|Fig. 1: An example electric grid diagram modeled after current European Systems (Source: Wikimedia Commons )|
The "Smart Grid" is a phrase that has been mentioned quite a lot in recent years, but it really is an umbrella term that people use to refer to a wide variety of concepts surrounding the general idea of modernizing existing electric grids. In general, an electric grid consists of a large network of transmission lines and transformers that transfer power from large power plants to individual residences or buildings. As Fig. 1 shows, with the increased initiatives to integrate alternative renewable energy resources with coal driven plants, most electric grids in developed countries have become incredibly complex, distributed pieces of infrastructure.
As a result, there is an increased need for automated and digital management of this system. This is where the Smart Grid comes in to play. According to the United States SmartGrid government initiative, what makes the grid smart is "the digital technology that allows for two-way communication between the utility and its customers, and the sensing along the transmission lines".  The primary goals of this initiative are also laid out as the following:
More efficient transmission of electricity
Quicker restoration of electricity after power disturbances
Reduced operations and management costs for utilities, and ultimately lower power costs for consumers
Reduced peak demand, which will also help lower electricity rates
Increased integration of large-scale renewable energy systems
Better integration of customer-owner power generation systems, including renewable energy systems
Before getting into the issues identified above, we must outline the basic technology and tools that have been the basis for most Smart Grid efforts. According to the 2014 US Smart Grid System Report, these technologies can be broken down into four main categories.  The first is advanced metering infrastructure (AMI) which includes all smart meters, information management systems, and communication networking. The next is customer facing technology that includes things like programmable thermostats etc. The third category of technology includes sensing and control programs that generally integrate with field devices in large distributed systems. Finally, there are a set of advanced sensors and high-speed communication technologies that are used specifically to optimize transmission power networks. These four areas of technology all address pieces of the problems/goals identified in the previous section as we will discuss further now.
|Fig. 2: "Duck curve" showing time dependent electricity loads and the relation to renewable energy sources such as wind and solar.  (Source: Wikimedia Commons)|
Using the three points identified in the first section as a framework for analysis, we focus primarily on Smart Grid efforts in the United States, but similar efforts are being made in many other developed countries. The first issue often mentioned when it comes to the Smart Grid is the goal of creating more efficient transmission of electricity. In general, the question of efficiency can be broken down into two parts: supply-side and demand- side. Improving efficient transmission, addresses the supply-side piece. Current electric grids have many places where transmission losses are an issue. The reasons for these losses are actually quite technical and require and understanding of the physics of power transmission. At a high level, the promising methods for reducing transmission losses include the following. First, is the idea of system reconfiguration which involves the real-time transferring of loads and unloading of heavily-loaded lines using controllable sectionalizers in the transmission network and sensors for monitoring the loads.  A theoretical analysis of complex algorithms for doing this reconfiguration has shown that this kind of approach can possibly lead to 54%-58% loss reduction.  Another approach to increasing efficiency is conservation voltage reduction, which involves using automated distributed algorithms to optimize network voltage levels to be at the lowest acceptable feeder voltage levels (reducing power losses).  It has been shown in practice that CVR can help reduce line losses by an additional 3% nationally.  Most of these efforts to reduce losses and increase efficiency are captured by the third and fourth categories of technology mentioned in the previous paragraph.
Now, the arguably most important piece of most Smart Grid efforts is the reduction of peak demand. Much of the work in this area involves using AMI infrastructure and two-way communication with customers to modulate demand response. A great example of this can be seen in a case study performed by Honeywell in California. The company implemented a system that involved a network of energy management systems which could implement pre-determined customer directives in response to received signals (such as peak energy warnings) from an automated demand response controller.  This system helped customers respond effectively to cost fluctuations during high demand times and actually was able to reduce the average demand for a given customer (averaged across all customers) by ~4500 kW at the peak time. This study and also others undertaken by the U.S. Department of Energy show that use of smart technology can greatly help with this problem of reducing peak demand.
Stemming from this discussion, there is a natural connection to the final issue of managing the integration of renewable energy resources with the current electrical grid. The reason this issue is closely related to that of reducing peak demand is highlighted in Figure 2. Because renewable energy resources like wind and solar are extremely variable in production, there is an inherent imbalance between peak demand and the peak production of these renewables. As discussed in a White House report on the integration of variable energy resources (VERs), many efforts to help integrate VERs into the current grid have to do with the reduction of peak demand and the flattening of the "duck curve" in the hopes building better demand response programs. In combination with the improved demand response, the goal is to improve energy storage so that the storage of VERs can be integrated with smart grid networking technologies to support the demand response.  The studies referred to in this report seem to suggest that there is quite a lot of work being done in this area. Specifically, controlled case-studies have indicated that this is a promising application for "smart" technologies.
Between 2010 and 2013, the United States alone has spent $18 billion dollars on the implementation of smart grid technologies in the current electrical grid (ranging from AMI to sensors and control systems for monitoring transmission and performing load balancing).  It is clear that there is a large amount of money and force behind this initiative. The question to be debated is: are these efforts well-founded or misplaced? No one can deny the fact that the electrical grid is in great need of revamping. It is also relatively clear from the existing data that technology for reducing losses on the supply side (in transmission lines etc.) is improving and will be valuable. However, the issue of whether the Smart Grid can really change demand curves and balance electrical loads effectively at scale is still in question. The Honeywell study is a promising case study, but scaling this kind of work to the entire country effectively is not an easy task.  In addition, the complicating factor of integrating renewable energy sources makes this harder. Much of the work in how to optimize an electrical grid's efficiency and distribution with many different energy sources is still in an academic phase, and while there are promising approaches being published, it remains unclear how quickly this work can make it into the real electric grid system. 
Nevertheless, as the energy system is invariably becoming more and more distributed, the necessity for better control systems, sensing, and efficiency within the electrical grid becomes greater. Therefore, discussion about the issues presented in this article must be a priority in the coming years as it will be vital to help build a sustainable system for energy distribution in the future.
© Nikhil Parthasarathy. The author grants permission to copy, distribute and display this work in unaltered form, with attribution to the author, for noncommercial purposes only. All other rights, including commercial rights, are reserved to the author.
 E. Turban et al., Electronic Commerce: A Managerial and Social Networks Perspective, 8th Ed. (Springer, 2015), pp. 287.
 "2014 Smart Grid System Report," U.S. Department of Energy, August 2014.
 R. Jackson et al., "Opportunities for Energy Efficiency Improvements in the U.S. Electricity Transmission and Distribution System," Oak Ridge National Laboratory, ORNL/TM-2015/5, April 2015.
 N. Rugthaicharoencheep, and S. Sirisumrannukul, "Feeder Reconfiguration for Loss Reduction in Distribution System with Distributed Generators by Tabu Search," GMSARN International Journal 3, 47 (2009).
 K. Schneider et al., "Evaluation of Conservation Voltage Reduction (CVR) on a National Level," Pacific Northwest National Laboratory, PNNL-19596, July 2010.
 "Automated Demand Response Benefits California Utilities and Commercial & Industrial Customers," U.S. Department of Energy, September 2014.
 "Incorporating Renewables Into the Electric Grid: Expanding Opportunities For Smart Markets and Energy Storage," Executive Office of the President of the United States, June 2016.
 F. Babonneau, M. Caramanis and A. Haurie, " A Systems Approach to Regional Energy Modeling with Smart Grid Integrated Distributed Energy Resources," in IAEE Energy Forum, International Association for Energy Economics, Spring 2016, p. 15.
 "Renewables Watch," California Independent Systems Operator, 22 Oct 16.