The Pace of Decarbonization: Can the Power System Transition Meet Climate Policy Goals?

To reach net zero greenhouse gas emissions by 2050, the United States will need to simultaneously expand and decarbonize its electricity supply. Aggressive clean energy policies are necessary for the pace of the transition to meet this goal. Policymakers rely on computer modeling to inform decarbonization policies, even though the models were not developed for this purpose. This paper investigates the role of electricity modeling in climate policy design through a case study of Massachusetts. The analysis compares modeling results with recent energy projects in order to highlight the strengths and weaknesses of power sector modeling as a tool to inform policy making. The results show that modeling is useful for identifying technically feasible options and for comparing them based on quantifiable indicators. Models are incapable of identifying socially optimal solutions and estimating achievable pace of decarbonization, because they omit social factors that affect decarbonization goals

A Decision Framework for Optimal Pairing of Wind and Demand Response Resources

Day-ahead electricity markets do not readily accommodate power from intermittent resources such as wind because of the scheduling difficulties presented by the uncertainty and variability in these resources. Numerous entities have developed methods to improve wind forecasting and thereby reduce the uncertainty in a day-ahead schedule for wind power generation. This paper introduces a decision framework for addressing the inevitable remaining variability resulting from imperfect forecasts. The framework uses a paired resource, such as demand response, gas turbine, or storage, to mitigate the generation scheduling errors due to wind forecast error. The methodology determines the cost-effective percentage, or adjustment factor, of the forecast error to mitigate at each successive market stage, e.g., 1 h and 10 min ahead of dispatch. This framework is applicable to any wind farm in a region with available pairing resources, although the magnitude of adjustment factors will be specific to each region as the factors are related to the statistics of the wind resource and the forecast accuracy at each time period. Historical wind data from New England are used to illustrate and analyze this approach. Results indicate that such resource pairing via the proposed decision framework will significantly reduce the need for an independent system operator to procure additional balancing resources when wind power participates in the markets

Wind Power Uncertainty and Power System Performance

The penetration of wind power into global electric power systems is steadily increasing, with the possibility of 30% to 80% of electrical energy coming from wind within the coming decades. At penetrations below 10% of electricity from wind, the impact of this variable resource on power system operations is manageable with historical operating strategies. As this penetration increases, new methods for operating the power system and electricity markets need to be developed. As part of this process, the expected impact of increased wind penetration needs to be better understood and quantified. This paper presents a comprehensive modeling framework, combining optimal power flow with Monte Carlo simulations used to quantify the impact of high levels of wind power generation in the power system. The impact on power system performance is analyzed in terms of generator dispatch patterns, electricity price and its standard deviation, CO2 emissions and amount of wind power spilled. Simulations with 10%, 20% and 30% wind penetration are analyzed for the IEEE 39 bus test system, with input data representing the New England region. Results show that wind power predominantly displaces natural gas fired generation across all scenarios. The inclusion of increasing amounts of wind can result in price spike events, as the system is required to dispatch down expensive demand in order to maintain the energy balance. These events are shown to be mitigated by the inclusion of demand response resources. Benefits include significant reductions in CO2 emissions, up to 75% reductions at 30% wind penetration, as compared to emissions with no wind integration

Hybrid Power System Options for Off-Grid Rural Electrification in Northern Kenya

For domestic consumers in the rural areas of northern Kenya, as in other developing countries, the typical source of electrical supply is diesel generators. However, diesel generators are associated with both CO2 emissions, which adversely affect the environment and increase diesel fuel prices, which inflate the prices of consumer goods. The Kenya government has taken steps towards addressing this issue by proposing The Hybrid Mini-Grid Project, which involves the installation of 3 MW of wind and solar energy systems in facilities with existing diesel generators. However, this project has not yet been implemented. As a contribution to this effort, this study proposes, simulates and analyzes five different configurations of hybrid energy systems incorporating wind energy, solar energy and battery storage to replace the stand-alone diesel power systems servicing six remote villages in northern Kenya. If implemented, the systems proposed here would reduce Kenya’s dependency on diesel fuel, leading to reductions in its carbon footprint. This analysis confirms the feasibility of these hybrid systems with many configurations being profitable. A Multi-Attribute Trade-Off Analysis is employed to determine the best hybrid system configuration option that would reduce diesel fuel consumption and jointly minimize CO2 emissions and net present cost. This analysis determined that a wind-diesel-battery configuration consisting of two 500 kW turbines, 1200 kW diesel capacity and 95,040 Ah battery capacity is the best option to replace a 3200 kW stand-alone diesel system providing electricity to a village with a peak demand of 839 kW. It has the potential to reduce diesel fuel consumption and CO2 emissions by up to 98.8%

Co-optimizing High and Low Voltage Systems: Bi-Level vs. Single-Level Approach

This paper presents a bi-level optimization framework applied to optimize system performance with (i) increasing presence of distributed energy resources (DER) at the low-voltage level, and (ii) variable wind power generation at the high-voltage level. The paper investigates various system configurations with increasing presence of microgrids, with active devices. System simulations quantify system performance in terms of cost, first using the traditional single-level optimization framework, and second using the proposed bi-level framework. Comparisons between the system with traditional, passive distribution systems and with microgrids are also presented, with results again quantified via the interconnected system operating costs. Results show that at low levels of DER and microgrid penetration, traditional (single-level) system optimization algorithms perform adequately as compared to the proposed bi-level optimization framework. However, as DER and microgrid penetration increase, the traditional single-level framework does not accurately capture the full system benefits of distributed technologies. The results demonstrate that new optimization algorithms, such as the proposed bi-level framework, will be required if the benefits of DER are to be accurately quantified in the evolving power system

Data Privacy in the Smart Grid: A Decentralized Approach

Evolution toward the smart grid includes implementation of elements such as smart meters, embedded microprocessors, two-way communication systems from consumers to system operators, and automated demand response as supported through dynamic pricing. Dynamic pricing throughout the smart grid will require frequent transfer of energy consumption data from the customers to the ISOs. Privacy and security issues related to transferring this data are widely studied. However, typical frameworks rely on a trusted third party, such as the ISO or a load aggregator, that would then have access to all of the consumer data. This paper proposes a Bitcoin-like decentralized model as a solution for secure information transfer within the smart grid, eliminating the presence of a centralized data aggregator or other third party operator. Each smart meter participates as an equal peer in the proposed peer-to-peer network, and elements of authentication, confidentiality and data verification are developed similar to the existing Bitcoin framework. The contribution of this paper is the proposed framework for the smart grid which cryptographically secures the transfer of energy consumption data while ensuring privacy

Renewable energy technologies : analysis and policy tools for utility integration

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1994.Includes bibliographical references (p. 143-150).by Judith Bernitt Cardell.M.S

Estimating the System Costs of Wind Power Forecast Uncertainty

Uncertainty in forecasts of wind power generation raises concerns of integrating wind power into power system operations and electricity markets at acceptable costs. The analysis presented in this paper uses an optimal power flow (OPF) model in a Monte Carlo Simulation (MCS) framework to estimate the additional cost of power system operation with uncertain output from a wind farm. A base case dispatch is established along with alternate dispatches based upon a probability distribution of real time wind power generation. The cost of the uncertainty in wind power forecasts is then quantified in terms of the difference in production cost between the base case and the cost for system dispatch under scenarios drawn from the distribution of real time wind power generation. Using various regional load levels and ramp capabilities of other generators, the results from the OPF and MCS show that wind power forecast uncertainty for the test system can increase production cost between 2.5% and 11%

Key Technical Challenges for the Electric Power Industry and Climate Change

This paper, prepared by the Climate Change Technology Subcommittee, a subcommittee of the Power and Energy Society Energy Development and Power Generation Committee, identifies key technical issues facing the electric power industry, related to global climate change. The technical challenges arise from: 1) impacts on system operating strategies, configuration, and expansion plans of emission-reducing technologies; 2) power infrastructure response to extreme weather events; 3) effects of government policies including an expanded use of renewable and alternative energy technologies; and 4) impacts of market rules on power system operation. Possible lessons from other industries\u27 responses to climate change are explored

Distributed Resource Participation in Local Balancing Energy Markets

In response to the new and potentially conflicting economic and technical demands of independent, distributed resources, the power system requires a new means for coordinating system and market operations. Price signals are one mechanism available to coordinate the operation of a power system in the emerging competitive markets. This paper discusses the integration of distributed resources into the operations of the power system by means of organizing the resources into microgrids and allowing them to participate in local electricity markets through responding to price signals. The simulations of price signals proposed in this paper successively expand upon the current open loop market framework. For distributed resources to participate in energy markets and provide energy balancing two new price mechanisms are introduced and analyzed. First, an open loop strategy is introduced, based upon the concept of a proposed price-droop. Second, a closed loop strategy using a hypothesized dynamic cost equation is introduced. The behavior of distributed resources responding to these two proposed mechanisms is compared to their behavior in a strictly competitive environment