Business models for flexibility of electric vehicles

The electrical grid is undergoing an unprecedented evolution driven mainly by the adoption of smart grid technologies. The high penetration of distributed energy resources, including renewables and electric vehicles, promises several beneits to the diferent market actors and consumers, but at the same time imposes grid integration challenges that must adequately be addressed. In this paper, we explore and propose potential business models (BMs) in the context of distribution networks with high penetration of electric vehicles (EVs). The analysis is linked to the CENERGETIC project (Coordinated ENErgy Resource manaGEment under uncerTainty considering electrIc vehiCles and demand lexibility in distribution networks). Due to the complex mechanisms needed to fulill the interactions between stakeholders in such a scenario, computational intelligence (CI) techniques are envisaged as a viable option to provide eicient solutions to the optimization problems that might arise by the adoption of innovative BMs. After a brief review on evolutionary computation (EC) applied to the optimization problems in distribution networks with high penetration of EVs, we conclude that EC methods can be suited to implement the proposed business models in our future CENERGETIC project and beyond.This research has received funding from FEDER funds through the Operational Programme for Competitiveness and Internationalization (COMPETE 2020), under Project POCI-01-0145-FEDER-028983; by National Funds through the FCT Portuguese Foundation for Science and Technology, under Projects PTDC/EEI-EEE/28983/2017 (CENERGETIC), UID/EEA/00760/2019; and the São Paulo Research Foundation (FAPESP), under Projects 2018/08008-4 and 2018/20355- 1info:eu-repo/semantics/publishedVersio

Interfacing power electronics systems for smart grids: innovative perspectives of unified systems and operation modes

The power distribution grid is centrally managed concerning the requirements of the end-users, however, with the appearance of smart grids, new technologies arc arising. Therefore, distributed energy resources, mainly, renewables, energy storage systems, electric mobility, and power quality are viewed as encouraging contributions for improving power management. In these circumstances, this paper presents a power electronics perspective for the power distribution grid, considering innovative features, and including a power quality perception. Throughout the paper are presented relevant concepts for a concrete realization of a smart grid, supported by the integration of power electronics devices as the interface of the mentioned technologies. Aiming to support the innovative power electronics systems for interfacing the mentioned technologies in smart grids, a set of developed power electronics equipment was developed and, along with the paper, are shown and described, supporting the most important contributions of this paper.This work has been supported by FCT -Fundação para a Ciencia e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. This work has been supported by the FCT Project newERA4GRIDs PTDC/EEI-EEE/30283/2017

The Role of Plug-In Electric Vehicles with Renewable Resources in Electricity Systems

Deux voies technologiques, la génération d’électricité renouvelable et l’électrification des véhicules, sont souvent avancées comme solution à deux des plus grands défis de notre époque : satisfaire à une demande énergétique croissante tout en réduisant les émissions de gaz à effet de serre. La réalisation de ces deux objectifs implique le besoin de transférer une partie de la demande de combustibles fossiles vers d’autres sources d’énergie primaire. La diffusion des énergies renouvelables et des véhicules électriques rechargeables (VER) a été entravée par des obstacles importants, malgré leur potentiel reconnu d’améliorer la durabilité énergétique dans les secteurs de l’électricité et du transport. Les deux technologies ont des synergies naturelles entre elles : les VER sont une source inhérente de flexibilité du côté de la demande aussi bien que de l’offre, qui pourraient aider à mitiger les effets négatifs de la variabilité de la génération d’électricité renouvelable. Dans cet article nous examinons les obstacles au déploiement des renouvelables et des VER, ainsi que les synergies entre les deux voies technologiques. Nous soulevons des questions autour de l’implémentation ainsi que des mesures d’incitation et des modèles d’affaires qui pourraient empêcher ou aider à réaliser la valeur de ces synergies. Nous proposons enfin de nouvelles problématiques de recherche qui pourraient amener à résoudre ces questions d’implémentation.Two technology options, renewable electricity generation and vehicle electrification, are being promoted to achieve two of the greatest objectives of this century: meeting growing global energy demand while reducing greenhouse gas emissions. Addressing both objectives implies shifting part of this energy demand away from fossil fuels to other primary energy sources. Renewables and plug-in electric vehicle (PEV) adoption has been hindered by significant challenges despite their known potential to improve energy sustainability in electric power systems and transportation. The two technologies have natural synergies between them, however: PEVs are a natural source of demand -and supply-side flexibility, which can help mitigate the negative ancillary effects of renewable variability and uncertainty. In this paper we discuss the issues hindering renewable and PEV adoption and the synergies between these two technology pathways. Finally, we raise some issues with implementation and challenges with incentive and business plan design that may hinder fully realizing these synergies. We also propose some important research questions that would help address these implementation issues

Cost and emission impacts of virtual power plant formation in plug-in hybrid electric vehicle penetrated networks

Cataloged from PDF version of article.With increasing interest in alternative energy resources and technologies, mass penetration of PHEVs (plug-in hybrid vehicles) into the electricity grid and widespread utilization of DERs (distributed energy resources) are anticipated in the near future. As an aggregation unit, the VPP (virtual power plant) is introduced for load management and resource scheduling. In this article, we develop an energy management model for VPPs and analyze the cost and emission impacts of VPP formation and PHEV penetration. We conduct a case study for the state of California using real-world data from official resources. An average of 29.5% cost reduction and 79% CO2 and 83% NOx emission reductions are attained as shared benefits of consumers in the case study. Results are illustrative of opportunities that VPP formation can provide for the community. Sensitivity of the results to the DER costs and capacities, battery and gasoline prices are also analyzed. In addition, we prove that charging and discharging do not simultaneously occur in the solutions, which leads to a simplification in traditional energy management models. 2013 Elsevier Ltd. All rights reserved

Towards the next generation of smart grids: semantic and holonic multi-agent management of distributed energy resources

The energy landscape is experiencing accelerating change; centralized energy systems are being decarbonized, and transitioning towards distributed energy systems, facilitated by advances in power system management and information and communication technologies. This paper elaborates on these generations of energy systems by critically reviewing relevant authoritative literature. This includes a discussion of modern concepts such as ‘smart grid’, ‘microgrid’, ‘virtual power plant’ and ‘multi-energy system’, and the relationships between them, as well as the trends towards distributed intelligence and interoperability. Each of these emerging urban energy concepts holds merit when applied within a centralized grid paradigm, but very little research applies these approaches within the emerging energy landscape typified by a high penetration of distributed energy resources, prosumers (consumers and producers), interoperability, and big data. Given the ongoing boom in these fields, this will lead to new challenges and opportunities as the status-quo of energy systems changes dramatically. We argue that a new generation of holonic energy systems is required to orchestrate the interplay between these dense, diverse and distributed energy components. The paper therefore contributes a description of holonic energy systems and the implicit research required towards sustainability and resilience in the imminent energy landscape. This promotes the systemic features of autonomy, belonging, connectivity, diversity and emergence, and balances global and local system objectives, through adaptive control topologies and demand responsive energy management. Future research avenues are identified to support this transition regarding interoperability, secure distributed control and a system of systems approach

Optimization of Electric-Vehicle Charging: scheduling and planning problems

The progressive shift from traditional vehicles to Electric Vehicles (EVs ) is considered one of the key measures to achieve the objective of a significant reduction in the emission of pollutants, especially in urban areas. EVs will be widely used in a not-so-futuristic vision, and new technologies will be present for charging stations, batteries, and vehicles. The number of EVs and Charging Stations (CSs) is increased in the last years, but, unfortunately, wide usage of EVs may cause technical problems to the electrical grid (i.e., instability due to intermittent distributed loads), inefficiencies in the charging process (i.e., lower power capacity and longer recharging times), long queues and bad use of CSs. Moreover, it is necessary to plan the CSs installation over the territory, the schedule of vehicles, and the optimal use of CSs. This thesis focuses on applying optimization methods and approaches to energy systems in which EVs are present, with specific reference to planning and scheduling decision problems. In particular, in smart grids, energy production, and storage systems are usually scheduled by an Energy Management System (EMS) to minimize costs, power losses, and CO2 emissions while satisfying energy demands. When CSs are connected to a smart grid, EVs served by CSs represent an additional load to the power system to be satisfied, and an additional storage system in the case of vehicle-to-grid (V2G) technology is enabled. However, the load generated by EVs is deferrable. It can be thought of as a process in which machines (CSs) serve customers/products (EVs) based on release time, due date, deadline, and energy request, as happens in manufacturing systems. In this thesis, first, attention is focused on defining a discrete-time optimization problem in which fossil fuel production plants, storage systems, and renewables are considered to satisfy the grid's electrical load. The discrete-time formalization can use forecasting for renewables and loads without data elaboration. On the other side, many decision variables are present, making the optimization problem hard to solve through commercial optimization tools. For this reason, an alternative method for the optimal schedule of EVs characterized by a discrete event formalization is presented. This new approach can diminish the number of variables by considering the time intervals as variables themselves. Of course, the solution's optimality is not guaranteed since some assumptions are necessary. Moreover, the last chapter proposes a novel approach for the optimal location and line assignment for electric bus charging stations. In particular, the model provides the siting and sizing of some CSs to maintain a minimum service frequency over public transportation lines

Optimization of Aggregators Energy Resources considering Local Markets and Electric Vehicle Penetration

O sector elétrico tem vindo a evoluir ao longo do tempo. Esta situação deve-se ao facto de surgirem novas metodologias para lidarem com a elevada penetração dos recursos energéticos distribuídos (RED), principalmente veículos elétricos (VEs). Neste caso, a gestão dos recursos energéticos tornou-se mais proeminente devido aos avanços tecnológicos que estão a ocorrer, principalmente no contexto das redes inteligentes. Este facto torna-se importante, devido à incerteza decorrente deste tipo de recursos. Para resolver problemas que envolvem variabilidade, os métodos baseados na inteligência computacional estão a se tornar os mais adequados devido à sua fácil implementação e baixo esforço computacional, mais precisamente para o caso tratado na tese, algoritmos de computação evolucionária (CE). Este tipo de algoritmo tenta imitar o comportamento observado na natureza. Ao contrário dos métodos determinísticos, a CEé tolerante à incerteza; ou seja, é adequado para resolver problemas relacionados com os sistemas energéticos. Estes sistemas são geralmente de grandes dimensões, com um número crescente de variáveis e restrições. Aqui a IC permite obter uma solução quase ótima em tempo computacional aceitável com baixos requisitos de memória. O principal objetivo deste trabalho foi propor um modelo para a programação dos recursos energéticos dos recursos dedicados para o contexto intradiário, para a hora seguinte, partindo inicialmente da programação feita para o dia seguinte, ou seja, 24 horas para o dia seguinte. Esta programação é feita por cada agregador (no total cinco) através de meta-heurísticas, com o objetivo de minimizar os custos ou maximizar os lucros. Estes agregadores estão inseridos numa cidade inteligente com uma rede de distribuição de 13 barramentos com elevada penetração de RED, principalmente energia renovável e VEs (2000 VEs são considerados nas simulações). Para modelar a incerteza associada ao RED e aos preços de mercado, vários cenários são gerados através da simulação de Monte Carlo usando as funções de distribuição de probabilidade de erros de previsão, neste caso a função de distribuição normal para o dia seguinte. No que toca à incerteza no modelo para a hora seguinte, múltiplos cenários são gerados a partir do cenário com maior probabilidade do dia seguinte. Neste trabalho, os mercados locais de eletricidade são também utilizados como estratégia para satisfazer a equação do balanço energético onde os agregadores vão para vender o excesso de energia ou comprar para satisfazer o consumo. Múltiplas metaheurísticas de última geração são usadas para fazer este escalonamento, nomeadamente Differential Evolution (DE), Hybrid-Adaptive DE with Decay function (HyDE-DF), DE with Estimation of Distribution Algorithm (DEEDA), Cellular Univariate Marginal Distribution Algorithm with Normal-Cauchy Distribution (CUMDANCauchy++), Hill Climbing to Ring Cellular Encode-Decode UMDA (HC2RCEDUMDA). Os resultados mostram que o modelo proposto é eficaz para os múltiplos agregadores com variações de custo na sua maioria abaixo dos 5% em relação ao dia seguinte, exceto para o agregador e de VEs. É também aplicado um teste Wilcoxon para comparar o desempenho do algoritmo CUMDANCauchy++ com as restantes meta-heurísticas. O CUMDANCauchy++ mostra resultados competitivos tendo melhor performance que todos os algoritmos para todos os agregadores exceto o DEEDA que apresenta resultados semelhantes. Uma estratégia de aversão ao risco é implementada para um agregador no contexto do dia seguinte para se obter uma solução mais segura e robusta. Os resultados mostram um aumento de quase 4% no investimento, mas uma redução de até 14% para o custo dos piores cenários.The electrical sector has been evolving. This situation is because new methodologies emerge to deal with the high penetration of distributed energy resources (DER), mainly electric vehicles (EVs). In this case, energy resource management has become increasingly prominent due to the technological advances that are taking place, mainly in the context of smart grids. This factor becomes essential due to the uncertainty of this type of resource. To solve problems involving variability, methods based on computational intelligence (CI) are becoming the most suitable because of their easy implementation and low computational effort, more precisely for the case treated in this thesis, evolutionary computation (EC) algorithms. This type of algorithm tries to mimic behavior observed in nature. Unlike deterministic methods, the EC is tolerant of uncertainty, and thus it is suitable for solving problems related to energy systems. These systems are usually of high dimensions, with an increased number of variables and restrictions. Here the CI allows obtaining a near-optimal solution in good computational time with low memory requirements. This work's main objective is to propose a model for the energy resource scheduling of the dedicated resources for the intraday context, for the our-ahead, starting initially from the scheduling done for the day ahead, that is, 24 hours for the next day. This scheduling is done by each aggregator (in total five) through metaheuristics to minimize the costs or maximize the profits. These aggregators are inserted in a smart city with a distribution network of 13 buses with a high penetration of DER, mainly renewable energy and EVs (2000 EVs are considered in the simulations). Several scenarios are generated through Monte Carlo Simulation using the forecast errors' probability distribution functions, the normal distribution function for the day-ahead to model the uncertainty associated with DER and market prices. Multiple scenarios are developed through the highest probability scenario from the day-ahead when it comes to intraday uncertainty. In this work, local electricity markets are used as a mechanism to satisfy the energy balance equation where each aggregator can sell the excess of energy or buy more to meet the demand. Several recent and modern metaheuristics are used to solve the proposed problems in the thesis, namely Differential Evolution (DE), Hybrid-Adaptive DE with Decay function (HyDE-DF), DE with Estimation of Distribution Algorithm (DEEDA), Cellular Univariate Marginal Distribution Algorithm with NormalCauchy Distribution (CUMDANCauchy++), Hill Climbing to Ring Cellular Encode-Decode UMDA (HC2RCEDUMDA). Results show that the proposed model is effective for the multiple aggregators. The metaheuristics present satisfactory results and mostly less than 5% variation in costs from the day-ahead except for the EV aggregator. A Wilcoxon test is also applied to compare the performance of the CUMDANCauchy++ algorithm with the remaining metaheuristics. CUMDANCauchy++ shows competitive results beating all algorithms in all aggregators except for DEEDA, which presents similar results. A risk aversion strategy is implemented for an aggregator in the day-ahead context to get a safer and more robust solution. Results show an increase of nearly 4% in day-ahead cost but a reduction of up to 14% of worst scenario cost

Dynamic electricity pricing for electric vehicles using stochastic programming

Electric Vehicles (EVs) are an important source of uncertainty, due to their variable demand, departure time and location. In smart grids, the electricity demand can be controlled via Demand Response (DR) programs. Smart charging and vehicle-to-grid seem highly promising methods for EVs control. However, high capital costs remain a barrier to implementation. Meanwhile, incentive and price-based schemes that do not require high level of control can be implemented to influence the EVs’ demand. Having effective tools to deal with the increasing level of uncertainty is increasingly important for players, such as energy aggregators. This paper formulates a stochastic model for day-ahead energy resource scheduling, integrated with the dynamic electricity pricing for EVs, to address the challenges brought by the demand and renewable sources uncertainty. The two-stage stochastic programming approach is used to obtain the optimal electricity pricing for EVs. A realistic case study projected for 2030 is presented based on Zaragoza network. The results demonstrate that it is more effective than the deterministic model and that the optimal pricing is preferable. This study indicates that adequate DR schemes like the proposed one are promising to increase the customers’ satisfaction in addition to improve the profitability of the energy aggregation business.info:eu-repo/semantics/acceptedVersio

Open-Source, Open-Architecture SoftwarePlatform for Plug-InElectric Vehicle SmartCharging in California

This interdisciplinary eXtensible Building Operating System–Vehicles project focuses on controlling plug-in electric vehicle charging at residential and small commercial settings using a novel and flexible open-source, open-architecture charge communication and control platform. The platform provides smart charging functionalities and benefits to the utility, homes, and businesses.This project investigates four important areas of vehicle-grid integration research, integrating technical as well as social and behavioral dimensions: smart charging user needs assessment, advanced load control platform development and testing, smart charging impacts, benefits to the power grid, and smart charging ratepayer benefits

From Packet to Power Switching: Digital Direct Load Scheduling

At present, the power grid has tight control over its dispatchable generation capacity but a very coarse control on the demand. Energy consumers are shielded from making price-aware decisions, which degrades the efficiency of the market. This state of affairs tends to favor fossil fuel generation over renewable sources. Because of the technological difficulties of storing electric energy, the quest for mechanisms that would make the demand for electricity controllable on a day-to-day basis is gaining prominence. The goal of this paper is to provide one such mechanisms, which we call Digital Direct Load Scheduling (DDLS). DDLS is a direct load control mechanism in which we unbundle individual requests for energy and digitize them so that they can be automatically scheduled in a cellular architecture. Specifically, rather than storing energy or interrupting the job of appliances, we choose to hold requests for energy in queues and optimize the service time of individual appliances belonging to a broad class which we refer to as "deferrable loads". The function of each neighborhood scheduler is to optimize the time at which these appliances start to function. This process is intended to shape the aggregate load profile of the neighborhood so as to optimize an objective function which incorporates the spot price of energy, and also allows distributed energy resources to supply part of the generation dynamically.Comment: Accepted by the IEEE journal of Selected Areas in Communications (JSAC): Smart Grid Communications series, to appea