Elsevier

Applied Energy

Volume 221, 1 July 2018, Pages 535-556
Applied Energy

Computational tools for design, analysis, and management of residential energy systems

https://doi.org/10.1016/j.apenergy.2018.03.111Get rights and content

Highlights

  • This paper thoroughly reviews computational tools for design, analysis, and management of residential energy systems.

  • The tools are analyzed based on the conventional and CEN-CENELEC-ETSI Smart Grid Reference Architecture.

  • Tools’ availability, sources, typical applications, strengths, and limitations are discussed in detail.

  • Necessary information is provided to help researchers to choose the right tools to meet specific objectives.

Abstract

Selecting an appropriate software tool for a particular energy-management system is a challenging task as there is little information available and several software tools to pick from. Each tool has its own strengths and limitations, and making a right choice is critical for accurate and feasible analyses. This paper reviews more than one hundred simulation software packages that are useful for residential-energy-management system analysis. The tools are analyzed based on the conventional and CEN-CENELEC-ETSI Smart Grid Reference Architecture. Additionally, tools’ availability, sources, typical applications, strengths, and limitations are discussed. A case study on residential energy management and optimization is carried out to show the strengths and limitations of a certain computational tool. It is observed that none of the tools cover all the applications of residential energy systems, however necessary information is provided to help researchers to choose the right tools to meet specific objectives.

Introduction

Power generation, transmission, distribution, and the management of current power systems has been experiencing a significant change in recent years. Both aggregated and non-aggregated renewable-energy based power generators are replacing the bulky thermal power stations. At the same time transmission and distribution systems are becoming more complex, integrating distributed energy sources and grid-connected micro grids. Various non-conventional loads and sources like electric vehicles (EVs), and battery energy-storage systems (BESSs) are becoming a common part of the grid [1], [2], [3], [4]. The future grid, alternatively known as the internet of energy (IoE), will facilitate plug-and-play features to integrate small-scale energy sources [4]. IoE customers, alternatively known as prosumers, will play the role of consumers and sellers simultaneously. Prosumers will utilize off-peak hours to store energy and sell it back during peak-load hours to optimize the electricity cost.

To get the full advantage of the energy devices and their plug-and-play capability, a robust energy-management system is necessary. Otherwise, uncontrolled loads and distributed energy sources may introduce power-quality degradation and instability to the grid. As power systems are becoming more disaggregated, it is important to manage the grid from the customers’ premises. For example, customers in a high-rise building have a higher energy demand, which can be minimized by introducing building-attached renewable sources with battery storages, thereby optimizing the power consumption. Any effective energy management at the consumer point has a benefit for both consumers and the utility. Therefore, researches in residential energy systems, their economy, management and optimization are getting significant for power systems.

In residential-energy-systems research, the most common areas are load modeling and control, renewable energy integration, energy optimization, energy cost analysis, power quality analysis, home-to-grid (H2G) bidirectional energy transfer analysis, and electrical protection systems analysis. It is important to analyze the energy modeling and management concept before its real implementation. However, the plethora of simulation software in the past decade has made it difficult for researchers to choose a particular tool appropriate to their research objective. Many researchers have reviewed the use of computational tools for various applications. These broadly cover the following: integration of renewable energy into various energy systems [1], integration of EV to grid [2], [3], design and analysis of power systems [4], and hybrid renewable-energy systems [5]. However, none of the research focused on the tools related to residential energy systems. Residential energy management plays a pivotal role in power quality management, power regulation, load management, and grid efficiency enhancement, in power systems.

Therefore, this paper provides an overview of the computational tools for the design, analysis, and management of residential energy systems. All the tools are systemically categorized based on their applications, functional capability, strengths, and limitations. A case study covering the overall areas of residential energy management has been carried out to show the strengths and limitations of a certain simulation tool. In this case study, the residential load is managed using battery-energy-storage systems and electric vehicles (EVs). The charging-discharging of the battery and EVs is controlled based on the load and the photovoltaic (PV) power generation to reduce the grid power demand. The pre-heating and pre-cooling demand is managed using the PV power, and the thermal-comfort satisfaction and dis-satisfaction index is analyzed based on that energy management. Additionally, the overall building design is optimized to enhance the energy efficiency. In this case study, it is observed that residential energy management is a wide area covering electrical loads, energy sources, renewable integration, HVAC, and the architectural design, and all these factors’ effects should be considered for efficient energy management. However, none of the tools covers all these areas, as each tool has its own strengths and limitations. Therefore, in this paper, all the tools are categorized and analyzed based on their availability, applications, strengths, and limitations on the basis of both traditional and future smart-grid architecture. The goal of the paper is to help researchers to an easy selection of a specific tool appropriate to their research objective in the residential energy management domain.

Section snippets

List of residential energy systems tools

The simulation tools that are related to residential energy systems for modeling and analysis, optimization, management, economic analysis, and renewable energy integration are listed in Table 1, Table 2, Table 3, Table 4, Table 5. Although the tools cover a wide range of applications, they are listed in separate tables because of their strength in a certain area. Each table is further segmented into tools’ names, sources, availability, and common applications. Typical applications include load

Distributed energy resources integration to home

It is expected that the future smart grid, or internet of energy (IoE) [166], will be more automated and disaggregated by including small-scale energy devices. Users will be known as prosumers where they will connect energy devices through a plug-and-play facility, and transfer energy in a bidirectional way. Customers will use various energy storages and renewable sources to reduce their dependency on the grid. In this section various renewable-energy integration to the grid are discussed.

Description of simulation tools

Some selected simulation tools from Table 1, Table 2, Table 3, Table 4, Table 5, based on their wide range of applications in the residential energy systems are described in this section. Each description contains tool’s features, applications, and internal functionalities.

Case studies

In this section, a case study has been carried out to manage residential energy systems. From the tools listed in Table 1, Table 2, Table 3, Table 4, Table 5, GridLAB-D is selected for the energy-management simulation, as it is open-source software and has a wider range of applications in residential energy systems.

Conclusion

This paper provides an insight on the available software tools for conducting studies on residential energy-management systems. The existing software tools are divided into several groups based on their suitability for different types of applications. The aim is to assist users to easily select the right software tool for a specific application. The strengths and limitations of each tool are discussed in detail so that users are aware of the types of analyses that can be performed. A case study

Acknowledgement

Authors are thankful to Dr. Keith Imrie for the proofreading.

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