Keywords

Electrification of rural areas; energy storage system; HOMER software; renewable energy; residential microgrids; optimal planning

Chapter Outline

Outline

1.1 Introduction 1

1.2 Microgrids in rural areas 3

1.2.1 Microgrids structure 3

1.2.2 Microgrid configurations 4

1.2.3 Microgrids components 6

1.2.4 Issues related to microgrids in rural areas 7

1.3 Planning of residential microgrids 9

1.3.1 Problem identification 9

1.3.2 Input data 10

1.3.3 Objective functions 12

1.3.4 Design constraints 16

1.3.5 How to solve the microgrids planning problem 18

1.4 HOMER software 19

1.4.1 Software introduction 19

1.4.2 Equipment models in HOMER 19

1.4.3 Optimization in HOMER 21

1.4.4 Output results by HOMER 21

1.4.5 Sensitivity analysis in HOMER 22

1.4.6 HOMER deficiencies 22

1.5 Conclusion 22

References 23

1.1 Introduction

Electricity access is still a significant challenge for more than 1.2 billion people (almost 17% of people in the world) in rural areas worldwide. Africa and Asia have the most critical portion (93%) of the population without access to electricity (Arriaga, Cañizares, & Kazerani, 2014). Residential electrification in rural areas is a challenge for power network designers. Traditionally, the low population in rural areas induced the network designers to spread the power system with long distribution lines for electrification. Sometimes, long transmission lines were also required to electrify communities in remote areas (Combe, Mahmoudi, Haque, & Khezri, 2019a).

By applying the microgrid concept, the electrification of the rural areas eased. A microgrid is a decentralized group of interconnected distributed energy resources (DERs), energy storage systems (ESSs), and loads that can operate in two modes: stand-alone and grid-connected (Khodayar, 2017). The microgrids can be easily installed in rural areas, even remote areas, to supply the load. The generation capacity of microgrids can be changed between kilowatts and megawatts. The markets for commercial and residential applications of microgrids, including rural electrification, telecommunications, and healthcare, are expected to develop at a significant compound annual growth rate in 2020–25 (Microgrid Market, 2021) significantly.

DERs in microgrids can be conventional dispatchable DERs such as diesel generators (DGs) or nondispatchable allocated renewable energy resources such as wind turbine (WT) and solar photovoltaic (PV) (Hatziargyriou, Asano, Iravani, & Marnay, 2007). Conventionally, the DGs are the most used components in rural electrification. However, this causes a high rate of CO2 emission from the use of diesel fuel in DGs. One of the main advantages of spreading microgrids in rural areas is the ability to apply distributed renewable energy resources. This can eliminate the emissions and decrease the cost of the electricity supply. However, this adds intermittent generation to the microgrid, which may cause interruptions in the electricity supply. To ensure the reliability of microgrids using renewable resources, ESSs are strongly needed.

ESS can assist microgrids that rely heavily on renewable energy resources to improve their controllability, stability, and profitability (Jalilpour, Khezri, Mahmoudi, & Oshnoei, 2019). To improve controllability, ESS can be efficiently controlled with the high stochastic generation of renewable energy. A sufficient capacity of ESS can ensure the stability of the microgrid during severe disturbances. To attain higher profitability in microgrids, the surplus power generation of renewable energies can be stored in ESSs during low electricity exchange rates with the upstream grid. Various ESS technologies are available in the market, such as chemical, mechanical, battery, and electromagnetic technologies. Compatibility of renewable energy resources and ESS for any specific microgrid depends on the geographical location, availability in the market, price of the components, and so on.

A microgrid is the best option, with a diverse range of components, for electrification in rural areas. Optimal planning is the first and most crucial stage (Siddaiah & Saini, 2016). In this stage, the optimal capacity of components should be determined by using optimization methods. Hence a mathematical model of the problem is needed to achieve a considered objective function as the minimum cost of electricity (COE). Optimal planning is a challenging problem, owing to the stochastic behavior of loads in microgrids and renewable generation. This chapter explains the optimal planning of microgrids for electrification in rural areas.

This chapter is organized into five sections. Section 1.2 explains the structure, components, and interconnected microgrids for residential electrification in rural areas. The planning problem of microgrids in rural areas is discussed in Section 1.3. The problem identification, objective functions, design constraints, and practical solution algorithms are explained. As the most used tool for optimal planning of microgrids in rural areas, HOMER software is described in Section 1.4. The software is introduced, the equipment and capabilities are discussed, and the optimization procedure with the results and sensitivity analysis are investigated.

1.2 Microgrids in rural areas

Microgrids are the most valuable option for residential electrification in rural areas. In this section the microgrids’ structure, system components, and related issues are discussed.

1.2.1 Microgrids structure

Microgrids are complicated systems in which a diverse range of components are interconnected. Fig. 1.1 shows a schematic diagram of a sample microgrid for residential electrification in a rural area. As illustrated, a range of generation and storage components are connected to the residential microgrid, which can operate in a connection mode through the point of common coupling. The microgrid can exchange energy with the primary grid. The energy exchange rates are specified, and the amount of exchanged energy (imported and exported) is recorded by the smart meter.

Figure 1.1 A schematic diagram of a sample microgrid for residential electrification in a rural area.

The loads of microgrids in rural areas can be residential or agricultural loads. It is vital to have accurate data on the loads. Hence the lifestyle of the people in the area, the type of agriculture, the heating systems in the area, and the vehicles should be specified. The loads are classified as controllable or uncontrollable loads. The uncontrollable loads should be adequately supplied. Several controllable loads in the microgrid can participate in the demand response programs to decrease cost and increase reliability. The controllable loads are categorized as shiftable loads and curtailable loads. Shiftable loads can be controlled by shifting their demand from one time to another time. Loads from the use of dishwashers, electric vehicles (EVs), and washing machines are some examples of shiftable loads. For instance, using the washing machine can be shifted to late at night to decrease electricity demand during the daytime. The charging pattern of EVs is flexible when they are parked at home or in parking lots. By using vehicle-to-home or vehicle-to-grid technologies, the EV can be discharged to supply the loads in the residential microgrid.

The heart of a microgrid is its energy management system (EMS) (Zia, Elbouchikhi, & Benbouzid, 2018). All components in the microgrid are connected to an EMS center by communication lines. For example, load forecasting can update the EMS regarding the electricity demand in the following hours of operation. Then the EMS should allocate enough generation to supply the load adequately. The EMS can use weather forecasts to predict the generation of renewable energy resources during the operation. The EMS also receives the exchange electricity rates from the main grid. It can then decide on the proper control of power flow between the components to decrease the operation cost. The EMS should also receive the available charging/discharging energy of ESS, fuel cost, and maintenance of components.

1.2.2 Microgrid configurations

Based on the connection between microgrid components, various system configurations can be extracted. Fig. 1.2 depicts these microgrid configurations in the presence of DERs, ESSs, main grids, and loads. An essential component for attaining these configurations is the converter. Since the structures are designed according to whether they are DC-coupled, AC-coupled, or hybrid AC–DC-coupled, the power converters play an essential role (Chauhan & Saini, 2014). Each microgrid configuration has advantages and disadvantages. The microgrid configuration shown in Fig. 1.2A is a DC-coupled microgrid in which all the components are connected by DC buses. The main advantage of DC-coupled microgrids is the elimination of power quality issues such as reactive power and harmonic (Maleki, Khajeh, & Ameri, 2016). This configuration is becoming very common among researchers. The main disadvantage is a high penetration of power converters in the microgrid, increasing the cost and decreasing the reliability with converters outages. Fig. 1.2B illustrates an AC-coupled microgrid configuration in which all the components are connected to a common AC bus. The main advantage of an AC-coupled microgrid is its higher reliability compared to a DC-coupled microgrid. In an AC-coupled microgrid, if there is any problem with the power converters, the AC sources that do not need converters can continue the load supply. The major challenges in an AC-coupled microgrid are synchronization and power quality issues (Nehrir et al., 2011). Fig. 1.2C depicts a hybrid AC–DC-coupled microgrid, which is the most preferred configuration nowadays. The main advantages of a hybrid configuration is the flexibility of microgrids in supplying AC and DC loads and lower cost using fewer converter devices (Ogunjuyigbe, Ayodele, & Akinola, 2016).

Figure 1.2 Various configurations of microgrids for electrification of residential in rural area. (A) DC-Coupled microgrid configuration, (B) AC-Coupled microgrid configuration, and (C) Hybrid AC-DC-Coupled microgrid.

1.2.3 Microgrids components

Various components are integrated into microgrids. These components are classified as DGs (dispatchable), renewable energy resources (nondispatchable), and ESSs. The compatibility of each component with any specific microgrids in rural areas should be investigated based on the geographical location, land availability, weather, investment budget, and component availability for that region.

1.2.3.1 Diesel generators

DGs are commonly installed in rural areas for electricity supply. The broad availability of the components in the market and the simple installation are the main reasons for their wide application. However, there are several impediments to the further installation of DGs in rural areas. Dependency on diesel fuel is a significant challenge. Diesel is not available in all countries. Furthermore, the cost of fuel is not constant and changes in the market. The most significant barrier is environmental emissions resulting from fuel consumption. Therefore the installation of DGs for residential electrification of rural areas is decreasing rapidly.

1.2.3.2 Renewable energy resources

Renewable energy resources are competent alternatives for electricity generation in microgrids. The renewable DERs are based on renewable energy, which is naturally replenished. Different renewable DERs have been developed based on the type of renewable energy. WTs, solar PV, biogas systems, and tidal turbines are the most suitable renewable DERs for residential microgrids in rural areas.

Each component has specifications that should be considered in the optimal planning process. For example, the hub height of a WT is vital to use the wind speed in a proportional height. If a solar PV system is installed on rooftops, then the size of PV panels should be considered along with the rooftop area to achieve the optimal capacity. Since tidal turbines are installed in the sea and there may be a long distance between the sea and the community, the power losses in the power line should be taken into account in the planning process.

1.2.3.3 Energy storage systems

Nowadays, ESSs are an inseparable component of microgrids because of the high penetration level of renewable DERs. These components are installed alongside renewable DERs to absorb their extra power after supplying the microgrid load. Then the ESS can be discharged whenever there is not enough generation in the microgrid. The ESS has a much more critical role in off-grid microgrids, in which the ESS has the backup role for the system.

Several ESSs, including electrochemical, mechanical, hydrogen, and chemical ESSs, have been developed to be used in microgrids. Table 1.1 lists the specifications of the most suitable energy storage technologies for application in rural microgrids (Aneke & Wang, 2016; Koohi-Fayegh & Rosen, 2020). The first four rows of the table are electrochemical ESSs known as battery energy storage systems (BESSs). High efficiency, power, and energy densities and reasonable capital cost are the salient features of BESS technologies. The most used BESS technologies are lead-acid, lithium-ion, vanadium redox flow, and sodium-sulfur technologies. Among them, lithium-ion technology has gained the most attention in recent applications for residential microgrids. The main advantages of having a lithium-ion battery in the microgrid are the high efficiency of the technology, high calendar and cycle lifetimes, and a high power rating. The fuel cell is a hydrogen-based ESS that generates electricity by combining hydrogen and oxygen. The main features of a fuel cell are very high energy density and an extended discharge time. However, the high capital cost of the technology and its very low efficiency are the main barriers to increased penetration in microgrids. Supercapacitor energy storages, like the main structure of BESSs, comprise a pair of electrodes (anode and cathode) and an electrolyte with a separator for porous membrane. The main advantages of supercapacitors are their quick discharge time with the highest power density between the storage technologies. However, the supercapacitor energy storage has the lowest energy density. Flywheel energy storage is generally used as a backup for the black start of DGs. The flywheel technology has a very high power density with a low cost. However, the discharge time of the flywheel is short.

Table 1.1

1.2.4 Issues related to microgrids in rural areas

There are several issues related to microgrids for electrification in rural areas that should be adequately studied. These issues are the residential microgrid’s design, control, and stability for electrification in rural areas. The first issue is to design residential microgrids. In the design procedure of microgrids, planning is the most important part to optimize DERs and ESSs to supply the load uninterruptedly. When the planning stage is done, then the control and stability of the microgrid can be investigated for proper operation of the system in transient disturbances.

Optimal planning has become more critical as a result of the global need to decrease CO2 emission. Owing to increasing the penetration of renewable energy resources, the unpredictability of these resources is a big challenge of the planning stage. Generally, microgrid designers need to use high-capacity ESSs to overcome the variable output of renewable energy resources. However, the high cost of ESSs, especially the BESS technologies, is a barrier to use of a high-capacity ESS. Hence the optimal planning should determine whether to use a high-capacity ESS and plan for higher costs of the microgrid or a low-capacity ESS to achieve a lower price with the tradeoffs of lower reliability and higher emission.

1.3 Planning of residential microgrids

The planning process of residential microgrids involves using the required data, objective functions, and design constraints.

1.3.1 Problem identification

Optimal planning of residential microgrids involves optimizing components on the basis of the electricity consumption and other data of the microgrid (Khezri & Mahmoudi, 2020). Hence the problem is an optimization challenge. For this purpose, the situation should be mathematically modeled. Therefore the mathematical model of each component should be generated, the objective function and design constraints should be specified, and the decision variables should be determined. Because the problem is optimal planning, the decision variables are the capacities of the microgrid’s components.

Fig. 1.3 shows a general flowchart for optimal planning of a residential microgrid. The optimization algorithm starts with the required input data for the optimal planning study. Then the optimization algorithm is initialized. The operation of the residential microgrid is evaluated in the next stage. The design constraints should be satisfied during microgrid operation. If constraints are not satisfied, then the algorithm initialization is repeated. Once the design constraints have been met, the objective function is calculated, and the optimal results (capacity of components) are shown. The stages of the optimal sizing procedure are discussed in this section.

Figure 1.3 A general flowchart for optimal planning of residential microgrids.

1.3.2 Input data

A set of data is needed to achieve accurate optimal planning of microgrids for electrification in rural areas. The data should be given to the optimization platform to find the most appropriate components to reach the design goal (minimizing the cost, maximizing the reliability, minimizing pollution, etc.). Inaccurate input data may lead to an incorrect capacity of components, which will jeopardize the proper operation of the microgrid.

While the required input data for optimal planning depends on the microgrid components and location, four categories of data should be analyzed. These four categories are shown in Fig. 1.4 and can be listed as follows:

1. Weather data

2. Load data

3. Electricity rates and grid technical data

4. Technical and economic data of components

Figure 1.4 Required input data for optimal planning of microgrids.

Each of these types of input data is discussed in this section.

1.3.2.1 Weather data

Weather data should be extracted for the microgrid location, and its components are supposed to be installed. The weather data include all meteorological data related to the renewable energy resources to achieve an accurate output generation during the microgrid operation. These data involve the ambient temperature, humidity, solar insolation, wind speed, and wave data. The weather data that are needed depend on the type of renewable energy resources in the microgrid for the rural area. For example, if the microgrid is a solar PV system, then the solar insolation and ambient temperature are enough to get the output generation of the PV. However, if a combined PV-WT is installed, the wind speed of the location is also needed.

The stochastic output power of renewable energy resources should be obtained by using weather data. Depending on the type of study, the weather data can be arranged in seconds, minutes, half-hourly, hourly, and daily. For control and stability studies, the weather data need to be arranged by second or minute. However, hourly data are the most used weather data in planning studies of microgrids. The data should be available for 1 year to attain accurate results. In some studies, 1-day data for each season are used. However, the most used weather data are hourly arranged 1-year data (8760 hours). NASA World Weather (https://worldwind.arc.nasa.gov/worldweather/) and Ninja Renewables (https://www.renewables.ninja/news/raw-weather-data) are the most used websites to attain weather data.

The weather data can also be forecasted by using appropriate methods. The most often applied methods are artificial neural networks and autoregressive integrated moving average. For efficient weather forecasting, these methods use historical data from the location of the microgrid. For example, historical data are needed to teach artificial neural networks for forecasting the day-ahead weather variations.

1.3.2.2 Load data

The load data include the electricity consumption and the type of loads in the residential microgrid. Electricity consumption is the most critical input data for microgrid planning studies. The variation in electricity consumption depends on the loads in the microgrids. The loads are classified as residential, commercial, industrial, and agriculture loads. The electricity consumption can be arranged in seconds, minutes, hours, and days. Like weather data, annual electricity consumption data are needed, which can be hourly.

Types of loads in the microgrid are another type of vital load data. The loads should be specified to determine whether there is any controllable load for demand response during the microgrid operation. The EMS should determine the microgrid demand