Andrew L. Liu

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.

Collusive game solutions via optimization

A Nash-based collusive game among a finite set of players is one in which the players coordinate in order for each to gain higher payoffs than those prescribed by the Nash equilibrium solution. In this paper, we study the optimization problem of such a collusive game in which the players collectively maximize the Nash bargaining objective subject to a set of incentive compatibility constraints. We present a smooth reformulation of this optimization problem in terms of a nonlinear complementarity problem. We establish the convexity of the optimization problem in the case where each players strategy set is unidimensional. In the multivariate case, we propose upper and lower bounding procedures for the collusive optimization problem and establish convergence properties of these procedures. Computational results with these procedures for solving some test problems are reported.

Quantifying System-Level Benefits from Distributed Solar and Energy Storage

AbstractMicrogeneration using solar photovoltaic (PV) systems is one of the fastest growing applications of solar energy in the United States. Its success has been partly fueled by the availability of net metering by electric utilities. However, with increasing solar PV penetration, the availability of net metering is likely to be capped. Households would then need to rely on distributed storage to capture the full benefits of their installed PV systems. Although studies of these storage systems to assess their benefits to the individual household have been examined in literature, the systemwide benefits have yet to be fully examined. In this study, the utility level benefits of distributed PV systems coupled with electricity storage are quantified. The goal is to provide an estimate of these benefits so that these savings can potentially be translated into incentives to drive more PV investment. An agent-based residential electricity demand model is combined with a stochastic programming unit commitment ...

On Nash–Cournot games with price caps

Abstract In this paper, we study an N -person Nash–Cournot game with a price cap. Under certain mild conditions, we show that this game can be formulated as a complementarity problem. Based on this formulation, we study various properties of the game, including equilibrium existence, computability, and a description of the equilibrium set when there are multiple equilibria.

A Memoryless Robot that Assembles Seven Subsystems to Copy Itself

This paper presents a robot that can assemble exact functional replicas of itself from seven more basic parts/subsystems. The robot follows lines on the floor using light sensors and a simple control circuit without any onboard memory. It performs a self-replication task comparable in difficulty to those of previous self-replicating robots, but with a greatly simplified control system and reduced overall system complexity. Three methods are presented that quantify aspects of the complexity of the robot and the pattern of lines it follows. The complexity measures provide a way to compare existing self-replicating robot systems and to evaluate new designs. Robotic self-replication is an aspect of automated assembly that has not been studied extensively in hardware, and this work (which was the outcome of a project in a mechatronics course at JHU) is one step in a larger effort to quantify and demonstrate various aspects of this research area.

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.

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

Long-Term Planning of Wind Farm Siting in the Electricity Grid

Abstract Wind power is the fastest growing electricity generation source in the United States and is expected to play an increasing role in the electricity system. The variable and uncertain nature of wind power will require changes in the operation of the grid under higher penetration rates. In order to understand the changes necessary at both the planning and operational levels we propose a stochastic optimization approach that enables the consideration of the variable output of wind power as well as the costs of wind farm construction. Results from a small example system are used to illustrate the advantages of the approach.