Techno-economic models in Smart Grids: Demand side flexibility optimization for bidding and scheduling problems

摘要

Summary of the thesisIntroduction of power-intensive appliances such as electric vehicle chargers and induction cooktops, as well as technologies for local renewable electricity generation from solar panels and wind turbines will provide challenges for distribution in the coming years. High power peaks, rapid power changes and less predictability will increase the need for transmission capacity and reserves. Traditionally, such problems are met with costly investments in new capacity. An alternative approach is to use flexibility from the end users, which means that generation and consumption of electricity is changed as a response to prices or other signals. Introduction of batteries in buildings, advanced metering infrastructure (AMI) and the Internet of Things (IoT) increase the potential for demand side flexibility. Altogether, these technologies constitute the concept denoted the Smart Grid.To realize this increased flexibility potential, financial incentives must be introduced. Major changes are therefore expected in the electricity market in the coming years, including introduction of new, innovative contract types and business models, changes in market designs and the establishment of new market roles.To maximize the benefit of demand side flexibility, there is a need for development of new decision support models. This thesis proposes and analyzes models for trading in different markets and for the scheduling of flexible devices in an operational situation. The models are based on operations research. The decision problems are mathematically formulated, and a particular focus is on how to handle uncertain parameters, such as consumption, generation and market prices. Stochastic programming is used for this purpose.The thesis consists of four articles. In Article 1 a basic model is established where flexibility is divided into different classes. The article analyzes a prosumer in the retail market, where flexibility gives cost savings by exploiting price variations over a day, between energy carriers and by reducing the demand charge at the grid tariffs. In Article 2 several prosumers are coordinated via an aggregator who buys and sells electricity in a spot market and where imbalances are settled in a balancing market. Article 3 focuses on flexibility trade, where the value of an aggregated flexibility portfolio is maximized by trading in three sequential markets. The last article analyzes the decision problem to a service provider who operates a charging site for electric vehicles, where the capacity is limited. All articles contain case studies that have been conducted in close cooperation with companies in the Norwegian electricity market.