Mejora de la estabilidad en sistemas eléctricos de distribución mediante el uso de autos eléctricos como fuentes de inyección de energía

stability in Electrical Distribution Systems (DS) through the use of Electric Vehicle (EV) batteries charging and discharging systems as sources of energy injection that replace the energy produced by Thermal Generators, to satisfy the needs of the demand, maximizing the efficiency and minimizing the use of resources in peak hours. This is done through the application of the Fuzzy Logic (FL) that defines the possible states of the operation, determining an optimal scenario for: loading times of the EV, times of use of the EV and loading a DS of Injection. The study is supported by mathematical simulation in the software MATLAB and its fuzzy toolbox, allowing to analyze and subsequently optimize the delivery of electrical energy to DS, supporting the case studies discussed in this paper, testing the results of stability and feasibility in the system.El presente documento tiene como finalidad la mejora de la estabilidad en Sistemas Eléctricos de Distribución (DS) mediante la utilización de Sistemas Coordinados de carga y descarga de las baterías de los Vehículos Eléctricos (EV) como fuentes de inyección de energía reemplazando a la energía producida por las Generadoras Térmicas, para satisfacer las necesidades de la demanda, maximizando la eficiencia y minimizando la utilización de recursos energéticos en horas consideradas picos. Esto se lo realiza mediante la aplicación de la Lógica Difusa (FL) definiendo los posibles estados de operación del DS, determinando un Escenario Optimo para: tiempos de carga de los EV, tiempos de uso de los EV, carga a Inyectarse al DS. El estudio se respalda mediante la simulación matemática en el software MATLAB y su Fuzzy Toolbox permitiendo analizar y posteriormente optimizar la entrega de energía eléctrica al DS, sustentando los casos de estudio tratados en el presente documento, probando los resultados de estabilidad y factibilidad en el sistema

Resource Planning to Service Restoration in Power Distribution Systems

Whenever there are extreme weather events, electric power distribution systems are generally affected largely because they are highly subject by their constructive nature: overhead networks. In this context, the management of maintenance actions is generally referred to as emergency service order, usually associated with a lack of supply and requiring human intervention. The key issue for the resource planning refers to an estimation of service time that allows for more assertive planning possible. This chapter proposes a predictive modelling of emergency services for resource planning when considering the geographic dispersion of such services and also the time windows that comprise the amount of service time demanded. After presenting the methodological procedures, a case study depicts the application of the proposed method in order to support proactive service routing

Optimizing Service Restoration in Distribution Systems with Uncertain Repair Time and Demand

This paper proposes a novel method to co-optimize distribution system operation and repair crew routing for outage restoration after extreme weather events. A two-stage stochastic mixed integer linear program is developed. The first stage is to dispatch the repair crews to the damaged components. The second stage is distribution system restoration using distributed generators, and reconfiguration. We consider demand uncertainty in terms of a truncated normal forecast error distribution, and model the uncertainty of the repair time using a lognormal distribution. A new decomposition approach, combined with the Progressive Hedging algorithm, is developed for solving large-scale outage management problems in an effective and timely manner. The proposed method is validated on modified IEEE 34- and 8500-bus distribution test systems.Comment: Under review in IEEE Transactions on Power System

Cyber-physical interdependent restoration scheduling for active distribution network via ad hoc wireless communication

This paper proposes a post-disaster cyber-physical interdependent restoration scheduling (CPIRS) framework for active distribution networks (ADN) where the simultaneous damages on cyber and physical networks are considered. The ad hoc wireless device-to-device (D2D) communication is leveraged, for the first time, to establish cyber networks instantly after the disaster to support ADN restoration. The repair and operation crew dispatching, the remote-controlled network reconfiguration and the system operation with DERs can be effectively coordinated under the cyber-physical interactions. The uncertain outputs of renewable energy resources (RESs) are represented by budget-constrained polyhedral uncertainty sets. Through implementing linearization techniques on disjunctive expressions, a monolithic mixed-integer linear programming (MILP) based two-stage robust optimization model is formulated and subsequently solved by a customized column-and-constraint generation (C&CG) algorithm. Numerical results on the IEEE 123-node distribution system demonstrate the effectiveness and superiorities of the proposed CPIRS method for ADN

Aperiodic two-layer energy management system for community microgrids based on blockchain strategy

Regulatory changes in different countries regarding self-consumption and growing public concern about the environment are encouraging the establishment of community microgrids. These community microgrids integrate a large number of small-scale distributed energy resources and offers a solution to enhance power system reliability and resilience. This work proposes a geographically-based split of the community microgrids into clusters of members that tend to have similar consumption and generation profiles, mimicking the most typical layout of cities. Assuming a community microgrid divided into clusters, a two-layer architecture is developed to facilitate the greater penetration of distributed energy resources in an efficient way. The first layer, referred as the market layer, is responsible for creating local energy markets with the aim of maximising the economic benefits for community microgrid members. The second layer is responsible for the network reconfiguration, which is based on the energy balance within each cluster. This layer complies with the IEC 61850 communication standard, in order to control commercial sectionalizing and tie switches. This allows the community microgrid network to be reconfigured to minimise energy exchanges with the main grid, without requiring interaction with the distributed system operator. To implement this two-layer energy management strategy, an aperiodic market approach based on Blockchain technology, and the additional functionality offered by Smart Contracts is adopted. This embraces the concept of energy communities since it decentralizes the control and eliminates intermediaries. The use of aperiodic control techniques helps to overcome the challenges of using Blockchain technology in terms of storage, computational requirements and member privacy. The scalability and modularity of the Smart Contract-based system allow each cluster of members to be designed by tailoring the system to their specific needs. The implementation of this strategy is based on low-cost off-the-shelf devices, such as Raspberry Pi 4 Model B boards, which operate as Blockchain nodes of community microgrid members. Finally, the strategy has been validated by emulating two use cases based on the IEEE 123-node system network model highlighting the benefits of the proposal.Comunidad de Madri

Resilience-oriented design and proactive preparedness of electrical distribution system

Extreme weather events, such as hurricanes and ice storms, pose a top threat to power distribution systems as their frequency and severity increase over time. Recent severe power outages caused by extreme weather events, such as Hurricane Harvey and Hurricane Irma, have highlighted the importance and urgency to enhance the resilience of electric power distribution systems. The goal of enhancing the resilience of distribution systems against extreme weather events can be fulfilled through upgrading and operating measures. This work focuses on investigating the impacts of upgrading measures and preventive operational measures on distribution system resilience. The objective of this dissertation is to develop a multi-timescale optimization framework to provide some actionable resilience-enhancing strategies for utility companies to harden/upgrade power distribution systems in the long-term and do proactive preparation management in the short-term. In the long-term resilience-oriented design (ROD) of distribution system, the main challenges are i) modeling the spatio-temporal correlation among ROD decisions and uncertainties, ii) capturing the entire failure-recovery-cost process, and iii) solving the resultant large-scale mixed-integer stochastic problem efficiently. To deal with these challenges, we propose a hybrid stochastic process with a deterministic casual structure to model the spatio-temporal correlations of uncertainties. A new two-stage stochastic mixed-integer linear program (MILP) is formulated to capture the impacts of ROD decisions and uncertainties on system responses to extreme weather events. The objective is to minimize the ROD investment cost in the first stage and the expected costs of loss of load, DG operation, and damage repairs in the second stage. A dual decomposition (DD) algorithm with branch-and-bound is developed to solve the proposed model with binary variables in both stages. Case studies on the IEEE 123-bus test feeder have shown the proposed approach can improve the system resilience at minimum costs. For an upcoming extreme weather event, we develop a pre-event proactive energy management and preparation strategy such that flexible resources can be prepared in advance. In order to explicitly materialize the trade-off between the pre-event resource allocation cost and the damage loss risk associated with an event, the strategy is modeled a two-stage stochastic mixed-integer linear programming (SMILP) and Conditional Value at-Risk (CVaR). The progressive algorithm is used to solve the proposed model and obtain the optimal proactive energy management and preparation strategy. Numerical studies on the modified IEEE 123-bus test feeder show the effectiveness of the proposed approach to improve the system resilience at different risk levels