Demand management for home energy networks using cost-optimal appliance scheduling

This paper uses problem decomposition to show that optimal dynamic home energy prices can be used to reduce the cost of supplying energy, while at the same time reducing the cost of energy for the home users. The paper makes no specific recommendations on the nature of energy pricing, but shows that energy prices can normally be found that not only result in optimal energy consumption schedules for the energy provider's problem and are economically viable for the energy provider, but also reduce total users energy costs. Following this, the paper presents a heuristic real-time algorithm for demand management using home appliance scheduling. The presented algorithm ensures users' privacy by requiring users to only communicate their aggregate energy consumption schedules to the energy provider at each iteration of the algorithm. The performance of the algorithm is evaluated using a comprehensive probabilistic user demand model which is based on real user data from energy provider E.ON. The simulation results show potential reduction of up to 17% of the mean peak-to-average power estimate, reducing the user daily energy cost for up to 14%

Market-based coordination for domestic demand response in low-carbon electricity grids

Efforts towards a low carbon economy are challenging the electricity industry. On the supply-side, centralised carbon-intensive power plants are set to gradually decrease their contribution to the generation mix, whilst distributed renewable generation is to successively increase its share. On the demand-side, electricity use is expected to increase in the future due to the electrification of heating and transport. Moreover, the demand-side is to become more active allowing end-users to invest in generation and storage technologies, such as solar photovoltaics (PV) and home batteries. As a result, some network reinforcements might be needed and instrumentation at the users’ end is to be required, such as controllers and home energy management systems (HEMS). The electricity grid must balance supply and demand at all times in order to maintain technical constraints of frequency, voltage, and current; and this will become more challenging as a result of this transition. Failure to meet these constraints compromises the service and could damage the power grid assets and end-users’ appliances. Balancing generation, although responsive, is carbon-intensive and associated with inefficient asset utilisation, as these generators are mostly used during peak hours and sit idle the rest of the time. Furthermore, energy storage is a potential solution to assist the balancing problem in the presence of non-dispatchable low-carbon generators; however, it is substantially expensive to store energy in large amounts. Therefore, demand response (DR) has been envisioned as a complementary solution to increase the system’s resilience to weather-dependent, stochastic, and intermittent generation along with variable and temperature-correlated electric load. In the domestic setting, operational flexibility of some appliances, such as heaters and electric cars, can be coordinated amongst several households so as to help balance supply and demand, and reduce the need of balancing generators. Against this background, the electricity supply system requires new organisational paradigms that integrate DR effectively. Although some dynamic pricing schemes have been proposed to guide DR, such as time of use (ToU) and real-time pricing (RTP), it is still unclear how to control oscillatory massive responses (e.g., large fleet of electric cars simultaneously responding to a favourable price). Hence, this thesis proposes an alternative approach in which households proactively submit DR offers that express their preferences to their respective retailer in exchange for a discount. This research develops a computational model of domestic electricity use, and simulates appliances with operational flexibility in order to evaluate the effects and benefits of DR for both retailers and households. It provides a representation for this flexibility so that it can be integrated into specific DR offers. Retailers and households are modelled as computational agents. Furthermore, two market-based mechanisms are proposed to determine the allocation of DR offers. More specifically, a one-sided Vickrey-Clarke-Groves (VCG)-based mechanism and penalty schemes were designed for electricity retailers to coordinate their customers’ DR efforts so as to ameliorate the imbalance of their trading schedules. Similarly, a two-sided McAfee-based mechanism was designed to integrate DR offers into a multi-retailer setting in order to reduce zonal imbalances. A suitable method was developed to construct DR block offers that could be traded amongst retailers. Both mechanisms are dominant-strategy incentive-compatible and trade off a small amount of economic efficiency in order to maintain individual rationality, truthful reporting, weak budget balance and tractable computation. Moreover, privacy preserving is achieved by including computational agents from the independent system operator (ISO) as intermediaries between each retailer and its domestic customers, and amongst retailers. The theoretical properties of these mechanisms were proved using worst-case analysis, and their economic effects were evaluated in simulations based on data from a survey of UK household electricity use. In addition, forecasting methods were assessed on the end-users’ side in order to make better DR offers and avoid penalties. The results show that, under reasonable assumptions, the proposed coordination mechanisms achieve significant savings for both end-users and retailers, as they reduce the required amount of expensive balancing generation

Government Dilemmas in the Private Provision of Public Goods

Following his graduation, Arjen Mulder (1970, MSc Erasmus University)has worked as an energy sector economist at the Netherlands Economic Institute (NEI, nowadays Ecorys). At NEI, he became more and more interested in industrial organisation and market working. He started lecturing at the RSM in 1998, and quit his job as an economist to become a PhD student at the Public Management Department (nowadays Department of Business-Society Management) at the RSM in the year 2000. He is currently assistant professor at the Financial Management department of the Rotterdam School of Management, Erasmus University. His research interests have shifted to the economics of contracts, financial contracting, valuation, and incentives in investment, ventures and partnerships. Abstract in Dutch: Het privaat aanbieden van publieke goederen is een veelbesproken onderwerp, zowel binnen de academia als in de ‘echte wereld’. Vanuit een wetenschappelijk perspectief bestaan er vele drempels voor het fenomeen, zoals het financieringsprobleem, de bereidheid tot betalen bij eindgebruikers, en het zogenaamde ‘free rider’ probleem. Hierdoor wordt bij publieke goederen de overheid vrijwel altijd als logisch alternatief voor de markt wordt gezien. Met een zich uit de markt terugtrekkende overheid als actieve aanbieder van goederen en diensten, echter, ontstaat er een hernieuwde aandacht voor het thema ‘privaat aanbieden van publieke goederen’. Dit proefschrift beziet het perspectief van een overheid, en onderzoekt hoe overheden de private sector kunnen stimuleren om te investeren in het aanbieden van publieke goederen. Aangezien vanuit een economisch perspectief ‘dwingende’ maatregelen (lees: regulering) per definitie inefficiënt zijn, richt ik mij op de niet-dwingende maatregelen. Hiermee wordt automatisch een uitwisselingsprobleem geïntroduceerd—dwingende maatregelen zijn het meest voorspelbaar qua uitkomsten, maar minder efficiënt, terwijl niet-dwingende maatregelen het meest efficiënt zijn, maar minder voorspelbaar uitpakken. De keuze voor niet-dwingende beleidsinstrumenten leidt tot een aantal dilemma’s, die direct de complexiteit onderstrepen van de te maken beleidskeuzes. De vier dilemma’s die worden besproken zijn het Beïnvloedbaarheidsdilemma, het Slimme Bestuursdilemma, het Beleidsinstrumentenmixdilemma, en het Gemeenschappelijk Eigendomsdilemma.The private provision of public goods is a much debated topic, both in the academic and the ‘real life’ literature. From an academic perspective, numerous potential pitfalls exist with respect to funding, willingness-to-pay, and the free rider problem. The logical solution to these problems has therefore always been government provision of public goods. In an era where governments withdraw from the market place as active providers of goods and services, however, there is a renewed interest in the private provision of these activities. This thesis takes a governmental perspective, asking how governments can encourage investments in the private provision of public goods. Since from an economic perspective the so-called ‘coercive’ measures (most noteworthy: regulation) are by definition inefficient, I focus on the non-coercive measures. Therewith, a trade-off is introduced between the efficiency and effectiveness of the government intervention—coercive measures are most predictable in their outcomes, but less efficient, whereas non-coercive measures are most efficient, but less predictable. The choice for non-coercive intervention instruments yields a number of dilemmas, illustrating the complexity of the choices to be made. The four dilemmas discussed are the Influenceability Dilemma, the Smart Governance Dilemma, the Policy Portfolio Dilemma, and the Joint Ownership Dilemma

Foundations of Trusted Autonomy

Trusted Autonomy; Automation Technology; Autonomous Systems; Self-Governance; Trusted Autonomous Systems; Design of Algorithms and Methodologie

Transaction Machines – The Infrastructure of Financial Markets

This thesis describes financial markets as complex machines in the broader sense, as systems for organizing informational flows and performing certain functions in regards to the processing of transactions. We focus on the transaction infrastructure of financial markets, on the flow architecture that allows transactions to happen in the first place. First, in order for a financial market to function there needs to be some mechanism for aggregating and matching disparate transactional requests. Another mechanism is then needed in order to untangle and reduce the complexity of overlapping exposures between participants. The history of finance shows us that there are indeed certain patterns and regularities, procedures and mechanisms present in any system that processes financial transactions. The thesis describes this sequence of functions as transaction machines, understood as complex socio-technical systems for the execution of financial transactions. This is achieved by leveraging a specific philosophical account of technology coupled with a computational and evolutionary account of financial markets. We ultimately focus two types of transaction machines, performing the matching and clearing of financial flows, acting as the infrastructure of financial markets. We also provide a sketch for an evolutionary trajectory of these machines, evolving under the demands and needs of marker participants. From medieval fairs to the millisecond electronic platforms of today, transaction machines have gradually transitioned from human-based ‘hardware’ to electronic automated platforms. Moreover, we also describe the complex power dynamics of contemporary transaction machines. In as much as they are the dominant hubs of global financial markets, the thesis argues for the necessity of a more granular account of the functioning and evolution of transaction machines