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.

                  Recent developments in smart grid technology and management have focused on the demand side of energy and consumer behavior. Energy supply is reactive to consumer demand, so changes to the system have involved educating users and providing incentives to use less energy in a smarter way. Italo Atzeni and his fellow authors approach this topic from the demand-side as well by applying game theory to cooperative and noncooperative day-ahead energy consumption scheduling. They gathered hour-by-hour energy consumption data for a 24 hour period from a variety of consumers and used the information to produce algorithms to represent usage. Both passive and active users were accounted for in the algorithms; passive users being those that simply accept electricity from the grid and active users being those who possess energy storage or production capacity. Active users maintain the ability to function independently from the grid at times of peak usage by tapping into their own stored or personally produced sources and can take advantage of non-peak energy prices by storing energy to use in the future. The authors constructed two different models in which active users could determine their electricity usage for the following day with the intention of reducing personal energy costs by consulting a pricing model. The first model incorporated noncooperative users in which users considered only their own needs. The second model was cooperative and allowed groups of users to collaborate in deciding how much energy they would use and during what time slots. Factors accounted for in the models included the type of users, nondispatchable and distributed generation power, the storage capacities and characteristics of energy storage devices, and the varying cost per unit of energy.
                  Each model resulted in similar load shifts from peak hours to non-peak hours, thus evening out the aggregate electricity demand curve. Consequently, both active and passive users observed a decrease in their electricity costs. While each method produced the same result, the authors concluded that the cooperative optimization practices are probably superior to the noncooperative ones because they offer optimization on a larger scale which contributes to greater predictability and stability within the system. The authors also concluded that users who implemented their own energy production and/or storage technologies experienced the highest energy savings. Ultimately, the findings from these models demonstrated the ability that consumers have when using smart grids to stabilize electricity usage such that high-emission power plants that are only used in times of peak power are no longer required to meet society’s electricity demand.

                  As the global population continues to grow and individual electricity usage increases, reforming the energy grid has become a regulatory priority in an effort to confront climate change. Addressing energy consumption from the demand-side requires educating consumers and increasing communication regarding rates and usage trends, but it does not necessarily require extensive new infrastructure like supply-side advancements demand. As the authors demonstrated, there is much that can be done by consumers to improve energy efficiency and reduce carbon emissions. The continued expansion of renewable energy sources and increasing prevalence of energy storage vehicles and devices that are present on the electricity grid will allow smart users to rely on clean energy and to store excess when it is available in order to reduce their reliance on fossil fuels.

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