Framing and the Production of Environmentally Conscious Citizens

by Margaret Loncki

One of the most obvious solutions to prevent further climate change is energy usage reductions, but with the increased magnitude of global energy consumption, these seem unlikely anytime soon. Spence et al. (2014) explore whether energy savings is most beneficial when presented in terms of financial cost, CO2, or kilowatt-hours. In the United Kingdom, smart meters are used to measure the energy consumption of private residences. Although the government is pushing to have smart meters be standard in every home, only half of the population know what they are, and of those who do, only a quarter understand their purpose. Framing energy consumption in terms of cost is a very easy concept for consumers to understand but due to varying energy costs, the benefits of energy reduction are not often clear. Presenting energy consumption in terms of CO2 release is not as easily understood as financial cost, but is thought to reduce the “psychological distance” of climate change. Spence et al. also found that environmental framing encourage behavioral spillover, the idea that changing one behavior for environmental reasons often leads to picking up other environmental behaviors as a result. Continue reading

Implementation Status of Electric Distribution Systems in U.S. Smart Grid Projects Funded Under the 2009 American Recovery and Reinvestment Act

by Stephanie Oehler
As climate change continues to have a greater impact on individuals and habitats around the world, many nations are taking actions to reduce their impacts by minimizing greenhouse gas emissions. Modernization of electrical grids in order to increase efficiency, accommodate new sources of energy and technology, minimize losses, and ultimately reduce harmful emissions from high-polluting forms of energy production has become a priority for many countries. For example, the American and Chinese governments each contributed over seven billion U.S. dollars to national Smart Grid deployment in 2010, with numerous other developed countries investing similarly large amounts in their own electricity infrastructures. Ghosh et al. (2013) explored the current status of projects partially funded through the Smart Grid Investment Grant (SGIG)  and the Smart Grid Demonstration (SGDP) programs created under the American Recovery and Reinvestment Act (ARRA) of 2009. Through a quantitative analysis of customer profile and distribution circuit data collected by the Department of Energy (DOE) specific to the progress of implementation of federally funded Smart Grid projects, the authors were able to observe trends in the impacts of utility size and type of technology on status of completion of Electric Distribution Systems (EDS) modernization specifically. Using these data, the authors concluded that SCADA technology tended to be implemented more quickly than DA devices, regardless of utility size. In the future, this may have an impact on which technologies developers decide to use in upgrading electricity grids. 
Ghosh, S., Pipattanasomporn, M., Rahman, S., 2013. Technology deployment status of U.S. Smart grid projects — electric distribution systems. IEEE Innovative Smart Grid Technologies, 1—8.

                  There is a variety of Smart Grid technologies that improve communication between the supply and demand sides of the grid, increase the efficiency of electricity transmission, reduce consumption, and increase reliability.  Ghosh and colleagues at the Advanced Research Institute at Virginia Tech briefly explored the five types of projects that the 99 recipients of $3.5 billion in grants from the DOE through the SGIG program fell under. These projects improved Advanced Metering Infrastructure (AMI), Customer Systems (CS), Electric Distribution Systems (EDS), Electric Transmission Systems (ETS), and Equipment Manufacturing. Due to the interconnections between these operations, 39% of the projects incorporated several of them. However, a large percentage (57%) of projects were related to EDS, which focuses on the operations and communications of distribution technologies. The authors explored the different types, status of deployment, and significance of EDS projects. In 2009, $1.96 billion in federal funding was distributed to EDS projects which have been estimated by the U.S. Energy Information Administration to impact over 34 million consumers, 30 million of which are in the residential sector and comprise 23% of America’s total residential electricity consumers. The authors went on to define the parameters that they would refer to throughout the article as they evaluated the extent of implementation of various projects, which included the size of the utility as determined by the number of distribution circuits within a service area, ranging from Very Small ( under 50 substations) to Large (more than 500 substations). They considered two types of EDS technologies, DA devices and SCADA systems. DA devices include technologies such as automated feeder switches, automated capacitors, automated regulators, fault current limiters, smart relays, remote fault indicators, and monitoring systems; while SCADA systems are implemented in large, spread out systems where uniformity of operations and extensive communication efforts are critical. Ultimately, they examined the ratio between the number of substations that have the new technologies to the total number within each area. The ratio of substations integrating EDS technologies to baseline substations was much higher in those with SCADA systems than those with DA devices, and they were more completely implemented. The results for DA devices varied between utility sizes and appeared to be dependent on which technology was used in each specific situation. Overall, most projects that were partially funded by federal grant money through the SGIG program were near completion if they had used SCADA, and still progressing if they had used DA devices.

                  This quantitative case study will be useful as a model for systems progress as new technologies are implemented, particularly with respect to EDS projects. While this study focuses on the EDS side of the SGIG grant, there is much more to explore regarding the successes and failures of the other types of Smart Grid projects. Additionally, comparative quantitative studies offer the federal government a tool to evaluate the effectiveness of its investments and the results may direct investments in the future.

Noncooperative and Cooperative Demand

by Stephanie Oehler
Electricity production and usage are closely related with the impacts of climate change because many forms of energy come from the combustion of fossil fuels and emit high amounts of carbon dioxide into the atmosphere. Smart grids, electricity networks that incorporate communication devices and allow energy to flow both to and from consumers and producers in order to increase efficient energy usage, offer a promising solution to many problems that are associated with traditional energy grids. By accommodating individual energy producers and storers, in addition to traditional production methods, smart grids provide a necessary modernization of energy management systems. Atzeni et al. (2013) studied several optimization methods that cater to different types of users on the demand side of smart grids. Through day-ahead demand-side management, the authors monitored the behavior of noncooperative and cooperative energy users in order to determine which was more beneficial in reducing costs among consumers. Both simulations demonstrated that active electricity users had the potential to reduce costs by distributing generation and storage according to time-slot dependent rates, regardless of whether they were strategizing individually or within a group, thereby stabilizing the load throughout the day and improving predictability of aggregate demand.
Atzeni, I., Ordonez L., Scutari, G., Palomar, D., Fonollosa J., 2013. Noncooperative and cooperative optimization of distributed energy generation and storage in the demand-side of the smart grid.              IEEE Transactions on Signal Processing 61, 2454—2472.

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Putting the “Smarts” into the Smart Grid: A Grand Challenge for Artificial Intelligence

The developed world has grown to its current state of monetary wealth because of the exploitation of cheap energy in the form of fossil fuels.  However, oil is becoming increasingly scarce, with peak production predicted to be reached within twenty years.  To further complicate matters, much of the oil reserves that remain lie in environmentally and politically vulnerable areas.  This makes oil subject to a higher chance of supply side shocks which drive energy prices higher.  Once we do run out of oil, we will need to transition to mostly renewable electrical energy sources such as wind, solar, and tidal energy.  Current projections already estimate the world’s electrical energy demand will increase by 76% by 2030, based on 2007 levels.  In order to accommodate the increased demand in electricity, the grids upon which the transmission of electricity depends must be adapted to the renewable infrastructure.  The new “smart” grids must both integrate widely distributed generators with varying outputs, and manage prosumers, who consume and produce electricity based on their local conditions and requirements.  Perhaps the most striking obstacle in the implementation of a new grid system is the artificial intelligence (AI) that must be developed to control energy flow, as investigated by (Ramchurn et al. 2011).—Donald Hamnett
Ramchurn, S., Vytelingum, P., Rodgers, A., Jennings, N., 2011. Putting the “smarts” into the smart grid: a grand challenge for artificial intelligence. University of Southampton, 1–9.

            Ramchurn and his colleagues organized the challenges involved with creating the smart grid’s AI into five categories:  demand-side management, electric vehicles, virtual power plants, energy prosumers, and self-healing networks.  Each of these possesses its own obstacles.  The researchers took into account the current state-of-the-art technologies to determine the AI advances that must be made to incorporate the aforementioned categories.
            In order for the grid to be safe and efficient, it must perfectly balance supply and demand.   The current grid adjusts supply to meet power demand, but in a renewable grid with varying output, demand-side management would help to reduce demand when energy supply is insufficient.  Artificial Intelligence that responds to price levels, owners’ preferences, and constraints on the grid could be used to flatten this demand curve.  For example, appliance use could be set to a timer which runs during low demand hours.  Also, both individuals on the consumer end and operators of the grid must have the ability to control and predict energy use.
            Another consideration of the smart grid is the future use of the electric vehicle (EV).  These vehicles put a large load on the grid due to the need for a rapid charge capable of a reasonable range of travel.  A system to predict individual and aggregate charging demands of EVs, and incentives to decentralize charging would need to be provided by AI to prevent tripping transformers.
            In the smart grid, virtual power plants (VPPs) are one proposed method of combining heterogeneous actors into aggregates.  AI would be necessary to model the complex interactions of a grid to form VPPs.  One of the difficulties in this regard is that on a constrained grid, individual actions affect all other parties in the grid.  A fair profit-sharing outcome would need to be reached with the most efficient VPPs possible being formed.
            The emergence of the energy prosumer with renewable energy on the smart grid requires AI that can predict prosumer profiles.  The consumption and generation prediction could then be used with price predictions to inform energy trading.  Profit maximization could be reached with such predictions, as could human-grid interactions that take into account prosumers’ preferences.
            A self-healing network requires real-time information to be shared between different nodes, which can coordinate to balance supply and demand.  The AI would need to estimate voltage and phase distribution given prosumers’ demand and supply.  Lastly, the AI would need to make predictions accurately even when faced with incomplete information.
            Switching to renewable energy is not only a matter of energy production, but also one of infrastructure and technology that are much more sophisticated than the current grid, including Artificial Intelligence.