Incentives-Based Mechanism for Efficient Demand Response Programs

In this work we investigate the inefficiency of the electricity system with strategic agents. Specifically, we prove that without a proper control the total demand of an inefficient system is at most twice the total demand of the optimal outcome. We propose an incentives scheme that promotes optimal outcomes in the inefficient electricity market. The economic incentives can be seen as an indirect revelation mechanism that allocates resources using a one-dimensional message space per resource to be allocated. The mechanism does not request private information from users and is valid for any concave customer's valuation function. We propose a distributed implementation of the mechanism using population games and evaluate the performance of four popular dynamics methods in terms of the cost to implement the mechanism. We find that the achievement of efficiency in strategic environments might be achieved at a cost, which is dependent on both the users' preferences and the dynamic evolution of the system. Some simulation results illustrate the ideas presented throughout the paper.Comment: 38 pages, 9 figures, submitted to journa

Optimal GENCO bidding strategy

Electricity industries worldwide are undergoing a period of profound upheaval. The conventional vertically integrated mechanism is being replaced by a competitive market environment. Generation companies have incentives to apply novel technologies to lower production costs, for example: Combined Cycle units. Economic dispatch with Combined Cycle units becomes a non-convex optimization problem, which is difficult if not impossible to solve by conventional methods. Several techniques are proposed here: Mixed Integer Linear Programming, a hybrid method, as well as Evolutionary Algorithms. Evolutionary Algorithms share a common mechanism, stochastic searching per generation. The stochastic property makes evolutionary algorithms robust and adaptive enough to solve a non-convex optimization problem. This research implements GA, EP, and PS algorithms for economic dispatch with Combined Cycle units, and makes a comparison with classical Mixed Integer Linear Programming.;The electricity market equilibrium model not only helps Independent System Operator/Regulator analyze market performance and market power, but also provides Market Participants the ability to build optimal bidding strategies based on Microeconomics analysis. Supply Function Equilibrium (SFE) is attractive compared to traditional models. This research identifies a proper SFE model, which can be applied to a multiple period situation. The equilibrium condition using discrete time optimal control is then developed for fuel resource constraints. Finally, the research discusses the issues of multiple equilibria and mixed strategies, which are caused by the transmission network. Additionally, an advantage of the proposed model for merchant transmission planning is discussed.;A market simulator is a valuable training and evaluation tool to assist sellers, buyers, and regulators to understand market performance and make better decisions. A traditional optimization model may not be enough to consider the distributed, large-scale, and complex energy market. This research compares the performance and searching paths of different artificial life techniques such as Genetic Algorithm (GA), Evolutionary Programming (EP), and Particle Swarm (PS), and look for a proper method to emulate Generation Companies\u27 (GENCOs) bidding strategies.;After deregulation, GENCOs face risk and uncertainty associated with the fast-changing market environment. A profit-based bidding decision support system is critical for GENCOs to keep a competitive position in the new environment. Most past research do not pay special attention to the piecewise staircase characteristic of generator offer curves. This research proposes an optimal bidding strategy based on Parametric Linear Programming. The proposed algorithm is able to handle actual piecewise staircase energy offer curves. The proposed method is then extended to incorporate incomplete information based on Decision Analysis. Finally, the author develops an optimal bidding tool (GenBidding) and applies it to the RTS96 test system

Demand Response in Smart Grids

The Special Issue “Demand Response in Smart Grids” includes 11 papers on a variety of topics. The success of this Special Issue demonstrates the relevance of demand response programs and events in the operation of power and energy systems at both the distribution level and at the wide power system level. This reprint addresses the design, implementation, and operation of demand response programs, with focus on methods and techniques to achieve an optimized operation as well as on the electricity consumer

Effect of information on household water and energy use, The

2014 Summer.Water and Energy Utilities are faced with growing demand at a time when supply expansion is increasingly costly, inconsistent and taxing on the environment. Given that supply expansion is limited, to meet future needs utilities need demand-side management policies to result in more reliable and consistent consumer responsiveness. Currently, most households do not have access to the level or type of information needed to respond to price signals in a reliable and effective way. Advanced information technology solutions exist and are being increasingly adopted, but we need to know more about how the informational setting affects decision-making, consumption levels and price responsiveness. This research analyzes the effect of information on household water and energy consumption, which is a decision-making environment characterized by uncertainty and imperfect information. This study also analyzes additional complexities stemming from infrequent billing, non-linear pricing structures, and combined utility bills, each of which may dampen price signals. I first develop a theoretical model of decision-making under uncertainty. I use this model to illustrate the effect of more frequent information, which eliminates uncertainty about past decisions, on remaining decisions within the billing period. The model emphasizes the role of risk preferences and the realization of the uncertain quantity. On average, risk averse consumers will increase consumption when uncertainty is reduced; risk seeking consumers will do the opposite. Introduction of a non-linear rate structure induces behavior that makes individuals appear as if they are risk averse or risk seeking, despite their actual risk preferences. This model highlights the importance of modeling multiple decisions within a billing period and accounting for a spectrum of risk preferences. In Chapter 3, I create a computerized laboratory experiment designed to generate data used to test some of the hypotheses formulated in the theoretical model presented in Chapter 2. Results from the experiment show that, on average, individuals consume more when provided with more frequent information that resolves uncertainty about past decisions made within a single billing period. This result is driven by the fact that the majority of participants are risk averse or risk neutral. Risk seeking participants instead reduce use when facing less uncertainty. Also as predicted by the theoretical model in Chapter 2, combining behavior driven by risk preferences with the presence of an increasing block rate structure results in behavior that looks like consumers are targeting the block boundary. This experiment shows that providing more information may not lead to reduced use without other incentives, goal-setting, or mechanisms designed to help individuals process the information. In Chapter 4, I empirically analyze a ten-year household-level panel data set of monthly utility bills. A single utility provides electricity, natural gas and water services to its customers and therefore bills through a single utility bill. I first show that price responsiveness varies by the number and combination of services subscribed to by a given household. Second, through a price salience model I show that households are more responsive to the price of water when the water portion of the total bill is greater. When multiple services are contained on a single bill, the salience of any individual price signal is dampened. This study confirms that households are inelastic though not unresponsive to water prices. In order to make pricing policies more effective, utilities need to acknowledge that households may be responding to total utility costs (i.e., may respond to a high utility bill by reducing electricity use despite the true driver of the high bill) and will need to find ways to make quantity and price information more salient to their customers. Chapter 5 concludes this dissertation by summarizing the contributions of the research and possible extensions for future work. By improving the informational environment surrounding household water and energy use, there will be great capacity for households to use water and energy more efficiently and ultimately make choices that reduce residential water/energy consumption and yield benefits for customers, utilities, and the environment

Technology diffusion policy design : cost-effectiveness and redistribution in California solar subsidy programs

textHuman-induced climate change, with its potentially catastrophic impacts on weather patterns, water resources, ecosystems, and agricultural production, is the toughest global problem of modern times. Impeding catastrophic climate change necessitates the widespread deployment of renewable energy technologies for reducing the emissions of heat-trapping gases, especially carbon di-oxide (CO₂). However, the deployment of renewable energy technologies is plagued by various market failures, such as environmental externalities from conventional energy sources, learning-by-doing, innovation spillover effects, and peer effects. In efforts to begin to address these market failures, several governments at all levels—city, state, regional, and national—have instituted various subsidies for promoting the adoption of renewable energy technologies. Public resources are limited and have competing uses. So, it is important to ask: how cost-effective are renewable energy subsidies? Are the subsidies even reaching the intended subjects—the potential adopters of renewable energy technologies? In this empirically-driven dissertation, I analyze these important policy design and evaluation questions with a focus on the solar subsidy programs in California. All programs to incentivize the adoption of renewable energy technologies run into the same key question: what is the optimal (maximum capacity inducing) rebate schedule in the face of volatile product prices and the need for policy certainty? Answering this question requires careful attention to both supply-side (learning-by-doing) and demand-side (peer effects) market dynamics. I use dynamic programming to analyze the effectiveness of the largest state-level solar photovoltaic (PV) subsidy program in the U.S. – the California Solar Initiative (CSI) – in maximizing the cumulative PV installation in California under a budget constraint. I find that previous studies overestimated learning-by-doing in the solar industry. Consistent with other studies, I also find that peer effects are a significant demand driver in the California solar market. The main implication of this empirical finding in the dynamic optimization context is that it forces the optimal solution towards higher subsidies in earlier years of the program, and, hence, leads to a lower program duration (for the same budget). In particular, I find that the optimal rebate schedule would start not at $2.5/W as it actually did in CSI, but instead at$4.2/W; the effective policy period would be only three years instead of the realized period of six years. This optimal (i.e., most cost effective) solution results in total PV adoption of 32.2 MW (8.1%) higher than that installed under CSI, using the same budget. Furthermore, I find that the optimal rebate schedule starts to look like the actual CSI in a ‘policy certainty’ scenario where the variation of periodic subsidy-level changes is constrained. Finally, introduction of stochastic learning-by-doing as a way to better capture the dynamic nature of learning in markets for new products does not yield significantly different results compared to the deterministic case. Another, still-unanswered, redistribution question related to the CSI program is: to what degree have the direct PV incentives in California been passed through from installers to consumers? I address this question by carefully examining the residential PV market in California by applying multiple methods. Specifically, I apply a structural-modeling approach, a reduced-form regression analysis, and regression discontinuity designs to estimate the incentive pass-through rate in California’s solar program. The results consistently suggest a high average pass-through rate of direct incentives of nearly 100%, though with regional differences among California counties and utilities. While these results could have multiple explanations, they suggest a relatively competitive market and a smoothly operating subsidy program. Combining evidence from the optimal subsidy policy design and the incentive pass-through analysis, this dissertation lends credibility to the cost-effectiveness of CSI given CSI’s design goal of providing policy certainty and also finds a near-perfect incidence in CSI. Long-term credible commitment as reflected through CSI’s capacity-triggered step changes in rebates along with policy and data transparency are important factors for CSI’s smooth and cost-effective functioning. Though CSI has now wound down because final solar capacity targets have been reached, the historical performance of CSI is relevant not only as an ex-post analysis in California, but potentially has broader policy implications for other solar incentive programs both nationally and internationally.Public Affair

Supply function equilibrium analysis for electricity markets

The research presented in this Thesis investigates the strategic behaviour of generating firms in bid-based electricity pool markets and the effects of control methods and network features on the electricity market outcome by utilising the AC network model to represent the electric grid. A market equilibrium algorithm has been implemented to represent the bi-level market problem for social welfare maximization from the system operator and utility assets optimisation from the strategic market participants, based on the primal-dual interior point method. The strategic interactions in the market are modelled using supply function equilibrium theory and the optimum strategies are determined by parameterization of the marginal cost functions of the generating units. The AC power network model explicitly represents the active and reactive power flows and various network components and control functions. The market analysis examines the relation between market power and AC networks, while the different parameterization methods for the supply function bids are also investigated. The first part of the market analysis focuses on the effects of particular characteristics of the AC network on the interactions between the strategic generating firms, which directly affect the electricity market outcome. In particular, the examined topics include the impact of transformer tap-ratio control, reactive power control, different locations for a new entry’s generating unit in the system, and introduction of photovoltaic solar power production in the pool market by considering its dependencyon the applied solar irradiance. The observations on the numerical results have shown that their impact on the market is significant and the employment of AC network representation is required for reliable market outcome predictions and for a better understanding of the strategic behaviour as it depends on the topology of the system. The analysis that examines the supply function parameterizations has shown that the resulting market solutions from the different parameterization methods can be very similar or differ substantially, depending on the presence and level of network congestion and on the size and complexity of the examined system. Furthermore, the convergence performance of the implemented market algorithm has been examined and proven to exhibit superior computational efficiency, being able to provide market solutions for large complex AC systems with multiple asymmetric firms, providing the opportunity for applications on practical electricity markets

Large-scale unit commitment under uncertainty: an updated literature survey

The Unit Commitment problem in energy management aims at finding the optimal production schedule of a set of generation units, while meeting various system-wide constraints. It has always been a large-scale, non-convex, difficult problem, especially in view of the fact that, due to operational requirements, it has to be solved in an unreasonably small time for its size. Recently, growing renewable energy shares have strongly increased the level of uncertainty in the system, making the (ideal) Unit Commitment model a large-scale, non-convex and uncertain (stochastic, robust, chance-constrained) program. We provide a survey of the literature on methods for the Uncertain Unit Commitment problem, in all its variants. We start with a review of the main contributions on solution methods for the deterministic versions of the problem, focussing on those based on mathematical programming techniques that are more relevant for the uncertain versions of the problem. We then present and categorize the approaches to the latter, while providing entry points to the relevant literature on optimization under uncertainty. This is an updated version of the paper "Large-scale Unit Commitment under uncertainty: a literature survey" that appeared in 4OR 13(2), 115--171 (2015); this version has over 170 more citations, most of which appeared in the last three years, proving how fast the literature on uncertain Unit Commitment evolves, and therefore the interest in this subject