An axiomatic framework for ex-ante dynamic pricing mechanisms in smart grid

In electricity markets, the choice of the right pricing regime is crucial for the utilities because the price they charge to their consumers, in anticipation of their demand in real-time, is a key determinant of their profits and ultimately their survival in competitive energy markets. Among the existing pricing regimes, in this paper, we consider ex-ante dynamic pricing schemes as (i) they help to address the peak demand problem (a crucial problem in smart grids), and (ii) they are transparent and fair to consumers as the cost of electricity can be calculated before the actual consumption. In particular, we propose an axiomatic framework that establishes the conceptual underpinnings of the class of ex-ante dynamic pricing schemes. We first propose five key axioms that reflect the criteria that are vital for energy utilities and their relationship with consumers. We then prove an impossibility theorem to show that there is no pricing regime that satisfies all the five axioms simultaneously. We also study multiple cost functions arising from various pricing regimes to examine the subset of axioms that they satisfy. We believe that our proposed framework in this paper is first of its kind to evaluate the class of ex-ante dynamic pricing schemes in a manner that can be operationalised by energy utilities

Mean-Field-Type Games in Engineering

A mean-field-type game is a game in which the instantaneous payoffs and/or the state dynamics functions involve not only the state and the action profile but also the joint distributions of state-action pairs. This article presents some engineering applications of mean-field-type games including road traffic networks, multi-level building evacuation, millimeter wave wireless communications, distributed power networks, virus spread over networks, virtual machine resource management in cloud networks, synchronization of oscillators, energy-efficient buildings, online meeting and mobile crowdsensing.Comment: 84 pages, 24 figures, 183 references. to appear in AIMS 201

Agile Market Engineering: Bridging the gap between business concepts and running markets

The agile market engineering process model (AMEP) is built on the insight, that market design and development is a wicked problem. Electronic markets are too complex to be completely designed upfront. Instead, AMEP tries to bridge the gap between theoretic market design and practical electronic market platform development using an agile, iterative approach that relies on early customer feedback and continuous improvement. The AMEP model is complemented by several supporting software artifacts

Investigation of Game-Theoretic Mechanisms for the Valuation of Energy Resources

Electricity systems are facing the pressure to change in response to the effects of new technology, particularly the proliferation of renewable technologies (such as solar PV systems and wind generation) leading to the retirement of traditional generation technologies that provide stabilising inertia. These changes create an imperative to consider potential future market structures to facilitate the participation of distributed energy resources (DERs; such as EVs and batteries) in grid operation. However, this gives rise to general questions surrounding the ethics of market structures and how they could be fairly applied in future electricity systems. Particularly the most basic question "how should electricity be valued and traded" is fundamentally a moral question without any easy answer. We give a survey of philosophical attitudes around such a question, before presenting a series of ways that these intuitions have been cast into mathematics, including: the Vickrey-Clarke-Groves mechanism, Locational Marginal Pricing, the Shapley Value, and Nash bargaining solution concepts. We compared these different methods, and attempted a new synthesis that brought together the best features of each of them; called the 'Generalised Neyman and Kohlberg Value' or the GNK-value for short. The GNK value was developed as a novel bargaining solution concept for many player non-cooperative transferable utility generalised games, and thus it was intrinsically flexible in its application to various aspects of powersystems. We demonstrated the features of the GNK-value against the other mathematical solutions in the context of trading the immediate consumption/generation of power on small sized networks under linear-DC approximation, before extending the computation to larger networks. The GNK value proved to be difficult to compute for large networks but was shown to be approximable for larger networks with a series of sampling techniques and a proxy method. The GNK value was ethically compared to other mechanisms with the unfortunate discovery that it allowed for participants to be left worse-off for participating, violating the ethical notion of 'euvoluntary exchange' and 'individual rationality'; but was offered as an interesting innovation in the space of transferable utility generalised games notwithstanding. For sampling the GNK value, there was a range of new and different techniques developed for stratified random sampling which iteratively minimise newly derived concentration inequalities on the error of the sampling. These techniques were developed to assist in the computation of the GNK value to larger networks, and they were evaluated in the context of sampling synthetic data, and in computation of the Shapley Value of cooperative game theory. These new sampling techniques were demonstrated to be comparable to the more orthodox Neyman sampling method despite not having access to stratum variances

Learning in Evolutionary Environments

The purpose of this work is to present a sort of short selective guide to an enormous and diverse literature on learning processes in economics. We argue that learning is an ubiquitous characteristic of most economic and social systems but it acquires even greater importance in explicitly evolutionary environments where: a) heterogeneous agents systematically display various forms of "bounded rationality"; b) there is a persistent appearance of novelties, both as exogenous shocks and as the result of technological, behavioural and organisational innovations by the agents themselves; c) markets (and other interaction arrangements) perform as selection mechanisms; d) aggregate regularities are primarily emergent properties stemming from out-of-equilibrium interactions. We present, by means of examples, the most important classes of learning models, trying to show their links and differences, and setting them against a sort of ideal framework of "what one would like to understand about learning...". We put a signifiphasis on learning models in their bare-bone formal structure, but we also refer to the (generally richer) non-formal theorising about the same objects. This allows us to provide an easier mapping of a wide and largely unexplored research agenda.Learning, Evolutionary Environments, Economic Theory, Rationality

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

Distributed Task Management in Cyber-Physical Systems: How to Cooperate under Uncertainty?

We consider the problem of task allocation in a network of cyber-physical systems (CPSs). The network can have different states, and the tasks are of different types. The task arrival is stochastic and state-dependent. Every CPS is capable of performing each type of task with some specific state-dependent efficiency. The CPSs have to agree on task allocation prior to knowing about the realized network's state and/or the arrived tasks. We model the problem as a multi-state stochastic cooperative game with state uncertainty. We then use the concept of deterministic equivalence and sequential core to solve the problem. We establish the non-emptiness of the strong sequential core in our designed task allocation game and investigate its characteristics including uniqueness and optimality. Moreover, we prove that in the task allocation game, the strong sequential core is equivalent to Walrasian equilibrium under state uncertainty; consequently, it can be implemented by using the Walras' tatonnement process