PowerSimulationsDynamics.jl -- An Open Source Modeling Package for Modern Power Systems with Inverter-Based Resources

The inclusion of inverter-based resources from renewable energy creates new challenges for the stability and transient behavior of power systems which are best understood by studying their dynamic responses through simulation. In this paper, we develop an open source simulation toolbox, PowerSimulationDynamics.jl, to study the dynamic response of a balanced system with high penetration of inverter-based resources. PowerSimulationDynamics.jl is implemented in the Julia language and features a rich library of synchronous generator components and inverter models. In addition, it allows the study of both quasi-static phasors that employ an admittance matrix representation for the network and electromagnetic dq models that use a dynamic representation of the network. Case studies and validation exercises show that PowerSimulationDynamics.jl results closely match Quasi-Static Phasor (QSP) tools like Siemens PSSe, ANDES, and wave-form Electro-magnetic Transient (EMT) simulations like PSCA

Small-Scale Communities Are Sufficient for Cost- and Data-Efficient Peer-to-Peer Energy Sharing

Due to ever lower cost, investments in renewable electricity generation and storage have become more attractive to electricity consumers in recent years. At the same time, electricity generation and storage have become something to share or trade locally in energy communities or microgrid systems. In this context, peer-to-peer (P2P) sharing has gained attention, since it offers a way to optimize the cost-benefits from distributed resources, making them financially more attractive. However, it is not yet clear in which situations consumers do have interests to team up and how much cost is saved through cooperation in practical instances. While introducing realistic continuous decisions, through detailed analysis based on large-scale measured household data, we show that the financial benefit of cooperation does not require an accurate forecasting. Furthermore, we provide strong evidence, based on analysis of the same data, that even P2P networks with only 2--5 participants can reach a high fraction (96% in our study) of the potential gain, i.e., of the ideal offline (i.e., non-continuous) achievable gain. Maintaining such small communities results in much lower associated costs and better privacy, as each participant only needs to share its data with 1--4 other peers. These findings shed new light and motivate requirements for distributed, continuous and dynamic P2P matching algorithms for energy trading and sharing

Challenges on Decarbonization of Electric Power Systems

Electric power systems are one of the fundamental pillars of modern society. Power systems require careful planning to ensure enough capacity for future electric demand, and simultaneously requiring meticulous operation to maintain a continuous supply-demand balance, which ensures a reliable and stable system. In this complex context, our electric systems are being decarbonized, moving away from fossil fuel based systems into more renewable ones, with larger contribution from wind and solar energy sources, reducing emissions of greenhouse gases which negatively contribute to climate change. The consequences of this transition to renewable systems are multi-fold. First, renewable sources, like wind and solar, are variable and face uncertainty that complicates future planning of these energy systems. Second, the integration of Distributed Energy Resources (DERs), like rooftop photovoltaic systems, changes the paradigm of how our electric system operates, where power was once only generated in large generating units, and delivered via high-voltage transmission lines to the demand hubs—commonly large cities. Traditional energy sources, such as coal or gas, inject power via synchronous generators, however renewable resources are mostly interfaced into the grid using power electronics, that creates a fundamental difference in how electricity is produced. All of these changes are affecting our understanding of the grid, in multiple aspects and with different problems, which force us to reevaluate the tools and techniques used to study power systems.This work focus on understanding the different challenges that are occurring in power systems due to the integration of variable Renewable Energy Sources (RESs), both in planning studies and day-to-day system operation. Thus, the main goal of this thesis is to study and analyze the changes in power systems with increasing shares of RESs, across multiple time- and space- scales.The first part of this dissertation investigates dynamic simulations that are necessary for analyzing power system stability and dynamic response in the presence of Inverter-based Resources (IBRs). We introduce the Julia package PowerSimulationDynamics.jl to study the effects of load and line modeling when grid-forming and grid-following inverters. A discussion is presented between phasor-type and electromagnetic transient-type simulations. Results confirm that rooted assumptions in transient simulations may not be valid in systems with large presence of IBRs. By enabling PowerSimulationDynamics.jl as a flexible software tool, we discover that more detailed network and load models are becoming more necessary to properly assess stability of future power systems, as the integration of IBRs increases.The second part focuses on DER investment in peer-to-peer and sharing economy setups. In particular, we propose an optimization model for distributed rooftop Photovoltaic (PV) investment to analyze how PV investment decisions can vary when consumers are subjected to different tariff schemes. Our results showcase how peer-to-peer tariff schemes, rather than traditional net-metering or feed-in-tariffs, can promote investment in rooftop PV.Finally, the third part of this dissertation discuss the Switch expansion planning model, and specifically the effects of Electric Vehicles (EVs) flexibility in the future Western Electricity Coordinating Council (WECC) grid. Motivated by future decarbonization scenarios in California, we added a new EV module to the Switch model to study the influence of EV flexibility in installed capacity in WECC by 2050. Our results confirms that demand flexibility can reduce system's peak load to defer large investment decisions, achieving savings in both planning and operation costs

Continuous-time echo state networks for predicting power system dynamics

With the growing penetration of converter-interfaced generation in power systems, the dynamical behavior of these systems is rapidly evolving. One of the challenges with converter-interfaced generation is the increased number of equations, as well as the required numerical timestep, involved in simulating these systems. Within this work, we explore the use of continuous-time echo state networks as a means to cheaply, and accurately, predict the dynamic response of power systems subject to a disturbance for varying system parameters. We show an application for predicting frequency dynamics following a loss of generation for varying penetrations of grid-following and grid-forming converters. We demonstrate that, after training on 20 solutions of the full-order system, we achieve a median nadir prediction error of 0.17 mHz with 95% of all nadir prediction errors within ±4 mHz. We conclude with some discussion on how this approach can be used for parameter sensitivity analysis and within optimization algorithms to rapidly predict the dynamical behavior of the system