Smart Energy Grid Engineering

By Hossam Gabbar

Smart Energy Grid Engineering provides in-depth detail on the various important engineering challenges of smart energy grid design and operation by focusing on advanced methods and practices for designing different components and their integration within the grid. Governments around the world are investing heavily in smart energy grids to ensure optimum energy use and supply, enable better planning for outage responses and recovery, and facilitate the integration of heterogeneous technologies such as renewable energy systems, electrical vehicle networks, and smart homes around the grid.

By looking at case studies and best practices that illustrate how to implement smart energy grid infrastructures and analyze the technical details involved in tackling emerging challenges, this valuable reference considers the important engineering aspects of design and implementation, energy generation, utilization and energy conservation, intelligent control and monitoring data analysis security, and asset integrity.

• Includes detailed support to integrate systems for smart grid infrastructures
• Features global case studies outlining design components and their integration within the grid
• Provides examples and best practices from industry that will assist in the migration to smart grids

• Language: English
• Publisher: Academic Press
• Release date: Oct 12, 2016
• ISBN: 9780128092323

Hossam Gabbar

Hossam A.Gabbar, PhD, Professor, Director of Energy Safety and Control Lab, Faculty of Energy Systems and Nuclear Science, University of Ontario Institute of Technology, Ontario. Dr. A. Gabbar is the author of 210 publications in the area of smart energy grids, safety, protection, and control, and has been a speaker in national and international events in smart energy grids, and general chair of the annual IEEE Conference on Smart Energy Grid Engineering (SEGE).

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Chapter 1

Introduction

H.A. Gabbar    University of Ontario Institute of Technology (UOIT), Oshawa, ON, Canada

Abstract

Smart energy grid (SEG) infrastructures will provide efficient bidirectional energy supply with reduced greenhouse gas emissions. SEG includes distributed energy generation (DEG) capabilities, such as thermal, electricity, and gas, with smart components, which will allow scalable architecture as integrated with interconnected micro energy grids (MEGs). In order to facilitate the design and implementation of such dynamic superstructures with local and global performance optimization, it is important to provide an intelligent modeling and simulation environment with distributed decision-making capabilities to manage data gathering and analysis functions and map to static and dynamic models of SEG components. This will support the design, control, and operation of SEG in a distributed and coordinated way, along with the integrated intelligent applications and data analytics. As SEG is a multiplayer domain, this requires collaborative decision making and control with a distributed knowledge base to support different views (eg, utilities, original equipment manufacturer, hybrid electric vehicle, consumers). This will support the automatic and dynamic identification and evaluation of control and protection boundaries and operational scenarios of MEGs with renewable DEG nodes. In addition, it supports the design and operation of SEG technological architectures as integrated within practical business models related to practical implementation of different components within the SEG architecture. The proposed intelligent and distributed modeling and simulation environment will provide functionalities to support these views. To facilitate the engineering design and operation of SEGs, energy semantic network is introduced to model all layers in SEG infrastructures. The proposed solution will enable and support the practical implementation of SEGs with proven low carbon and improved efficiency and reliability of energy supply infrastructures.

Keywords

Smart energy grid engineering (SEGE); Smart energy grid (SEG); Micro energy grid (MEG)

1.1 Introduction

Recently, the world has been witnessing an increased demand in electricity and energy. It is clear that energy use has a direct negative impact on climate changes, which provoked governments and industries to invest more in renewable energy (RE) technologies as alternative energy sources and power supply systems. There are increasing tendencies to utilize renewable green energy sources (solar, photovoltaic (PV), wave, tidal, fuel cell, biogas, and hydrogen), which are supported by economic and environmental factors. The increasing reliance on fossil fuels with an increasing rate of resource depletion is causing a basic shift to green RE alternatives, clean fuel replacement, and energy displacement of conventional fossil energy sources to new green renewable, environmentally safe and friendly counterparts [1,2]. PV and distributed energy resources (DERs) generation schemes are considered the most viable and economic choices for microgrid (MG) electrical energy generation [3]. The focus is on small isolated standalone integrated AC-DC grid power systems that utilize a combination of PV, fuel cell, and a diesel generator with a local grid backup with a capacity usually ranging in size from 15 to 1500 kW [4,5]. Typical applications include electricity supply to remote isolated islands/villages with a limited utility grid, heating, water pumping, and ventilation and air conditioning systems. Diesel generators are the backup source of electricity in most remote cases.

As part of the future implementation of smart energy grid (SEG), it is important to demonstrate regional SEGs and micro energy grids (MEGs) with RE as distributed generation. This decision involves capital cost for changing infrastructure to support the target dynamic and scalable SEG. In order to achieve such a target, it is essential to provide a modeling and simulation environment to understand existing grid structures and support the design and evaluation of the target SEG. There is no current modeling and simulation technology that can support the dynamic MGs with distributed generation structures as mapped to a geographic information system (GIS), which created a bottleneck to move forward in the design and implementation of true SEGs. In addition, there is no technology to support the evaluation of different grid applications in different scales within the grid, such as hybrid-electric vehicle. Moreover, most of the design and operation decisions should be based on safety and reliability of grid physical structures; however, current modeling and simulation technologies don’t support asset integrity models as linked with power/energy modeling, which created limitations to evaluate different design and operational scenarios in terms of safety, reliability, and availability. In addition, most of the current modeling tools don’t provide data analysis features that are needed to tune SEG physical structure models with dynamic simulation capabilities. This will lead to limitations on performing accurate optimization of grid performance.

The planning of resilient SEGs can be achieved via automated and intelligent monitoring, planning, and distributed modeling and simulation to support the design and operation of SEG. The solution will support real-time modeling and simulation of the superstructure of SEG in two levels: top-level grid model and low-level component level. A distributed data management module will be developed to construct and tune the following SEG models: grid superstructure physical and asset models; power/energy models; grid asset integrity and reliability models; safety and protection models; and operation and control models.

The proposed technology will support the following business functions: multisensor intelligent metering design support, distributed control and operation support, dynamic peak analysis and optimization, distributed energy generation (DEG) planning and optimization, asset degradation and upgrade scenario synthesis and verification, and design and implement security components in the component levels.

Technological features will be proposed to automatic and dynamic identification of practical distribution and partitioning strategies of MGs with the ability to define operational and alternative scenarios based on performance indicators and protection strategies. The target application will be a web-based interface with a simulation engine and distributed collaborative supply chain simulation models to allow local and global optimization for different SEG performance indicator parameters for design and operation scenarios. User interface will be accessible via cell phones to enable mobile access from remote locations to identify optimal ways to support energy supply and use within SEGs. Deterministic and predictive SEG models will be developed and associated with static and dynamic parameters of physical SEG superstructure and represented as energy semantic network (ESNs), which will be used to synthesize practical and optimal scenarios for MG generation, protection, and application integration. The proposed modeling and simulation will be mapped to GIS for geographical and environmental data analysis, and to monitor and analyze real-time risk index calculation with different energy supply chain scenarios.

There are several scenarios that can be adopted to analyze energy efficiency related to the DERs within MGs with different thermal-gas-electricity conversion and utilization strategies to cover regional energy needs. These strategies include (a) MG design and control; (b) thermal storage systems; (c) natural gas (NG) applications for transportation and facilities; (d) gas-electricity conversion technologies; and (e) energy conservation within MGs. Researchers, technology providers, as well as industries have been studying effective technologies in each of these tracks. Investors have also been pushing to deploy several technologies for hydrogen production and conversion so that it can be used as clean and cheap fuel along with NG to compensate the challenges of electric vehicle deployment for regional clean transportation.

The analysis of energy efficiency of the integration of MGs with existing energy infrastructures is most likely to impact operation, in terms of control, protection, energy quality, and sustainability. Fig. 1 shows an integrated view of SEGs where DERs are integrated in each stage in the interconnected processes from sources, transportation, treatment, conversion, generation, storage, and utilization. There are many challenges to facilitate the proper and smooth implementation of MGs and their integration with grid topologies, as islanded and grid-connected modes. This requires detailed investigation of the different integration scenarios of thermal-gas-electricity networks and technologies to ensure optimal energy savings and efficiency, along with minimizing lifecycle costs (LCCs). There are several scenarios to implement fully integrated thermal-gas-electricity grids, and demonstrate local distributed energy systems within MGs, which requires proper analysis of the different energy supply and production (ie, generation) scenarios based on lifecycle assessment (LCA) and cost analysis [6], as well as energy policies and regulations of thermal-gas-electricity networks and their operation [7]. This will also require conducting detailed analysis of the design and operational challenges of building interconnected MGs to ensure their optimal performance with clear determination of potential and efficient protection and control systems in view of existing grid infrastructures, energy resources, and the current forecasted demand in the local region [8,9].

Fig. 1 Energy (micro) grid engineering.

The huge investment of upgrading transmission lines or distribution lines motivated governments to invest in monitoring energy efficiency and power losses in all transmission and distribution lines, as well as MG infrastructures. The proposed DER will support SEG infrastructures that include thermal-gas-electricity networks, and can provide the most efficient energy supply to meet local or regional demands. In addition, an energy data center will support the evaluation of design, control, and operation scenarios of these integrated thermal-gas-electricity networks. It will also support the optimization of smart grids’ and MGs’ process parameters to achieve the most economical and potential solution to cover regional energy demands.

Energy data centers will include low- or medium-energy supply components, for example, thermal or voltage, as part of the energy distribution networks. Energy-efficiency parameters will be defined and analyzed for DERs connected at the distribution level, which will reflect energy supply system stability, reliability, energy quality, sustainability factors, LCC, and environmental factors. Preliminary studies at the Energy Safety and Control Laboratory, University of Ontario Institute of Technology completed the identification and modeling of energy efficiency and key performance indicators (KPIs) for all layers of MGs. In addition, initial infrastructure of energy data center is designed and well described and ready for implementations.

Energy efficiency will cover smart grids and MGs, such as: (a) an MG design based on available energy resources, existing grid topology, and load and energy demand forecasting; (b) dynamic protection with adequate risk-based fault propagation analysis in view of the dynamic nature of MGs in islanded and grid-connected modes; (c) energy supply quality and performance maximization in view of different operation parameters; (d) cost-benefit analysis for different energy technologies with realistic profit planning; and (e) considerations of international standards and national regulations for energy-efficiency limits and targets with multiview analysis based on detailed business models. In order to provide logical justification of building interconnected MGs, modeling and simulation tools are required, as explained in the following section.

1.2 SEGs infrastructures

Typically, an electricity grid includes transmission and distribution lines, where transformers are used to integrate them with loads in commercial, residential, and transportation levels.

SEGs can be modeled hierarchically in different regions and zones, which can include interconnected MEGs. A regional zone is a combination of a few subregional zones, and the subregional zones are the summation of cells. MEGs have been considered inside cells as shown in Fig. 2.

Fig. 2 Electricity grids.

In order to achieve adaptive SEG infrastructures, interconnected MEGs are proposed to dynamically transfer energy between MEGs and transmission and distribution lines in view of loads, demand, and other performance indicators, such as price. Fig. 3 shows the proposed superstructure of SEGs with interconnected MEGs. To upgrade the traditional electric power system to an SEG, it is essential to make several enhancements at various levels of operation and control infrastructure, which includes the integration of intelligent electronic devices, synchrophasor-based devices, advanced communication infrastructure, and efficient monitoring and control algorithms that would make optimal use of these devices; however, before deploying smart devices and intelligent systems in the actual power grid, it is important to test and validate their capabilities and functionality, as well as their accuracy. This will ensure the reliability and accuracy of these SEG technologies under different operating scenarios.

Fig. 3 SEG with transmission, distribution, cell, and interconnected MEGs.

Several testbeds of SEG demonstrations have been reported, such as in Ref. [4], where state-of-the-art research facilities are used for testing and validating SEG deployment capabilities. This includes SEG devices, such as phasor measurement units (PMUs), which are tested under different operating conditions. In addition, it is important to test the interoperability of different hardware devices for energy/power system automation. The proposed SEG platform will be used to demonstrate interconnected MEGs and distribution network with distributed control systems.

1.3 Micro energy grid

There has been a global movement in the direction of adoption and deployment of distributed and renewable resources. RE sources provide clean energy supply alternatives that differ from conventional fossil fuel sources. RE has dynamic behavior that cannot be scheduled. On the other hand, RE can provide relatively smaller energy supply capacities, which is smaller than conventional energy generation from typical power stations. RE is often connected to the electricity distribution system rather than the transmission system. The integration of such time-variable distributed or embedded sources into electricity network requires special consideration. Due to the ever-increasing demand for high-quality and reliable electric power, the concept of DERs has attracted widespread attention in recent years [1]. DERs consist of relatively small-scale generation and energy storage devices that interface with low- or medium-voltage distribution networks and can offset the local power consumption, or even export power to the upstream network if their generation surpasses the local consumption. One philosophy of operation, which is expected to enhance the utilization of DERs, is known as the MG concept [2,3]. MGs should widely utilize renewable energy resources (RERs) such as wind, solar, and hydrogen, to play a significant role in the electric power systems of the future, for cleaner air, reduced transmission and distribution costs, and incorporation of energy-efficiency enhancement initiatives. In addition, using energy storage devices such as batteries, energy capacitors, flywheels, and controllable loads. MGs including DGs will provide flexible and economic management of energy to meet local demands [4,5]. From a customer's point of view, MGs similar to traditional local voltage distribution networks not only provide their thermal and electricity needs, but in addition, they enhance local reliability, reduce emissions, improve power quality by supporting voltage and reducing voltage dips, and lead to lower costs of energy supply [10]. Much research has been performed to optimize the operation, load dispatch, and management of the energy storage system of the MGs. Particle swarm optimization method accordingly is employed in Ref. [11] to minimize the cost of MGs with controllable loads and battery storage. This is done by selling the stored energy at high prices to shave peak loads of the larger system. Linear programming algorithm is used in Refs. [12–14] to optimize MG operation cost and battery charge states. Maximizing benefits owing to the energy pricing differences between on-peak and off-peak periods are obtained by electrical and thermal storage charge scheduling in Refs. [15,16].

The important drawback of the above study is that it does not consider all uncertainties of the problem. Although employing RERs obviates environmental concerns and fossil fuel consumption, they introduce uncertain and fluctuated power because of the stochastic wind and solar variation [17]. In addition, this open-access power market and diverse commercial, residential and industrial consumer types, daily random nature of load demand [18]. Moreover, in an open-access power market, the degree of uncertainty of the load forecast error and market price can be even more perceptible. Engineers require computational methods that could provide solutions less sensitive to the environmental effects. So the techniques should take the uncertainty into account to control and minimize the risk associated with design and operation [19]. In order to consider uncertainty in optimal energy management planning of MGs effectively, the optimization problem should be solved for a suitable range of each uncertain input variables instead of just one estimated point. Using a deterministic optimization problem, a large computational burden is required to consider every possible and probable combination of uncertain input variables. Hence, from the system planning point of view, it turns out to be convenient to approach the problem of energy management planning of the MGs as a probabilistic problem. This leads to the problem known as energy management planning under uncertainty, where the output variable of an MEG objective function obtained as a random variable, and thus, it becomes easy to identify the possible ranges of the total operating cost.

There are several techniques to deal with problems under uncertainty. The three main approaches are analytical, simulation (Monte Carlo simulated), and approximate methods [20]. The vast majority of techniques have been analytically based, and simulation techniques have taken a minor role in specialized applications. All the proposed solutions for energy management of MEGs dealt with the MEG as a micropower grid comprised of electric supplies and loads and not as an MEG, in spite of existence of thermal and fuel supplies and loads.

This project is aiming at the development of a support tool for the design and operation of MEGs with the main objective of improving energy efficiency with conservation strategies in communities, commercial and residential buildings, industrial facilities, and transportation. This will support the deployment of high-performance energy supply grids with more penetration of NG applications such as boilers, NG vehicle, and NG-fuel cells. In addition, it will enable the penetration of high-performance solar systems and their applications in water and air heating, and cogeneration. The proposed project will include sensor network infrastructure to monitor and control MEGs and loads in efficient ways.

The team successfully developed energy modeling and a simulation environment with a knowledge base for energy conservation strategy evaluation as integrated within SEGs. In the new proposal, we are focussing on supporting energy and engineering consulting companies to evaluate the design and support the operation of MEGs in buildings, facilities, communities, and transportation infrastructures. In addition, we are presenting potential strategies to increase the deployment of NG applications in infrastructures worldwide through NG-fuel cell, NG-boilers, NG-vehicles.

The main objective of the research is to design an optimization tool for MEG that is able to analyze, configure, simulate, and evaluate an optimal combination of available energy resources. The design tool allows different disciplinary expertise (eg, geographical, weather, and energy) to collaborate and share knowledge. MEG designer selects, modifies, and/or adds a cost function that matches the desired objective of the MEG. The design tool uses both the collected knowledge and the energy demand requirements to develop an optimal energy configuration that minimizes predefined cost function. In addition, the tool provides reported analyses and evaluation for an optimal MEG. The objective can be achieved by the following:

1. Business modeling and requirement analysis of implementation of MEG using IDEF0 and gap analysis, based on local demand dynamic profiles, DER dynamic profiles, business, technical, and regulation constraints, and target performance criteria; support the lifecycle engineering of MEG design and operation.

2. Automatic synthesizing of ESN of the target MEG with appropriate model representation techniques (utilizing feature extraction) of different energy resources and loads. The archive allows a clear view of the performance of different energy resources and loads for matching purposes of increasing MEG efficiency and reducing its dependency on energy storage components.

3. Performing a comprehensive study of energy storage technologies available in the market. While it is a fact that energy storage technologies offer wide varieties of storage types, the questions of what, how, where, and when storage can be implemented are highly dependent on the MEG design structure, load profile, and cost, and the dynamic behavior of the storage component itself.

4. Generating element modeling of MEG components based on the structure and type of each element.

5. Designing a knowledge base ESN system for MEG.

(a) Designing a database mechanism to accept geographical data, weather data, and other data concerning possible supply of energy resources, such as NG and fossil fuel.

(b) Designing a dynamic inference engine that is able to add and modify energy resources rules.

(c) Developing a fuzzy inference mechanism to allow reasoning and uncertainty about both analytic and black-box models.

(d) Integrating artificial neural network (ANN) to allow dynamic nonlinear mapping between ESN nodes. The ANN allows a nonlinear relation between class nodes of ESN (recovering limited alternatives logical relations). In addition, ANN reinforces the related relations while weakening undesired relations between class nodes. Hence, a reduced effective energy scenario can be generated, allowing reasonable and efficient computational burden.

(e) ESN generating of all possible with most effective design parameters for the desired MEG with their dynamic range forming the possible scenarios of the energy resources, storage elements, dynamic connection between sources and loads, and load-source scheduling. In addition, the developed ESN will generate the most affected KPIs by the design parameter to reduce the computational