Preface

Emerging from a conventional power system to a more evolving dynamical system in which renewable energy sources play major roles, comes with benefits of sustainable and clean power generation in the form of smart grids. However, the intermittency of these resources imposes uncertainty and complexity to different layers of the system. As the continuity of power supply to the end-users is of significant importance, plenty of research related to the design of microgrids, energy management, and strategies is required to achieve a reliable power supply.

This book aims to present the recent materials related to the smart microgrids and the man‐ agement of intermittent renewable energy sources that organized into seven chapters.

Chapter 1 studies the optimal sizing and management of different microgrid configurations, including solar panels, wind turbines, and battery energy storage systems.

Chapter 2 presents an energy management system with several approaches to overcome in‐ termittency and create a semi-dispatchable generation supply in a power system with a small wind turbine, solar panels, fuel cells, and a hydrogen storage system.

Chapter 3 develops a LabVIEW design and testing of an energy management system for the interconnected or islanded operation of a microgrid to the public electric grid.

Chapter 4 works on a new strategy of a home energy management system in a smart grid environment to transform ordinary premises to a smart house to be energy efficient by sim‐ ply rescheduling operation time of the appliances at home.

Chapter 5 presents the unconventional backup structures in low-voltage smart microgrids and switching to a backup power supply to maintain the continuous power supply to the loads.

Chapter 6 focuses on composite system reliability assessment where reliability modeling of power system components is analyzed by the node elimination method and modified minimal cutset method that would be useful in the planning and operation of larger power systems.

Chapter 7 introduces the European research project called PLANET, which will aid different energy networks to leverage innovative energy conversion in alternative carriers and stor‐ age technologies to explore, identify, evaluate, and quantitatively assess optimal grid plan‐ ning and management strategies for future energy scenarios that target full energy system decarbonization.

In the call for the chapters in this book, we were overwhelmed with several high-quality manuscripts. However, to make the quality and clarity of the book easy to follow for differ‐ ent readers, only a handful was selected to briefly present the recent research on the smart microgrids. We therefore would like to thank all the authors who participated in writing this book and sharing their latest findings to the readers and researchers.

We would also like to thank the team at IntechOpen for their continuous support on this book, and special thanks go to Ms. Iva Lipović, the Publishing Process Manager, for her kind support and patience to coordinate the publishing process of this book.

> **Prof. Majid Nayeripour and Prof. Eberhard Waffenschmidt** Cologne University of Applied Science Cologne, Germany

> > **Assoc. Prof. Mostafa Kheshti** School of Electrical Engineering Shandong University, China

**Chapter 1**

**Provisional chapter**

**Renewable Energy Microgrid Design for Shared Loads**

Renewable energy resource (RER) energy systems are becoming more cost-effective and this work investigates the effect of shared load on the optimal sizing of a renewable energy resource (RER) microgrid. The RER system consists of solar panels, wind turbines, battery storage, and a backup diesel generator, and it is isolated from conventional grid power. The building contains a restaurant and 12 residential apartments. Historical meter readings and restaurant modeling represent the apartments and restaurant, respectively. Weather data determines hourly RER power, and a dispatching algorithm predicts power flows between system elements. A genetic algorithm approach minimizes total annual cost over the number of PV and turbines, battery capacity, and generator size, with a constraint on the renewable penetration. Results indicate that load-mixing serves to reduce cost, and the reduction is largest if the diesel backup is removed from the system. This cost is optimized with a combination of particle swarm optimization with genetic-algorithm approach minimizes total annual cost over the number of solar panels and micro-turbines, battery capacity, and diesel generator size, with a constraint on the renewable penetration. Results indicate that load-mixing serves to reduce cost, and the

reduction is largest if the diesel backup is removed from the system.

**Keywords:** renewable energy system, load profile, PV, wind turbine, battery,

Electrical power is historically generated at a few large power stations and transmitted over long distances to end users. However, in recent years, there is an increase in decentralized or distributed electric power generation, where the power is produced and used at the same

**Renewable Energy Microgrid Design for Shared Loads**

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,

distribution, and reproduction in any medium, provided the original work is properly cited.

DOI: 10.5772/intechopen.75980

Ibrahim Aldaouab and Malcolm Daniels

Ibrahim Aldaouab and Malcolm Daniels

http://dx.doi.org/10.5772/intechopen.75980

**Abstract**

loads shared

**1.1. Distributed electricity generation**

**1. Introduction**

Additional information is available at the end of the chapter

Additional information is available at the end of the chapter

#### **Renewable Energy Microgrid Design for Shared Loads Renewable Energy Microgrid Design for Shared Loads**

DOI: 10.5772/intechopen.75980

Ibrahim Aldaouab and Malcolm Daniels Ibrahim Aldaouab and Malcolm Daniels

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.75980

#### **Abstract**

microgrids. We therefore would like to thank all the authors who participated in writing this

We would also like to thank the team at IntechOpen for their continuous support on this book, and special thanks go to Ms. Iva Lipović, the Publishing Process Manager, for her

**Prof. Majid Nayeripour and Prof. Eberhard Waffenschmidt**

Cologne University of Applied Science

**Assoc. Prof. Mostafa Kheshti** School of Electrical Engineering Shandong University, China

Cologne, Germany

book and sharing their latest findings to the readers and researchers.

VIII Preface

kind support and patience to coordinate the publishing process of this book.

Renewable energy resource (RER) energy systems are becoming more cost-effective and this work investigates the effect of shared load on the optimal sizing of a renewable energy resource (RER) microgrid. The RER system consists of solar panels, wind turbines, battery storage, and a backup diesel generator, and it is isolated from conventional grid power. The building contains a restaurant and 12 residential apartments. Historical meter readings and restaurant modeling represent the apartments and restaurant, respectively. Weather data determines hourly RER power, and a dispatching algorithm predicts power flows between system elements. A genetic algorithm approach minimizes total annual cost over the number of PV and turbines, battery capacity, and generator size, with a constraint on the renewable penetration. Results indicate that load-mixing serves to reduce cost, and the reduction is largest if the diesel backup is removed from the system. This cost is optimized with a combination of particle swarm optimization with genetic-algorithm approach minimizes total annual cost over the number of solar panels and micro-turbines, battery capacity, and diesel generator size, with a constraint on the renewable penetration. Results indicate that load-mixing serves to reduce cost, and the reduction is largest if the diesel backup is removed from the system.

**Keywords:** renewable energy system, load profile, PV, wind turbine, battery, loads shared

## **1. Introduction**

#### **1.1. Distributed electricity generation**

Electrical power is historically generated at a few large power stations and transmitted over long distances to end users. However, in recent years, there is an increase in decentralized or distributed electric power generation, where the power is produced and used at the same

> © 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

location [1]. Often, this decentralized power is produced with renewable energy technologies, such as wind and solar, due to the decreasing costs of these technologies [1].

**1.3. Problem statement**

**2. Building energy demand model**

from the 12 residential apartments.

**2.1. Historical residential demand**

Several studies consider the optimal design and sizing of the RER system for residential or commercial building individually [7, 8]. The RER microgrid design in this work is applied to a mixed commercial and residential building. Residential loads peak in the evening and early morning times, whereas commercial loads peak in the daytime. A shared residential and commercial load therefore has the potential to be more uniform than one or the other alone. A uniform load is easier to efficiently match to the RER supply, and it may also help lower the grid power costs. This chapter studies the effect of combining loads for an RER microgrid on the full cost of the microgrid. The size of the RER system partly depends on the shape of the load profile, such that irregular profiles require larger RER systems than smooth profiles. For example, an irregular load profile requires more energy storage to satisfy peak loads. The concept explored in this work is the benefit of combining different realistic load profiles in order to develop a total profile that is smoother and less costly to satisfy with an RER microgrid.

Renewable Energy Microgrid Design for Shared Loads http://dx.doi.org/10.5772/intechopen.75980 3

The residential load profile used for this work is generated from measured aggregate hourly consumption data for 12 apartments in a residential building in Columbus, Ohio [8]. The apartments are on the third floor of a three-story building, which means that they will have higher heating loads in the winter and cooling loads in the summer. This choice represents a worst-case scenario in terms of the peaks in the residential load. One year of hourly metered power use for these apartments is available, starting at 12 am on Sunday, June 9, 2013. These apartments use electricity for hot water, heating, and cooling. The hourly commercial load profile is synthesized from typical load profiles for commercial kitchens [9]. The average demand from the commercial load is selected to be nearly the same as the average demand

**Figure 1** illustrates the weekly average for the aggregate residential load data. Each day of the week, there is a peak in the morning at about 8 am, representing the electricity consumption as residents prepare for the workday. At the end of the day, at about 8 pm, there is a larger

To determine the temperature-dependent component of the residential data, a piecewise linear regression is used. Each week, aggregate residential consumption data are averaged to create 52 single values. The same is done for the temperature. **Figure 2** shows a plot of the average weekly power versus average weekly outdoor temperature. The fit shown in **Figure 2** has five parameters as follows: a heating slope (HS), cooling slope (CS), heating temperature (HT), cooling temperature (CT), and baseline (B) [8]. The baseline component defines the hourly expected weather independent demand. The heating and cooling slopes KW per Fahrenheit degree (kW/°F) enable respective prediction of the hourly heating and cooling demand for a typical weather year.

peak as residents return home for dinner and other electricity-consuming activities.

There are many advantages to a power distribution system that relies on many small generation facilities rather than a few large power plants. Transmitting power over long distances is inefficient and requires expensive infrastructure. Smaller facilities that are close to where the power is used can provide higher quality power, with fewer blackouts and a more steady voltage. Since many of the small generating stations are natural gas powered or powered with renewable sources, there is less pollution than large plants that often run on coal. Finally, a distributed energy generation model is more secure than a centralized model.

The challenge to expanding distributed electricity generation is partly economic and partly technical. The capital costs for building a small-scale facility is large relative to the power it can produce. Many renewable technologies produce irregular power that varies with the weather, and this power is difficult to incorporate into the grid [2].

#### **1.2. Motivation for expanding distributed generation**

Increasing attention is given to designs for integrating renewable energy with traditional power to satisfy electrical loads from individual or multiple buildings. Research in this area is driven by several factors. First, costs for photovoltaic (PV) panels and wind micro-turbines (MT) are steadily dropping, along with battery energy storage systems (BESS). For example, the reported cost of installed solar PV systems fell by an average of 6–12% per year from 1998 to 2014, depending on the scale of the system [3]. Similarly, the price of wind power is dropping significantly, as more turbines are brought online, and currently about 5% of the energy requirements for the United States is supplied through wind power. In the previous decade, more than twothirds of all wind installations in the United States have been small- or mid-sized wind turbines [4]. Battery storage has also dropped to a level of about \$100 per kWh of capacity [5]. Another motivating factor is the various grid-pricing structures available, which often create an incentive for using less power at certain times of the day or making the overall electrical demand more uniform. Microgrids can achieve this, reducing the cost of grid electrical power.

In addition to economic motivation, there is increased recognition of the damage to the environment due to CO<sup>2</sup> emission from traditional power generation. The benefit of using RER microgrids for buildings is the potential reduction in carbon and other pollutants because buildings consume over 40% of end-use energy worldwide [6]. In order to address the problem of efficient building energy use and to reduce pollution in buildings, the United States has set a zero net energy target on 50% of commercial buildings by 2040 and on all commercial buildings by 2050 [6]. A zero net energy building is one that produces some renewable energy on-site, such that the building sometimes uses grid power and at other times produces extra renewable power. The average amount of renewable power produced annually is the same as the building's average annual consumption.

Finally, another advantage for microgrids is the improved reliability that they offer. There are multiple sources of power in a microgrid, such that there is less chance of a complete power outage.

#### **1.3. Problem statement**

location [1]. Often, this decentralized power is produced with renewable energy technologies,

There are many advantages to a power distribution system that relies on many small generation facilities rather than a few large power plants. Transmitting power over long distances is inefficient and requires expensive infrastructure. Smaller facilities that are close to where the power is used can provide higher quality power, with fewer blackouts and a more steady voltage. Since many of the small generating stations are natural gas powered or powered with renewable sources, there is less pollution than large plants that often run on coal. Finally, a

The challenge to expanding distributed electricity generation is partly economic and partly technical. The capital costs for building a small-scale facility is large relative to the power it can produce. Many renewable technologies produce irregular power that varies with the

Increasing attention is given to designs for integrating renewable energy with traditional power to satisfy electrical loads from individual or multiple buildings. Research in this area is driven by several factors. First, costs for photovoltaic (PV) panels and wind micro-turbines (MT) are steadily dropping, along with battery energy storage systems (BESS). For example, the reported cost of installed solar PV systems fell by an average of 6–12% per year from 1998 to 2014, depending on the scale of the system [3]. Similarly, the price of wind power is dropping significantly, as more turbines are brought online, and currently about 5% of the energy requirements for the United States is supplied through wind power. In the previous decade, more than twothirds of all wind installations in the United States have been small- or mid-sized wind turbines [4]. Battery storage has also dropped to a level of about \$100 per kWh of capacity [5]. Another motivating factor is the various grid-pricing structures available, which often create an incentive for using less power at certain times of the day or making the overall electrical demand

more uniform. Microgrids can achieve this, reducing the cost of grid electrical power.

In addition to economic motivation, there is increased recognition of the damage to the envi-

microgrids for buildings is the potential reduction in carbon and other pollutants because buildings consume over 40% of end-use energy worldwide [6]. In order to address the problem of efficient building energy use and to reduce pollution in buildings, the United States has set a zero net energy target on 50% of commercial buildings by 2040 and on all commercial buildings by 2050 [6]. A zero net energy building is one that produces some renewable energy on-site, such that the building sometimes uses grid power and at other times produces extra renewable power. The average amount of renewable power produced annually is the same as

Finally, another advantage for microgrids is the improved reliability that they offer. There are multiple sources of power in a microgrid, such that there is less chance of a complete power

emission from traditional power generation. The benefit of using RER

such as wind and solar, due to the decreasing costs of these technologies [1].

distributed energy generation model is more secure than a centralized model.

weather, and this power is difficult to incorporate into the grid [2].

**1.2. Motivation for expanding distributed generation**

ronment due to CO<sup>2</sup>

2 Smart Microgrids

outage.

the building's average annual consumption.

Several studies consider the optimal design and sizing of the RER system for residential or commercial building individually [7, 8]. The RER microgrid design in this work is applied to a mixed commercial and residential building. Residential loads peak in the evening and early morning times, whereas commercial loads peak in the daytime. A shared residential and commercial load therefore has the potential to be more uniform than one or the other alone. A uniform load is easier to efficiently match to the RER supply, and it may also help lower the grid power costs. This chapter studies the effect of combining loads for an RER microgrid on the full cost of the microgrid. The size of the RER system partly depends on the shape of the load profile, such that irregular profiles require larger RER systems than smooth profiles. For example, an irregular load profile requires more energy storage to satisfy peak loads. The concept explored in this work is the benefit of combining different realistic load profiles in order to develop a total profile that is smoother and less costly to satisfy with an RER microgrid.
