Reducing carbon dioxide emissions in the electricity sector using demand side management
Date
2019
Authors
Almohaimeed, Sulaiman, author
Suryanarayanan, Siddharth, advisor
Collins, George J., committee member
Zimmerle, Daniel, committee member
Aloise-Young, Patricia, committee member
O’Neill, Peter, committee member
Journal Title
Journal ISSN
Volume Title
Abstract
Increasing demand for energy consumption leads to concerns of global Greenhouse Gas (GHG) emissions. Most of the supplied energy comes from dirty generating units. Since there are no regulations to limit emissions of CO2 from electricity generation, power plants can emit unlimited amount of CO2. This dissertation, first, aims to explain some government directed plans to reduce GHG emissions. It gives an overview about the Clean Power Plan (CPP) and its benefits and challenges. Further, it explains several options of CPP in reducing emissions and its repeal. Further, this dissertation, discusses the Climate Action Plan (CAP) corresponding to Fort Collins, Colorado, U.S. and its timeline targets. Demand side management (DSM) is discussed as a solution from engineering practices to affect GHG. Several options from DSM are investigated to reduce emissions. In fact, reducing energy consumption through DSM leads to a reduction in harmful emissions to the environment. This dissertation aims to identify the best available DSM options that will make the biggest difference for GHG reductions. A framework is created to examine several options of DSM in reducing carbon footprints. The framework states that affecting GHG in electric power system is the main goal. The goal can be achieved by implementing DSM technologies in distribution systems. The framework proposes criteria such as cost, power quality, reliability, environmental collateral, and socioeconomic equity to examine the effectiveness of several alternatives: energy management, communication and intelligence, electrification of heating and transportation, and distributed generation. Multi-Criteria Decision Making (MCDM) algorithms have been proposed to prioritize alternatives and select the ones that achieve suitable emissions reduction. Analytic Hierarchy Process (AHP) is one of the most common tools to perform decision-making analysis. The findings from AHP show that the "communication and intelligence" option is the potential optimal alternative in achieving the goal. Analytic Network Process (ANP) is another method for making decisions. It provides feedback and interdependence relationships between all nodes of the problem. It is more realistic and accurate than AHP. The results obtained from ANP suggest that "communication and intelligence" is the optimum technology to reach the target. By using ANP, the overall priority ranking has changed and the difference in priorities has reduced. Institute of Electrical and Electronics Engineers (IEEE) 13-node test feeder is used, through Open Distribution System Simulator (OpenDSS), to perform power flow analysis on yearly load profile corresponding to Fort Collins, Colorado, U.S. The analysis includes simulation for several scenarios from the MCDM alternatives, either individual alternatives or mixed alternatives. The obtained results for the base case show the emissions decreased by 16.26% from 2005 level which comply with the results from emissions indicator released by the city. Integrating the MCDM alternatives indicates CO2 emissions change as a result of variation in supply and demand curve. The findings for 2017 load profile demonstrated that "electric stationary storage" is the best option, environmentally, since it contributes in more than 18% emissions reduction from 2005 level. The second alternative is "energy conservation" by achieving a 20.39% reduction in emissions, merging both alternatives in one scenario could increase the emissions mitigation up to 22.17%. By simulation the residential sector, "communication and intelligence" shows about 14% reduction in emissions from 2005 level. A scenario that combines "electric stationary storage" with "communication and intelligence" diminishes the emissions by more than 15%. Indeed, combining "communication and intelligence" with "energy conservation" can decrease the environmental footprint by 18.04%. Last scenario examined combining all MCDM alternatives in one option. The result finds that this option can reach 19.72% emissions reduction. Since the simulation part investigates the system from environmental perspective, this work deploys a Cost Benefit Analysis (CBA) to assess economic, technical, and environmental cost and benefits associated with each alternative. The economic evaluation shows that "electric stationary storage" is the potential best option. This is reasonable since ESS charges during lower electricity price and discharge during peaking demand. Thus, the customers can avoid the high electricity charges, and the utility is not required to run more generating units. "communication and intelligence" combined with "electric stationary storage" is the second option due to its flexibility in shifting the loads to off-peak periods is. The scenario that includes all MCDM options came in the third place since it provides almost 20% emissions reduction and its economic evaluation is beneficial. While "energy conservation" project and "electric stationary storage" with "energy conservation" project provide less economic impact than "communication and intelligence", those alternatives hold the fourth and fifth place, respectively, due to their environmental impact. The penultimate alternative is "communication and intelligence" because the Demand Response (DR) is designed to shift the peak load, and it has socioeconomic cost. Last alternative is combining "communication and intelligence" with "energy conservation". Although "energy conservation" performs environmentally better than "communication and intelligence", its socioeconomic cost plays a major role in selecting such alternative. However, the ranking might change according to the participants' choice. One can prefer environmental impact over economic output and vice versa. Therefore, this work presents a trade-off chart, so the decision maker can select the alternative based on their preference. All analysis, simulation, and results in this work are particularly based on Fort Collins distribution system data and is not a general assessment. There are several factors might affect the result such as the location, the data, or the distribution system structure.