New dispatching paradigm in power systems including EV charging stations and dispersed generation: A real test case

Electric Vehicles (EVs) are becoming one of the main answers to the decarbonization of the transport sector and Renewable Energy Sources (RES) to the decarbonization of the electricity production sector. Nevertheless, their impact on the electric grids cannot be neglected. New paradigms for the management of the grids where they are connected, which are typically distribution grids in Medium Voltage (MV) and Low Voltage (LV), are necessary. A reform of dispatching rules, including the management of distribution grids and the resources there connected, is in progress in Europe. In this paper, a new paradigm linked to the design of reform is proposed and then tested, in reference to a real distribution grid, operated by the main Italian Distribution System Operator (DSO), e-distribuzione. First, in reference to suitable future scenarios of spread of RES-based power plants and EVs charging stations (EVCS), using Power Flow (PF) models, a check of the operation of the distribution grid, in reference to the usual rules of management, is made. Second, a new dispatching model, involving DSO and the resources connected to its grids, is tested, using an Optimal Power Flow (OPF) algorithm. Results show that the new paradigm of dispatching can effectively be useful for preventing some operation problems of the distribution grids

Attributes of Big Data Analytics for Data-Driven Decision Making in Cyber-Physical Power Systems

Big data analytics is a virtually new term in power system terminology. This concept delves into the way a massive volume of data is acquired, processed, analyzed to extract insight from available data. In particular, big data analytics alludes to applications of artificial intelligence, machine learning techniques, data mining techniques, time-series forecasting methods. Decision-makers in power systems have been long plagued by incapability and weakness of classical methods in dealing with large-scale real practical cases due to the existence of thousands or millions of variables, being time-consuming, the requirement of a high computation burden, divergence of results, unjustifiable errors, and poor accuracy of the model. Big data analytics is an ongoing topic, which pinpoints how to extract insights from these large data sets. The extant article has enumerated the applications of big data analytics in future power systems through several layers from grid-scale to local-scale. Big data analytics has many applications in the areas of smart grid implementation, electricity markets, execution of collaborative operation schemes, enhancement of microgrid operation autonomy, management of electric vehicle operations in smart grids, active distribution network control, district hub system management, multi-agent energy systems, electricity theft detection, stability and security assessment by PMUs, and better exploitation of renewable energy sources. The employment of big data analytics entails some prerequisites, such as the proliferation of IoT-enabled devices, easily-accessible cloud space, blockchain, etc. This paper has comprehensively conducted an extensive review of the applications of big data analytics along with the prevailing challenges and solutions

DEFINITION OF AN ASSESSMENT FRAMEWORK FOR PROJECTS OF COMMON INTEREST IN THE FIELD OF SMART GRIDS under the EC "Proposal for a regulation of the European Parliament and of the Council on guidelines for trans-European energy infrastructure"

The document presents the methodology to be adopted in the evaluation of proposals of Smart Grids projects to be awarded the label of Projects of Common Interest. The label, established by the Regulation 347/2013 on Guidelines for trans-European energy infrastructure, aims at identifying trans-national projects with a significant added-value for the EU as a whole. The methodology has been developed by JRC within the Expert Group 4 "Infrastructure" of the EC Smart Grids Task Force and finally approved by the Expert Group in July 2012.JRC.F.3-Energy Security, Systems and Marke

The economic impact of electricity losses [WP]

Although electricity losses constitute an important, but inevitable, amount of wasted resources (and a share that has to be funded), they remain one of the lesser known parts of an electricity system, and this despite the fact that the decisions of generators, transmission and distribution system operators and consumers all impact on them. In this paper we analyse the effects of such losses from two perspectives: from that of consumption or outflows and from that of generation or inflows. Given that end-user consumption varies across the day, consumption has direct implications for electricity losses. Indeed, demand-side management policies seek to encourage consumers to use less energy during peak hours and to reduce network congestion. At the same time, from the perspective of generation, the recent growth in distributed generation has modified the traditional, unidirectional, downward flows in electricity systems. This affects losses as energy is produced in the lower voltage network, which is closer to points of consumption. In this paper we evaluate the impact of consumption patterns and different generation technologies on energy losses. To do so, we draw on data from a real electricity system with a high level of renewable penetration, namely, that of Spain between 2011 and 2013. To the best of our knowledge, this is the first paper to analyse the real impact of consumption and the effect of each generation technology on energy losses, offering an opportunity to evaluate the potential benefits of demand-side management policies and distributed generation. Based on our results, we make a number of regulatory recommendations aimed at exploiting to the full these potential benefits. Our results should serve as a baseline for countries that are in the early stages of implementing these policies

A Comprehensive Review of Congestion Management in Power System

In recent decades, restructuring has cut across all probable domains, involving the power supply industry. The restructuring has brought about considerable changes whereby electricity is now a commodity and has become a deregulated one. These competitive markets have paved the way for countless entrants. This has caused overload and congestion on transmission lines. In addition, the open access transmission network has created a more intensified congestion issue. Therefore, congestion management on power systems is relevant and central significance to the power industry. This manuscript review few congestion management techniques, consists of Reprogramming Generation (GR), Load Shedding, Optimal Distributed Generation (DG) Location, Nodal Pricing, Free Methods, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Fuzzy Logic System Method, as well as Additional Renewable Energy Sources. In this manuscript a review work is performed to unite the entire publications on congestion management

A Comprehensive Review of Congestion Management in Power System

In recent decades, restructuring has cut across all probable domains, involving the power supply industry. The restructuring has brought about considerable changes whereby electricity is now a commodity and has become a deregulated one. These competitive markets have paved the way for countless entrants. This has caused overload and congestion on transmission lines. In addition, the open access transmission network has created a more intensified congestion issue. Therefore, congestion management on power systems is relevant and central significance to the power industry. This manuscript review few congestion management techniques, consists of Reprogramming Generation (GR), Load Shedding, Optimal Distributed Generation (DG) Location, Nodal Pricing, Free Methods, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Fuzzy Logic System Method, as well as Additional Renewable Energy Sources. In this manuscript a review work is performed to unite the entire publications on congestion management

Analysis and Operation of Smart Grids with Electric Vehicles

Hoy en día la creciente preocupación por temas medioambientales está llevando a muchos países a tomar medidas que permitan un uso más racional de la energía y un futuro más sostenible. La mejora de la eficiencia de los sistemas y el uso de recursos renovables son algunos puntos sobre los que se debe trabajar para poder atajar las consecuencias de los gases de efecto invernadero, principal responsable del cambio climático. En relación con esto, el sector eléctrico es el uno de los más importantes responsables de emisiones nocivas a la atmósfera seguido por el sector del transporte. Su fuerte dependencia en los combustibles fósiles, particularmente el petróleo y sus derivados, justifica esta última afirmación. Por este motivo, la movilidad mediante vehículos eléctricos está atrayendo la atención de empresas, países y grupos de investigación, como una medida importante para poder hacer frente a las consecuencias negativas derivadas del uso actual de la energía. Resulta claro que la introducción del vehículo eléctrico afectará de manera importante a la operación, gestión y planificación de los sistemas eléctricos actuales. En primer lugar, será necesario tener en cuenta un consumo eléctrico adicional, la carga de las baterías de los vehículos eléctricos. En una primera etapa, donde el número de vehículos desplegados por las ciudades sea reducido no serán necesarias medidas especiales. Sin embargo, en un futuro con miles de vehículos, una mala gestión de la carga puede llevar a problemas técnicos de congestión en las líneas o niveles de tensión no admisibles. Por otra parte, su adecuada integración requiere que los sistemas eléctricos existentes tiendan a incorporar las tecnologías de la información y comunicación más avanzadas, mejores dispositivos de medida, una adecuada infraestructura para carga y descarga así como una mayor presencia de energías renovables. En definitiva, los sistemas eléctricos han de incorporar más inteligencia, ser más sostenibles, eficientes y seguros, en otras palabras, han de tender hacia el concepto de “smart grid”. De esta forma, esta tesis trata de cubrir puntos relevantes en relación la integración satisfactoria de los vehículos eléctricos en las redes eléctricas del futuro. Los aspectos tratados son la gestión de la demanda, la resolución de problemas técnicos y el papel de una nueva entidad gestora de vehículos eléctricos, el agregador. La gestión de la demanda hace referencia a estrategias específicas que tratan de cambiar los patrones de consumo actuales hacia otros comportamientos que permitan un funcionamiento más eficiente del sistema eléctrico. De esta forma, se tiene como objetivo reducir la demanda de electricidad de forma general o bien desplazar dicha demanda hacia otros periodos de tiempo más favorables. Para conseguir esto, es necesario proporcionar algún tipo de incentivo a los consumidores para que puedan modificar sus hábitos o planificar sus actividades de otra manera. En esta tesis, se propone usar señales de precio para provocar ese cambio. El resultado es una curva de demanda más plana que permite aprovechar mejor la infraestructura existente y los recursos de generación disponibles, retrasando ulteriores planificaciones. Desde el punto vista del consumidor, los costes de la energía son menores. En ausencia de medidas correctoras, la presencia de vehículos provocará en el futuro problemas técnicos en el sistema eléctrico. Con vistas a proporcionar solución los problemas más comunes de índole técnica, dos herramientas se desarrollan en esta tesis, un problema centralizado que despacha a los generadores de la red y un algoritmo que hace uso de los vehículos eléctricos. Ambos enfoques permiten aliviar las congestiones de manera efectiva en determinadas situaciones. En particular, la inyección de potencia o la carga de los vehículos en ciertos nudos de la red se propone como una medida posible para llevar al sistema a un estado seguro. Finalmente, se plantea una estrategia que permite la maximización de los beneficios de un agente agregador que gestiona la carga y la descarga de los vehículos. Como consecuencia de su aplicación, los conductores ven sus necesidades de movilidad satisfechas a la vez que los costes de carga se reducen. De esta forma, la carga se producirá en las horas nocturnas donde los costes de la energía son normalmente más pequeños y la descarga tendrá lugar en las horas donde hay picos de demanda. Dicha estrategia permite a los agregadores disponer de una herramienta útil a la hora de participar en los mercados de energía eléctrica. Su aplicación es ilustrada a través de un algoritmo de liquidación de mercado en el que además de los elementos comunes presentes hoy en día en los mercados eléctricos, la introducción de agentes agregadores es también tenida en cuenta

CO2 content of electricity losses

Countries are implementing policies to develop greener energy markets worldwide. In Europe, the ¨2030 Energy and Climate Package¨ asks for further reductions of green house gases, renewable sources integration, and energy efficiency targets. But the polluting intensity of electricity may be different in average than when considering market inefficiencies, in particular losses, and therefore the implemented policy must take those differences into account. Precisely, herein we study the importance in terms of CO2 emissions the extra amount of energy necessary to cover losses. With this purpose we use Spanish market and system data with hourly frequency from 2011 to 2013. Our results show that indeed electricity losses significantly explain CO2 emissions, with a higher CO2 emissions rate when covering losses than the average rate of the system. Additionally, we find that the market closing technologies used to cover losses have a positive and significant impact on CO2 emissions: when polluting technologies (coal or combined cycle) close the market, the impact of losses on CO2 emissions is high compared to the rest of technologies (combined heat and power, renewables or hydropower). To the light of these results we make some policy recommendations to reduce the impact of losses on CO2 emissions