Liberalisation of network industries : Is the electricity sector an exception to the rule?

For quite a long time, network industries used to be regarded as (natural) monopolies. This was due to these industries having some special characteristics. Network externalities and economies of scale in particular justified the (natural) monopoly thesis. Recently, however, a trend towards deregulation of such industries has been observed. This trend started with the successful introduction of competition in the telecommunications sector. The main reason behind this success is that the economies of scale have disappeared as a result of emerging new technologies. The successful deregulation in telecommunications is in line with micro-economic theory, which predicts an increase in efficiency and lower prices when markets are opened up to competition. The success in the telecommunications sector is often used as an argument for opening up other network industries to competition as well. In this paper we analyse whether this reasoning can be transposed to the electricity sector. It is argued that the two sectors, electricity and telecommunications, are similar in that they are both network industries which used to be characterised by economies of scale, and that technological progress might have put an end to this scale effect. There are however certain differences. Firstly, technological progress on the supply side was accompanied by a strong growth in demand in the telecommunications sector. This demand side effect is absent in electricity. Moreover, due to physical characteristics, the electricity sector seems to be more complicated: in order to introduce competition in the sector, it has to be split up into subsectors (production, transmission, distribution and supply). Competition is introduced in production and supply, transmission and distribution remain monopolies. This splitting up creates a new kind of costs, the so-called transaction costs. The paper is centered around two issues: (a) are the basic assumptions behind the theoretical model of the perfectly free market met in the deregulated subsectors? and (b) do the transaction costs (partly) offset possible price decreases in competitive segments ? There is no hard evidence that the hypotheses behind the theoretical model are met in the electricity sector, and there are strong indications that these transaction costs might be substantial. Moreover, in addition to the deregulation process, the electricity sector is also subject to other changes such as the internalisation of externalities (see the Kyoto protocol) and the debate on nuclear energy. These elements could exert an upward pressure on prices. Since electricity is ubiquitous, the deregulation process should be closely monitored.Welfare economics; market structure and pricing; organizational behaviour, transaction costs, property rights, Electric Utilities, Telecommunications.

Merchant Transmission Investment

We examine the performance attributes of a merchant transmission investment framework that relies on �market driven� transmission investment to provide the infrastructure to support competitive wholesale markets for electricity. Under a stringent set of assumptions, the merchant investment model appears to solve the natural monopoly problem and the associated need for regulating transmission companies traditionally associated with electric transmission networks. We expand the model to incorporate imperfection in wholesale electricity markets, lumpiness in transmission investment opportunities, stochastic attributes of transmission networks and associated property rights definition issues, the effects of the behaviour system operators and transmission owners on transmission capacity and reliability, co-ordination and bargaining considerations, forward contract, commitment and asset specificity issues. This significantly undermines the attractive properties of the merchant investment model. Relying primarily on a market driven investment framework to govern investment is likely to lead to inefficient investment decisions and undermine the performance of competitive markets

Transforming Energy Networks via Peer to Peer Energy Trading: Potential of Game Theoretic Approaches

Peer-to-peer (P2P) energy trading has emerged as a next-generation energy management mechanism for the smart grid that enables each prosumer of the network to participate in energy trading with one another and the grid. This poses a significant challenge in terms of modeling the decision-making process of each participant with conflicting interest and motivating prosumers to participate in energy trading and to cooperate, if necessary, for achieving different energy management goals. Therefore, such decision-making process needs to be built on solid mathematical and signal processing tools that can ensure an efficient operation of the smart grid. This paper provides an overview of the use of game theoretic approaches for P2P energy trading as a feasible and effective means of energy management. As such, we discuss various games and auction theoretic approaches by following a systematic classification to provide information on the importance of game theory for smart energy research. Then, the paper focuses on the P2P energy trading describing its key features and giving an introduction to an existing P2P testbed. Further, the paper zooms into the detail of some specific game and auction theoretic models that have recently been used in P2P energy trading and discusses some important finding of these schemes.Comment: 38 pages, single column, double spac

Reform and Regulation of the Electricity Sectors in Developing Countries

Reform and Regulation of the Electricity Sectors in Developing Countrie

Attributes of Big Data Analytics for Data-Driven Decision Making in Cyber-Physical Power Systems

Big data analytics is a virtually new term in power system terminology. This concept delves into the way a massive volume of data is acquired, processed, analyzed to extract insight from available data. In particular, big data analytics alludes to applications of artificial intelligence, machine learning techniques, data mining techniques, time-series forecasting methods. Decision-makers in power systems have been long plagued by incapability and weakness of classical methods in dealing with large-scale real practical cases due to the existence of thousands or millions of variables, being time-consuming, the requirement of a high computation burden, divergence of results, unjustifiable errors, and poor accuracy of the model. Big data analytics is an ongoing topic, which pinpoints how to extract insights from these large data sets. The extant article has enumerated the applications of big data analytics in future power systems through several layers from grid-scale to local-scale. Big data analytics has many applications in the areas of smart grid implementation, electricity markets, execution of collaborative operation schemes, enhancement of microgrid operation autonomy, management of electric vehicle operations in smart grids, active distribution network control, district hub system management, multi-agent energy systems, electricity theft detection, stability and security assessment by PMUs, and better exploitation of renewable energy sources. The employment of big data analytics entails some prerequisites, such as the proliferation of IoT-enabled devices, easily-accessible cloud space, blockchain, etc. This paper has comprehensively conducted an extensive review of the applications of big data analytics along with the prevailing challenges and solutions

On the interaction between Autonomous Mobility-on-Demand systems and the power network: models and coordination algorithms

We study the interaction between a fleet of electric, self-driving vehicles servicing on-demand transportation requests (referred to as Autonomous Mobility-on-Demand, or AMoD, system) and the electric power network. We propose a model that captures the coupling between the two systems stemming from the vehicles' charging requirements and captures time-varying customer demand and power generation costs, road congestion, battery depreciation, and power transmission and distribution constraints. We then leverage the model to jointly optimize the operation of both systems. We devise an algorithmic procedure to losslessly reduce the problem size by bundling customer requests, allowing it to be efficiently solved by off-the-shelf linear programming solvers. Next, we show that the socially optimal solution to the joint problem can be enforced as a general equilibrium, and we provide a dual decomposition algorithm that allows self-interested agents to compute the market clearing prices without sharing private information. We assess the performance of the mode by studying a hypothetical AMoD system in Dallas-Fort Worth and its impact on the Texas power network. Lack of coordination between the AMoD system and the power network can cause a 4.4% increase in the price of electricity in Dallas-Fort Worth; conversely, coordination between the AMoD system and the power network could reduce electricity expenditure compared to the case where no cars are present (despite the increased demand for electricity) and yield savings of up $147M/year. Finally, we provide a receding-horizon implementation and assess its performance with agent-based simulations. Collectively, the results of this paper provide a first-of-a-kind characterization of the interaction between electric-powered AMoD systems and the power network, and shed additional light on the economic and societal value of AMoD.Comment: Extended version of the paper presented at Robotics: Science and Systems XIV, in prep. for journal submission. In V3, we add a proof that the socially-optimal solution can be enforced as a general equilibrium, a privacy-preserving distributed optimization algorithm, a description of the receding-horizon implementation and additional numerical results, and proofs of all theorem