We study the interplay between social ties and financial transactions made through a recent cryptocurrency called G˘1. It has the particularity of combining the usual transaction record with a reliable network of identified users. This gives the opportunity to observe exactly who sent money to whom over a social network. This social network is a key piece of this cryptocurrency, which therefore puts much effort in ensuring that nodes correspond to unique, well identified, real living human users, linked together only if they met at least once in real world. Using this data, we study how social ties impact the structure of transactions and conversely. We show that users make transactions almost exclusively with people they are connected with in the social network. Instead, they tend to build social connections with people they will never make transactions with.
This paper presents a graph-based forward looking algorithm applied to distribution planning in the context of distributed photovoltaic penetration. We study the target hosting capacity problem where the objective is to find the least-cost sequence of system upgrades to reach a predefined hosting capacity target value. We show that commonly used short-term cost minimization approaches often lead to suboptimal long-term solutions. By comparing our method against such myopic techniques on real distribution systems, we show that our algorithm is able to reduce the overall integration costs by looking at future decisions. Because hosting capacity is hard to compute, this problem requires efficient methods to search the space. We demonstrate that heuristics using domain-specific knowledge can be efficiently used to improve the algorithm performance, such that real distribution systems can be studied.
In the context of the smart grid, we propose in this paper an algorithm that forms coalitions of agents, called prosumers, that both produce and consume. It is designed to be used by aggregators that aim at selling aggregated surplus of production of the prosumers they control. We rely on real weather data sampled across stations of a given territory in order to simulate realistic production and consumption patterns for each prosumer. This enables us to capture geographical correlations among the agents while preserving the diversity due to different behaviors. As aggregators are bound to the market operator by a contract, they seek to maximize their offer while minimizing their risk. The proposed graph-based algorithm takes the underlying correlation structure of the agents into account and outputs coalitions with both high productivity and low variability. We show that the resulting diversified coalitions are able to generate higher benefits on a constrained energy market, and are more resilient to random failures of the agents.
Distributed photovoltaic systems (DPV) can cause adverse grid impacts, including voltage or thermal violations. The installed capacity at which violations first occur and above which would require system upgrades is called the hosting capacity. Current methods for determining hosting capacity tend to be conservative by either only considering infrequent worst-case snapshots in time and/or only capturing coarse time and spatial resolution. Additionally, current hosting capacity methods do not accurately capture the time-dependence making them unable to capture the behavior of voltage regulating equipment and of some advanced controls mitigations. This can trigger delays from unnecessary engineering analysis or deter solar installations in areas that are actually suitable. We propose a quasi-static-time-series (QSTS) based PV hosting capacity methodology to address these issues. With this approach, we conduct power flow analysis over the course of a full year, to capture time-varying parameters and control device actions explicitly. We show that this approach can more fully capture grid impacts of DPV than traditional methods.
We propose here a control based method for improving the storage placement in a prosumer network where generators and loads are stochastic. The particularity of our approach is to use the energy required for stabilizing the system as a criterion for the optimization of the storage placement. We use a linearized AC model for the dynamic of the system and we consider the control inputs as being the amount of power injected in the grid by the batteries. Because a prosumer may both consume and produce electricity using renewable generators (DER), power imbalances are very likely, such that the system might lose synchrony and require the controlled actions of batteries. We show that the amount of energy that has to be used is this kind of situation depends on the storage locations. We chose the placement that minimizes, over the perturbations that may occur, the average energy required for driving the system back to equilibrium. For this purpose, we propose and validate an algorithm based on a submodular optimization that includes the physical constraints of the system and has a worst case guarantee.
In a smart grid environment, we study the coalition formation of prosumers that aim at entering the energy market. It is paramount for the grid operation that the energy producers are able to sustain the grid demand in terms of stability and a minimum production requirement. We design an algorithm that seeks to form coalitions that will meet both of these requirements: a minimum energy level for the coalitions and a steady production level, which leads to finding uncorrelated sources of energy to form a coalition. We propose an algorithm that uses graph tools such as correlation graphs or clique percolation to form coalitions that meet such complex constraints. We validate the algorithm against a random procedure and show that, it not only performs better in terms of social welfare for the power grid, but also that it is more robust against unforeseen production variations due to changing weather conditions for instance.
This thesis is devoted to the study of agents called prosumers because they can, from renewable, both produce and consume electricity. If their production exceeds their own needs, they are looking to sell their surplus on electricity markets. We propose to model these prosumers from meteorological data, which has allowed us to highlight non trivial spatial and temporal correlations. This is of great importance for aggregators that form portfolios of equipments to sell services to the network operator. As an aggregator is bound by a contract with the operator, it can be subject to penalties if it does not fulfill its role. We show that these correlations impact the stability of aggregates, and therefore the risk taken by the aggregators. We propose an algorithm minimizing the risk of the aggregations, while maximizing the expected gain. The placement of storage devices in a network where generators and loads are stochastic and not fixed is complex. We propose to answer this question with control theory. We model the electrical system as a network of coupled oscillators, whose phase angles dynamics is an approximation of the actual dynamics of the system. The goal is to find the subset of nodes in the graph that, during a disturbance of the system, allows returning to equilibrium if the right signals are injected and this with a minimum energy. We propose an algorithm to find a near optimal placement to minimize the average energy control