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

Capturing Transmission and Distribution Connected Wind Energy Variability

Although renewable energy provides a viable solution to address ongoing challenges of the economy and the environment in modern power systems, the variable generation of this technology results in major technical challenges for system operators. This issue is becoming more severe as the penetration of renewable generation is increasing. This dissertation addresses the variability challenge of renewable energy resources in transmission and distribution levels of modern power systems. For transmission level, this dissertation focuses on wind generation fluctuation. Three methods of reducing wind generation fluctuation are investigated from an economic perspective, including (a) dumping the wind generation, (b) using battery energy storage system (BESS) to capture excess wind generation, and (c) a hybrid method combining these two approaches. The economic viability of the hybrid method is investigated via a developed linear programming model with the objective of profit maximization, which in extreme cases will converge to one of the other methods. This dissertation further proposes a BESS planning model to minimize wind generation curtailment and accordingly maximize the deployment of this viable technology. For distribution level, this dissertation investigates the issue of microgrids net load variability stemmed from renewable generation. This is accomplished by investigating and comparing two options to control the microgrid net load variability resulted from high penetration of renewable generation. The proposed options include (a) Local management, which limits the microgrid net load variability in the distribution level by enforcing a cap constraint, and (b) Central management, which recommends on building a new fast response generation unit to limit aggregated microgrid net load variability in the distribution level. Moreover, the aggregated microgrid net load variability is studied in this dissertation by considering the distribution system operator (DSO). DSO would calculate the microgrids net load in day-ahead basis by receiving the aggregated demand bid curves. Accordingly, two models are proposed considering the DSO role in managing the grid operation and market clearing. The first one is security-constrained distribution system operation model which maximizes the system social welfare. The system security consists of distribution line outage as well as microgrid islanding. None of these two security events are in the control of the DSO, so associated uncertainties are considered in the problem modeling. The second one aims at reconfiguring the distribution grid, i.e., a grid topology control, using the smart switches in order to maximize the system social welfare and support grid reliability. The conducted numerical simulations demonstrate the effectiveness and the merits of the proposed models in identifying viable and economic options in capturing renewable generation variability