Yihsu Chen

Economic and Emissions Implications of Load-Based, Source-Based, and First-Seller Emissions Trading Programs Under California AB32

In response to Assembly Bill 32, the state of California considered three types of carbon emissions trading programs for the electric power sector: load-based, source-based, and first-seller. They differed in terms of their point of regulation and in whether in-state-to-out-of-state and out-of-state-to-in-state electricity sales are regulated. In this paper, we formulate a market equilibrium model for each of the three approaches, considering power markets, transmission limitations, and emissions trading, and making the simplifying assumption of pure bilateral markets. We analyze the properties of their solutions and show the equivalence of load-based, first-seller, and source-based approaches when in-state-to-out-of-state sales are regulated under the cap. A numeric example illustrates the emissions and economic implications of the models. In the simulated cases, “leakage” eliminates most of the emissions reductions that the regulations attempt to impose. Furthermore, “contract reshuffling” occurs to such an extent that all the apparent emissions reductions resulting from changes in sources of imported power are illusory. In reality, the three systems would not be equivalent because there will also be pool-type markets, and the three systems provide different incentives for participating in those markets. However, the equivalence results under our simplifying assumptions show that load-based trading has no inherent advantage compared to other systems in terms of costs to consumers, contrary to claims elsewhere.

Biofuels that cause land-use change may have much larger non-GHG air quality emissions than fossil fuels.

Although biofuels present an opportunity for renewable energy production, significant land-use change resulting from biofuels may contribute to negative environmental, economic, and social impacts. Here we examined non-GHG air pollution impacts from both indirect and direct land-use change caused by the anticipated expansion of Brazilian biofuels production. We synthesized information on fuel loading, combustion completeness, and emission factors, and developed a spatially explicit approach with uncertainty and sensitivity analyses to estimate air pollution emissions. The land-use change emissions, ranging from 6.7 to 26.4 Tg PM(2.5), were dominated by deforestation burning practices associated with indirect land-use change. We also found Brazilian sugar cane ethanol and soybean biodiesel including direct and indirect land-use change effects have much larger life-cycle emissions than conventional fossil fuels for six regulated air pollutants. The emissions magnitude and uncertainty decrease with longer life-cycle integration periods. Results are conditional to the single LUC scenario employed here. After LUC uncertainty, the largest source of uncertainty in LUC emissions stems from the combustion completeness during deforestation. While current biofuels cropland burning policies in Brazil seek to reduce life-cycle emissions, these policies do not address the large emissions caused by indirect land-use change.

A bottom-up biofuel market equilibrium model for policy analysis

Although the biofuel market remains at its early stage, it is expected to play an important role in climate policy in the future in the transportation sector. In this paper, we develop a bottom-up equilibrium model to study the supply chain of the biofuel market, explicitly formulating the interactions among farmers, biofuel producers, blenders, and consumers. The model is built on optimization problems faced by each entity and considers decisions associated with farmers’ land allocation, biomass transportation, biofuel production, and biofuel blending. As such, the model is capable of and appropriate for policy analysis related to interactions among multiple stakeholders. For example, the model can be used to analyze the impacts of biofuel policies on market outcomes, pass-through of taxes or subsidies, and distribution of consumers’ or producers’ surplus. The equilibrium model can also serve as an analytical tool to study the price impact of biomass, biofuel, and Renewable Identification Numbers (RINs) for biofuels. We demonstrate the model by applying it to a case study of Iowa. We specifically focus on the effects of market structure, i.e., points-of-implementation on subsidies on market outcomes. The results indicate that some entities can benefit greatly at the expense of others when they possess market power. Government oversight is therefore needed to safeguard the development of the sector.

The Impact of Imperfect Competition in Emission Permits Trading on Oligopolistic Electricity Markets

The impact and efficacy of a cap-and-trade regulation on the electric power industry depend on interactions of demand elasticity, transmission network, market structure, and strategic behavior of generation firms. This paper develops an equilibrium model of an oligopoly electricity market in conjunction with a Cap-and-Trade emissions permits market to study such interactions. The concept of conjectural variations is proposed to account for imperfect competition in the permits market. We demonstrate the model using a WECC 225-bus system with a detailed representation of the California market. In particular, we examine the extent to which permit trading strategies affect the market outcome. We find that a firm with more efficient technologies can employ strategic withholding of permits, which allows for its increase in output share in the electricity market at the expense of other less efficient firms.

Environmental regulation in transmission-constrained electricity markets

We discuss potential competitive effects of regulating carbon emissions in a transmission constrained electricity market. We compare two regulatory instruments, renewable portfolio standards and taxing emmissions. We derive general conclusions about impacts on prices and output on a three node network. We find that renewable portfolio standards increase the market power of nonpolluting generators whereas the tax is market-power neutral. We verify our conclusions through simulations.

Price Containment in Emissions Permit Market: Balancing Market Risk and Environmental Outcomes

While cap-and-trade policies have been advocated as an efficient market-based approach in regulating greenhouse gas (GHG) emissions from the power sector, a major criticism is that the resulting prices of emissions permit may be volatile, adding more uncertainty to market participants, and hence deter their interests in participating the electricity market. To ease such a concern, various permit price-containment instruments, such as a price ceiling, floor, or collar, have been proposed. Though such instruments may prevent permit prices from being extreme, they may incur inadvertent results such as underinvestment in low-emission technologies or little reduction of system-wide GHG emissions, hence defeating the purpose of establishing a cap-and-trade policy in the first place. To address such issues, this article examines the effect of imposing various price-containment policies on investment decisions and spot market equilibria in an electricity market. Our major contribution is that, unlike other work in this area in which the price-containment schemes are exogenous to their market models we endogenously incorporate a price ceiling/floor in our models and hence can analyze the interactions between the policies and their corresponding market outcomes. We further introduce uncertainties in our market models and use Californias data as a case study.

Foreword for the Special Section on Power System Planning and Operation Towards a Low-Carbon Economy

The nine papers in this special section on power system planning and operation towards a low-cost economy cover the following topics: power system planning models; power system operation methods and market behavior analysis; and risk assessment and emission management.

CO2 emissions leakage in the power system under Regional Greenhouse Gas policy

A number of states in the United States have taken collaborative actions to control for greenhouse gas (GHG) emissions from state governmental level. The effort by the western and the eastern states is called the Western Regional Climate Action Initiative and Regional Greenhouse Gas Initiative (RGGI), respectively. RGGI is a collaboration by 9 states in the northeast United States targeted at regional CO2 emissions. This paper proposes an analytical tractable model to illustrate the conditions under which the CO2 leakage would occur.

Analyzing the Long-run Impact of the Regional Greenhouse Gas Initiative on the Maryland Power Sector: Oligopoly Analysis

The regional greenhouse gas initiative (RGGI) is a joint effort by a number of northeast states to control for regional CO2 emissions. The objective is to reduce CO2 emissions from the power sector 10% below the current level by 2019 using a cap-and-trade program. The State of Maryland has sponsored an analysis to determine the impacts on Maryland of participating in RGGI. In particular, this analysis sought to evaluate the competitiveness of the state power sector and as well as the any increases in electricity costs for consumers in Maryland. This presentation will summarize the methods and the preliminary results of this study, focusing on results of the simulations that represent strategic behavior by generators.

Special Issue on Smart Grid and Emerging Technology Integration

Electrification for the world through the vast networks of electricity has been considered by the National Academy of Engineering as the most important engineering achievement of the twentieth century. The achievement is reflected by the generally successful management of power system complexity, which lies in its physical requirement of continuous balancing of supply and demand. The complexity, however, has been dramatically increased in the past decade, with the increasing penetration of variable-output renewable energy, the widespread of distributed generation and demand-side resources, and the electrification of transportation systems. To power the world’s economic growth and to meet societal needs, it becomes more apparent that the current power grid needs to be modernized so that the grid can be more reliable, flexible, efficient, and resilient. This gives rise to the notion of the smart grid, a power system characterized by a diverse generation resource mix, a combination of centralized and distributed generation, active demand participation, and a robust transmission and distribution network enhanced by advanced digital sensor, communication, and control technologies. While the vision of the smart grid is clear, there are many obstacles in implementing the various concepts. On the generation side, the variable-output nature of major renewable resources (wind and solar energy) poses considerable difficulties in maintaining system reliability. On the demand side, if not managed properly, more actively engaged distributed generation and demand-response resources will add more fluctuations to the system. On the transmission and distribution side, the increased uncertainty from both the supply and demand side calls for swift and flexible operations. This special issue is dedicated to address many of the challenges faced by the transitioning to the smart grid. More specifically, it focuses on four major topics, as follows: (1) renewable integration, (2) smart transmission, (3) distributed generation and storage, and (4) flexible demand resources. In aiding renewable generation integration to the grid, the paper “Joint Probability Distribution and Correlation Analysis of Wind and Solar Power Forecast Errors in the Western Interconnection” by J. Zhang, B.-M. Hodge, and A. Florita investigates how to utilize the correlation between wind and solar forecast errors to improve forecasting, and hence to facilitate the integration of both wind and solar into the grid. In the paper “Optimal Management of Wind Energy with Storage: Structural Implications for Policy and Market Design” by N. R. Kirby, L. C. Anderson, and M. Davison, they investigate strategies to bundle energy storage with wind energy to reduce variability and to increase operational and planning efficiency. While in “Impacts of the Renewable Portfolio Standard on Regional Electricity Markets” by Y. Zhou and T. Liu, they study the impacts of policies on the development of renewable energy. Aided by smart devices, power grids’ transmission networks can be operated more flexibly to both meet the challenges of the increasing uncertainty and to improve systems’ reliability. The paper “N-1 Reliable Unit Commitment via Progressive Hedging” by C. Li, M. Zhang, and K. W. Hedman proposes a customized stochastic programming method, based on the progressive hedging algorithm, to improve the unit commitment process while explicitly accounting for uncertainties. In “Real Time Corrective Transmission Switching in Response to N-m Events,” P. Balasubramanian and K. W. Hedman design a heuristic algorithm to improve the computational performance of the joint optimization of economic dispatch and transmission topology control. As a defining characteristic of the smart grid is to shift from centralized to distributed generation (DG), two papers in this special issue discuss several DG technologies. In “Novel Fuzzy Controlled Energy Storage for Low-Voltage Distribution Networks with Photovoltaic Systems under Highly Cloudy Conditions” by J. Wong, Y. S. Lim, and E. Morris, they develop a novel fuzzy control method to manage low-voltage photovoltaic (PV) systems coupled with storage resources. While in the paper “Stochastic, Multiobjective, Mixed-Integer Optimization Model for Wastewater-Derived Energy” by C. U-tapao, S. A. Gabriel, C. P. E. Peot, and M. Ramirez, a multiobjective, mixed-integer optimization model is developed for a wastewater-treatment plant, which can convert the solid end product from wastewater into renewable energy. Last but certainly not the least, as active demand participation is considered as a cornerstone for the smart grid, four papers in this special issue are dedicated to address the various aspects of demand response. In the paper “Is Deferrable Demand an Effective Alternative to Upgrading Transmission Capacity?” by A. J. Lamadrid, T. D. Mount, W. Jeon, and H. Lu, they investigate the many benefits of having deferrable load in the system, including reducing transmission congestion, lowering average wholesale electricity prices, and reducing the need of having extra generation capacity to maintain system reliability. From consumers’ perspective, F. Chen, L. V. Snyder, and S. Kishore in their paper “Efficient Algorithms and Policies for Demand Response Scheduling” propose several algorithms based on approximate dynamic programming to optimize energy consumption in a single electricityconsuming facility. While efficient management demand response (DR) resources is certainly important, another critical aspect is cyber security and privacy. In addressing this aspect, the paper “Achievable Privacy in Aggregate Residential Energy Management Systems,” by C. Chen, L. He, P. Venkitasubramaniam, S. Kishore, and L. V. Snyder, proposes strategies, such as inserting dummy