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

Contract Design for Energy Demand Response

Power companies such as Southern California Edison (SCE) uses Demand Response (DR) contracts to incentivize consumers to reduce their power consumption during periods when demand forecast exceeds supply. Current mechanisms in use offer contracts to consumers independent of one another, do not take into consideration consumers' heterogeneity in consumption profile or reliability, and fail to achieve high participation. We introduce DR-VCG, a new DR mechanism that offers a flexible set of contracts (which may include the standard SCE contracts) and uses VCG pricing. We prove that DR-VCG elicits truthful bids, incentivizes honest preparation efforts, enables efficient computation of allocation and prices. With simple fixed-penalty contracts, the optimization goal of the mechanism is an upper bound on probability that the reduction target is missed. Extensive simulations show that compared to the current mechanism deployed in by SCE, the DR-VCG mechanism achieves higher participation, increased reliability, and significantly reduced total expenses.Comment: full version of paper accepted to IJCAI'1

Dynamic Pricing, Advanced Metering, and Demand Response in Electricity Markets

Presents an overview and analysis of the possible approaches to bringing an active demand side into electricity markets. Part of a series of research reports that examines energy issues facing California

Monitoring-Based Commissioning: Tracking the Evolution and Adoption of a Paradigm-Shifting Approach to Retro-Commissioning

Proceedings of the 2012 ACEEE Summer Study (Panel 4, Paper 1130). Monitoring-based commissioning (MBCx) emphasizes permanent energy performance metering and trending—for diagnosis of energy waste, for savings accounting, and to enable persistence of savings. Emphasis on monitoring represents a paradigm shift for the retro-commissioning (RCx) industry, which has traditionally relied upon test protocols and modeled savings estimates. Since 2004, a major monitoring-based commissioning program at twenty-five California university campuses has evolved to meet the changing needs of university and utility partners. More recently the monitoring-based approach has been adopted by third-party programs in California. We present information on the progression of program design and results for the multiple phases of the original program, along with a look at third-party and other programs adopting similar program features

Allocation, incentives and distortions: the impact of EU ETS emissions allowance allocations to the electricity sector

The allowance allocation under the European Emission trading schemes differs fundamentally from earlier cap and trade programs, like SO2 and NOx in the USA. Because of the iterative nature of negotiations of the overall budget, the allocation also has to follow an iterative process. If power generators anticipate that their current behaviour will affect future allowance allocation, then this can distort today’s decisions. Furthermore, the National Allocation Plans (NAPs) contain multiple provisions dealing with existing installations, what happens to allocation when they close, and allocations to new entrants. We provide a framework to assess the economic incentives and distortions that provisions in NAPs can have on market prices, operation and investment decisions. To this end, we use both analytic models to illustrate the incentives effects and results from numerical simulation runs that estimate the magnitude of impacts from different allocation rules

An exact solution method for binary equilibrium problems with compensation and the power market uplift problem

We propose a novel method to find Nash equilibria in games with binary decision variables by including compensation payments and incentive-compatibility constraints from non-cooperative game theory directly into an optimization framework in lieu of using first order conditions of a linearization, or relaxation of integrality conditions. The reformulation offers a new approach to obtain and interpret dual variables to binary constraints using the benefit or loss from deviation rather than marginal relaxations. The method endogenizes the trade-off between overall (societal) efficiency and compensation payments necessary to align incentives of individual players. We provide existence results and conditions under which this problem can be solved as a mixed-binary linear program. We apply the solution approach to a stylized nodal power-market equilibrium problem with binary on-off decisions. This illustrative example shows that our approach yields an exact solution to the binary Nash game with compensation. We compare different implementations of actual market rules within our model, in particular constraints ensuring non-negative profits (no-loss rule) and restrictions on the compensation payments to non-dispatched generators. We discuss the resulting equilibria in terms of overall welfare, efficiency, and allocational equity

Demand Response Strategy Based on Reinforcement Learning and Fuzzy Reasoning for Home Energy Management

As energy demand continues to increase, demand response (DR) programs in the electricity distribution grid are gaining momentum and their adoption is set to grow gradually over the years ahead. Demand response schemes seek to incentivise consumers to use green energy and reduce their electricity usage during peak periods which helps support grid balancing of supply-demand and generate revenue by selling surplus of energy back to the grid. This paper proposes an effective energy management system for residential demand response using Reinforcement Learning (RL) and Fuzzy Reasoning (FR). RL is considered as a model-free control strategy which learns from the interaction with its environment by performing actions and evaluating the results. The proposed algorithm considers human preference by directly integrating user feedback into its control logic using fuzzy reasoning as reward functions. Q-learning, a RL strategy based on a reward mechanism, is used to make optimal decisions to schedule the operation of smart home appliances by shifting controllable appliances from peak periods, when electricity prices are high, to off-peak hours, when electricity prices are lower without affecting the customer’s preferences. The proposed approach works with a single agent to control 14 household appliances and uses a reduced number of state-action pairs and fuzzy logic for rewards functions to evaluate an action taken for a certain state. The simulation results show that the proposed appliances scheduling approach can smooth the power consumption profile and minimise the electricity cost while considering user’s preferences, user’s feedbacks on each action taken and his/her preference settings. A user-interface is developed in MATLAB/Simulink for the Home Energy Management System (HEMS) to demonstrate the proposed DR scheme. The simulation tool includes features such as smart appliances, electricity pricing signals, smart meters, solar photovoltaic generation, battery energy storage, electric vehicle and grid supply.Peer reviewe

Higher education reform: getting the incentives right

This study is a joint effort by the Netherlands Bureau for Economic Policy Analysis (CPB) and the Center for Higher Education Policy Studies. It analyses a number of `best practices¿ where the design of financial incentives working on the system level of higher education is concerned. In Chapter 1, an overview of some of the characteristics of the Dutch higher education sector is presented. Chapter 2 is a refresher on the economics of higher education. Chapter 3 is about the Australian Higher Education Contribution Scheme (HECS). Chapter 4 is about tuition fees and admission policies in US universities. Chapter 5 looks at the funding of Danish universities through the so-called taximeter-model, that links funding to student performance. Chapter 6 deals with research funding in the UK university system, where research assessments exercises underlie the funding decisions. In Chapter 7 we study the impact of university-industry ties on academic research by examining the US policies on increasing knowledge transfer between universities and the private sector. Finally, Chapter 8 presents food for thought for Dutch policymakers: what lessons can be learned from our international comparison