Participation of Electric Vehicle Aggregators in Wholesale Electricity Markets: Recent Works and Future Directions

Electric Vehicles are key to reducing carbon emissions while bringing a revolution to the transportation sector. With the massive increase of EVs in road networks and the growing demand for charging services, the electric power grid faces enormous system reliability and operation stability challenges. Demand and supply disparities create inconsistency in the smooth delivery of electrical power. As a potential solution, EVs and their charging infrastructure can be aggregated to prevent the unwanted effects on power systems and also facilitate ancillary services to the power grid. When not need for transportation purposes, EVs can leverage their batteries for power grid services by participating in the electricity market via mechanisms coordinated by system operators. Hence, the market participation of EV infrastructure can help alleviate the power grid stress during peak periods. However, further research is needed to demonstrate the multiple benefits to both EV owners and power grid operators. This paper briefly overviews the existing literature on market participation of EV aggregators, discuss associated challenges and needs, and propose research directions for future research

Spatial flexibility options in electricity market simulation tools: Deliverable D4.3

Project TradeRES - New Markets Design & Models for 100% Renewable Power Systems: https://traderes.eu/about/ABSTRACT: Deliverable D4.3 addresses the spatial flexibility options that are being considered by TradeRES models. D4.3 presents a report describing the spatial flexibility-related modelling components that are already implemented and those that are being designed for integration in TradeRES agent-based models. This report includes the main definitions, concepts and terminology related to spatial flexibility, as means to support the presentation of the specific models that are being developed by the project, namely about flow based market coupling, market spliting, nodal pricing, dynamic line rating, cross border intraday market, cross border reserve market, cross border capacity market, consumer flexibility aggregation, renewable energy aggregation, storage aggregation, electric vehicle aggregation and grid capacity.N/

A Comprehensive Assessment of Vehicle-to-Grid Systems and Their Impact to the Sustainability of Current Energy and Water Nexus

This dissertation aims to explore the feasibility of incorporating electric vehicles into the electric power grid and develop a comprehensive assessment framework to predict and evaluate the life cycle environmental, economic and social impact of the integration of Vehicle-to-Grid systems and the transportation-water-energy nexus. Based on the fact that electric vehicles of different classes have been widely adopted by both fleet operators and individual car owners, the following questions are investigated: 1. Will the life cycle environmental impacts due to vehicle operation be reduced? 2. Will the implementation of Vehicle-to-Grid systems bring environmental and economic benefits? 3. Will there be any form of air emission impact if large amounts of electric vehicles are adopted in a short time? 4. What is the role of the Vehicle-to-Grid system in the transportation-water-energy nexus? To answer these questions: First, the life cycle environmental impacts of medium-duty trucks in commercial delivery fleets are analyzed. Second, the operation mechanism of Vehicle-to-Grid technologies in association with charging and discharging of electric vehicles is researched. Third, the feasible Vehicle-to-Grid system is further studied taking into consideration the spatial and temporal variance as well as other uncertainties within the system. Then, a comparison of greenhouse gas emission mitigation of the Vehicle-to-Grid system and the additional emissions caused by electric vehicle charging through marginal electricity is analyzed. Finally, the impact of the Vehicle-to-Grid system in the transportation-water-energy nexus, and the underlying environmental, economic and social relationships are simulated through system dynamic modeling. The results provide holistic evaluations and spatial and temporal projections of electric vehicles, Vehicle-to-Grid systems, wind power integration, and the transportation-water-energy nexus

SALSA: A Formal Hierarchical Optimization Framework for Smart Grid

The smart grid, by the integration of advanced control and optimization technologies, provides the traditional grid with an indisputable opportunity to deliver and utilize the electricity more efficiently. Building smart grid applications is a challenging task, which requires a formal modeling, integration, and validation framework for various smart grid domains. The design flow of such applications must adapt to the grid requirements and ensure the security of supply and demand. This dissertation, by proposing a formal framework for customers and operations domains in the smart grid, aims at delivering a smooth way for: i) formalizing their interactions and functionalities, ii) upgrading their components independently, and iii) evaluating their performance quantitatively and qualitatively.The framework follows an event-driven demand response program taking no historical data and forecasting service into account. A scalable neighborhood of prosumers (inside the customers domain), which are equipped with smart appliances, photovoltaics, and battery energy storage systems, are considered. They individually schedule their appliances and sell/purchase their surplus/demand to/from the grid with the purposes of maximizing their comfort and profit at each instant of time. To orchestrate such trade relations, a bilateral multi-issue negotiation approach between a virtual power plant (on behalf of prosumers) and an aggregator (inside the operations domain) in a non-cooperative environment is employed. The aggregator, with the objectives of maximizing its profit and minimizing the grid purchase, intends to match prosumers' supply with demand. As a result, this framework particularly addresses the challenges of: i) scalable and hierarchical load demand scheduling, and ii) the match between the large penetration of renewable energy sources being produced and consumed. It is comprised of two generic multi-objective mixed integer nonlinear programming models for prosumers and the aggregator. These models support different scheduling mechanisms and electricity consumption threshold policies.The effectiveness of the framework is evaluated through various case studies based on economic and environmental assessment metrics. An interactive web service for the framework has also been developed and demonstrated

Multi-Objective Dynamic Economic Dispatch with Demand Side Management of Residential Loads and Electric Vehicles

In this paper, a multi-objective optimization method based on the normal boundary intersection is proposed to solve the dynamic economic dispatch with demand side management of individual residential loads and electric vehicles. The proposed approach specifically addresses consumer comfort through acceptable appliance deferral times and electric vehicle charging requirements. The multi-objectives of minimizing generation costs, emissions, and energy loss in the system are balanced in a Pareto front approach in which a fuzzy decision making method has been implemented to find the best compromise solution based on desired system operating conditions. The normal boundary intersection method is described and validated

Urban load optimization based on agent-based model representation

Tese de mestrado integrado em Engenharia da Energia e do Ambiente, apresentada à Universidade de Lisboa, através da Faculdade de Ciências, em 2018O sistema energético atravessará uma profunda transformação nos próximos anos à medida que a produção renovável distribuída, a flexibilidade no lado do consumo e as funcionalidades de SmartGrid são implementadas. Este processo, conduzido em grande parte pelas imposições causadas pelos efeitos das alterações climáticas, implica profundas transformações na produção e consumo de energia e torna a transição energética extremamente urgente. Simultaneamente, novos players, entidades e modelos de negócio têm emergido em quase todos os níveis da cadeia energética desde a produção, a transmissão, distribuição e comercialização até à gestão da rede elétrica, num movimento conduzido pelo processo de particionamento (unbundling) do sistema elétrico e pela exigência de um sistema mais descentralizado e horizontal. O efeito combinado desta nova paisagem energética torna possíveis novas funcionalidades e arquitecturas de sistema na mesma medida em que coloca enormes problemas de natureza física e matemática mas também enormes questões económicas, sociais e políticas que terão, necessariamente, de ser abordadas e resolvidas. A Gestão do Consumo é um termo abrangente que representa tanto os mecanismos de Resposta na Procura (Demand Response) ou a Gestão no Lado da Procura (Demand-Side Management) e que se impõe como um dos problemas actuais mais importantes em sistemas energéticos inteligentes caracterizados por altas penetrações renováveis e mecanismos de mercado. Para resolver estes problemas, um conjunto de métodos matemáticos e computacionais têm sido propostos nos últimos anos. Otimização distribuída e sistemas inteligentes, sistemas baseados em agentes de software e teoria de jogos encontram-se entre algumas das ferramentas usadas para otimizar o consumo de energia e determinar o agendamento e a alocação ótima de equipamentos e máquinas para consumidores residenciais, comerciais e industriais. Na sequência de trabalhos prévios disponíveis na literatura da especialidade, o presente trabalho propõe um modelo geral para abordar o problema da otimização de cargas através de arquitecturas e métodos baseados no paradigma dos Agentes. O trabalho começa por definir agentes em pontos críticos da rede elétrica e os seus processos internos de raciocínio representados por modelos de otimização matemática. Seguidamente as interações entre agentes são modeladas como um jogo de dois níveis (bi-level game) entre uma entidade gestora da rede e consumidores de energia tipificados de forma a coordenar o carregamento de diversos equipamentos, incluindo veículos elétricos, e determinar uma solução admissível para o sistema global. A funcionalidade geral do modelo proposto é demonstrada através da sua implementação em software proprietário e recorrendo a um conjunto de dados específicos. Está, então, pronto para ser complementado e refinado no futuro de forma a ser aplicado em problemas do mundo real, de grandes dimensões, mas também novas implementações em software open source de forma a ficar acessível a novos utilizadores.The energy system is expected to go through a phase change in coming years as distributed generation, demand flexibility and SmartGrid features gets implemented. The main driver for this process, climate change, imposes constraints on energy production and consumption making energy transition extremely urgent. Simultaneously, new players, entities and business models have emerged at almost all levels of the energy chain from production, transmission, distribution and commercialization down to power grid management driven by the unbundling process and the call for a more decentralized and horizontal energy system. The combined effect of this new energy landscape makes new system’s architectures and functionalities desirable and possible, but poses huge physical, mathematical, engineering, economic and political questions and problems that need to be tackled. Load Management is one broad term depicting Demand-Side Management and Demand Response mechanisms and is one of the pressing problems on smart energy systems. To solve them, a plethora of computational and mathematical methods have been proposed in recent years. Distributed optimization and intelligence, software agents, agent-based systems and game theory are among the tools used to optimize load consumption and determine optimal device scheduling for residential, commercial and industrial power consumers Following previous work found in literature, the present work proposes a general framework to treat the load optimization problem using agent-based architectures and models. We start by defining agents at critical points within the power grid as well as their internal reasoning process depicted by mathematical optimization models. We then proceed to model the cooperative interactions between agents as a Bi-level game between a grid entity and typified power consumers in order to coordinate the charging of several appliances and electrical vehicles and determine a feasible solution for the global system. We show the general functionality of the framework by implementing it in software and applying it to specific datasets. The framework is suitable for further refinement and development when applied to real world problems

An Aggregator Based Framework Using Electric Vehicles for Ancillary Services in a Distribution System

Demand for electric vehicles is expected to increase by four times projecting into year 2026. It is predicted that electrification of the transportation sector, alongside the distributed energy resource transition will be a major driver of change in how our secondary distribution systems are built, operated, and planned for new investments. The increase in number of electric vehicles, coupled with recent regulatory changes emanating from FERC’s order no. 2222, have created immense potential for participation of aggregators in organized electricity markets. This work explores the operation of an electric vehicles charging aggregator model that will provide ancillary services in terms of voltage support and congestion relief to the secondary distribution system and will be rewarded for its services by the local utility according to a compensation mechanism based on voltage-cost curves and congestion relief reward. The operation is expected to be mutually beneficial for all stakeholders