Local flexibility market design for aggregators providing multiple flexibility services at distribution network level

This paper presents a general description of local flexibility markets as a market-based management mechanism for aggregators. The high penetration of distributed energy resources introduces new flexibility services like prosumer or community self-balancing, congestion management and time-of-use optimization. This work is focused on the flexibility framework to enable multiple participants to compete for selling or buying flexibility. In this framework, the aggregator acts as a local market operator and supervises flexibility transactions of the local energy community. Local market participation is voluntary. Potential flexibility stakeholders are the distribution system operator, the balance responsible party and end-users themselves. Flexibility is sold by means of loads, generators, storage units and electric vehicles. Finally, this paper presents needed interactions between all local market stakeholders, the corresponding inputs and outputs of local market operation algorithms from participants and a case study to highlight the application of the local flexibility market in three scenarios. The local market framework could postpone grid upgrades, reduce energy costs and increase distribution grids’ hosting capacity.Postprint (published version

Distributed Energy Trading: The Multiple-Microgrid Case

In this paper, a distributed convex optimization framework is developed for energy trading between islanded microgrids. More specifically, the problem consists of several islanded microgrids that exchange energy flows by means of an arbitrary topology. Due to scalability issues and in order to safeguard local information on cost functions, a subgradient-based cost minimization algorithm is proposed that converges to the optimal solution in a practical number of iterations and with a limited communication overhead. Furthermore, this approach allows for a very intuitive economics interpretation that explains the algorithm iterations in terms of "supply--demand model" and "market clearing". Numerical results are given in terms of convergence rate of the algorithm and attained costs for different network topologies.Comment: 24 pages, 8 figures; new version answering reviewers' comments; the paper is now accepted for publication in the IEEE Transactions on Industrial Electronics; the paper is now publishe

Smart Grid as a Service: A Discussion on Design Issues

Smart grid allows the integration of distributed renewable energy resources into the conventional electricity distribution power grid such that the goals of reduction in power cost and in environment pollution can be met through an intelligent and efficient matching between power generators and power loads. Currently, this rapidly developing infrastructure is not as “smart” as it should be because of the lack of a flexible, scalable, and adaptive structure. As a solution, this work proposes smart grid as a service (SGaaS), which not only allows a smart grid to be composed out of basic services, but also allows power users to choose between different services based on their own requirements. The two important issues of service-level agreements and composition of services are also addressed in this work. Finally, we give the details of how SGaaS can be implemented using a FIPA-compliant JADE multiagent system

Evaluation of multistep forecasting model in a home energy management system

Home Energy Management Systems (HEMS) offers to forecast energy consumptions in a household, optimize and operate flexible appliances, as well as the generations, such as the photovoltaic (PV) panels and the battery storages. The forecasting model in a HEMS aims to anticipate the correct consumption for the battery, in order to have the sufficient amount of capacity for solar generation or to have sufficient charge for evening peaks. There is little research on how the accuracy of a forecasting model would affect the energy cost after the battery operation is optimized. In this thesis, a simple HEMS model is simulated with the programming language Python. Several forecasting models are simulated in combination of 3 tariff schemes, 2 seasons, and with 3 sets of household data. The forecasting results were evaluated with common error metrics and compared with the simulated energy cost. The results shows that the common error metrics don’t give a good indication how a forecasting model would perform monetarily. Addition experiments were conducted to explore the more important timesteps in a HEMS to lower the overall cost. The results show the effect of accurately forecasting the consumption decreases, the further the timestep is from the time of forecasting. The first timestep for the next 24 hours has the most significant effect of decreasing the total electricity cost, when predicted perfectly. A custom weighted error metric is tested with the forecasting models. The custom metric only performs marginally better in indicating the monetary performance of a forecasting model. The conclusion of thesis is the error metrics tested does not give a universal indication of how the forecasting model would perform without the optimization part of the HEMS. Secondly, the closer a timestep is to the time of forecast, the more important it is to have the forecast accurate. Therefore, when developing a forecasting algorithm for an HEMS, the focus should be on improving the accuracy of the first few timesteps

Stochastic interval-based optimal offering model for residential energy management systems by household owners

This paper proposes an optimal bidding strategy for autonomous residential energy management systems. This strategy enables the system to manage its domestic energy production and consumption autonomously, and trade energy with the local market through a novel hybrid interval-stochastic optimization method. This work poses a residential energy management problem which consists of two stages: day-ahead and real-time. The uncertainty in electricity price and PV power generation is modeled by interval-based and stochastic scenarios in the day-ahead and real-time transactions between the smart home and local electricity market. Moreover, the implementation of a battery included to provide energy flexibility in the residential system. In this paper, the smart home acts as a price-taker agent in the local market, and it submits its optimal offering and bidding curves to the local market based on the uncertainties of the system. Finally, the performance of the proposed residential energy management system is evaluated according to the impacts of interval optimistic and flexibility coefficients, optimal bidding strategy, and uncertainty modeling. The evaluation has shown that the proposed optimal offering model is effective in making the home system robust and achieves optimal energy transaction. Thus, the results prove that the proposed optimal offering model for the domestic energy management system is more robust than its non-optimal offering model. Moreover, battery flexibility has a positive effect on the system’s total expected profit. With regarding to the bidding strategy, it is not able to impact the smart home’s behavior (as a consumer or producer) in the day-ahead local electricity market.This work is supported by the European Commission H2020 MSCA-RISE-2014: Marie Sklodowska-Curie project DREAM-GO Enabling Demand Response for short and real-time Efficient And Market Based Smart Grid Operation—An intelligent and real-time simulation approach Ref. 641794, and Grant Agreement No. 703689 (Project ADAPT). Moreover, Amin Shokri Gazafroudi acknowledge the support by the Ministry of Education of the Junta de Castilla y León and the European Social Fund through a grant from predoctoral recruitment of research personnel associated with the research project "Arquitectura multiagente para la gestión eficaz de redes de energía a través del uso de técnicas de intelligencia artificial" of the University of Salamanca. Moreover, authors would like to thank Dr. Juan Miguel Morales González from University of Malaga for his thoughtful suggestions.info:eu-repo/semantics/publishedVersio

Estrutura de comercialização de energia entre consumidores e prosumidores de uma microrrede por intermédio de um leilão duplo

O presente trabalho tem como objetivo propor uma estrutura de comercialização de energia local entre consumidores e prosumidores de uma microrrede conectada a rede elétrica por intermédio de um leilão duplo com uma hora de antecedência. A microrrede é composta por três tipos de unidades consumidoras que são classificados em função da existência de geração e armazenamento de energia em suas instalações. Para cada classificação, foi desenvolvido um algoritmo para representar um agente autônomo que toma decisões em relação à oferta que a unidade consumidora na qual ele está instalado enviará para a plataforma digital de leilão. Esses algoritmos definem como cada agente deve comprar, armazenar e vender a energia elétrica na próxima hora. As transações reais de energia entre as unidades consumidoras e a concessionária ocorrem durante a hora para qual o leilão foi previsto. Assim, após a finalização dessas transações, comparam-se as transações financeiras previstas pelo leilão e as trocas de energia elétrica que foram realizadas. A concessionária realiza o ajuste financeiro, comprando ou vendendo a diferença de energia para cada agente para que eles possam cumprir os contratos firmados no leilão. Para ilustrar o funcionamento e os benefícios proporcionados pela utilização da estrutura de comercialização proposta, foram considerados diferentes cenários de análise simulados no software Matlab. Nesses cenários foram avaliados a influência do leilão, do armazenamento e da modalidade da tarifa da concessionária (convencional ou branca) utilizada como referência para as ofertas realizadas no leilão. Conclui-se esse trabalho apresentando os ganhos financeiros e benefícios aportados pela estrutura de comercialização proposta.The goal of this paper is to propose a local energy trading framework between consumers and prosumers of a microgrid connected to the power grid based on an hour-ahead double auction. The microgrid consists of three types of consumers which are classified according to the existence of energy generation and storage in their facilities. For each classification, an algorithm has been developed to represent an autonomous agent who makes decisions regarding the offer that the consumer unit in which it is installed will send to the digital auction platform. These algorithms define how each agent should buy, store and sell electricity in the next hour. Actual energy transactions between consumer units occur during the time for which the auction was predicted. Thus, after the conclusion of the transactions, the financial transactions provided by the auction and the actual electricity exchanges are compared. The utility makes the financial adjustment by buying or selling the energy difference for each agent so that they can fulfill the contracts signed in the auction. To evaluate the functioning and benefits provided by the use of the proposed energy trading framework, different scenarios were simulated in the software Matlab. In these scenarios, the influence of the auction, storage and utility tariff (conventional or white) used as a reference for the offers sent to the auction were evaluated. This paper is concluded by presenting the financial gains and the benefits provided by the proposed framework

A Multiagent Modeling and Investigation of Smart Homes With Power Generation, Storage, and Trading Features

Smart homes, as active participants in a smart grid, may no longer be modeled by passive load curves; because their interactive communication and bidirectional power flow within the smart grid affects demand, generation, and electricity rates. To consider such dynamic environmental properties, we use a multiagent-system-based approach in which individual homes are autonomous agents making rational decisions to buy, sell, or store electricity based on their present and expected future amount of load, generation, and storage, accounting for the benefits each decision can offer. In the proposed scheme, home agents prioritize their decisions based on the expected utilities they provide. Smart homes’ intention to minimize their electricity bills is in line with the grid’s aim to flatten the total demand curve. With a set of case studies and sensitivity analyses, we show how the overall performance of the home agents converges-as an emergent behavior-to an equilibrium benefiting both the entities in different operational conditions and determines the situations in which conventional homes would benefit from purchasing their own local generation-storage systems

Managing Distributed Information: Implications for Energy Infrastructure Co-production

abstract: The Internet and climate change are two forces that are poised to both cause and enable changes in how we provide our energy infrastructure. The Internet has catalyzed enormous changes across many sectors by shifting the feedback and organizational structure of systems towards more decentralized users. Today’s energy systems require colossal shifts toward a more sustainable future. However, energy systems face enormous socio-technical lock-in and, thus far, have been largely unaffected by these destabilizing forces. More distributed information offers not only the ability to craft new markets, but to accelerate learning processes that respond to emerging user or prosumer centered design needs. This may include values and needs such as local reliability, transparency and accountability, integration into the built environment, and reduction of local pollution challenges. The same institutions (rules, norms and strategies) that dominated with the hierarchical infrastructure system of the twentieth century are unlikely to be good fit if a more distributed infrastructure increases in dominance. As information is produced at more distributed points, it is more difficult to coordinate and manage as an interconnected system. This research examines several aspects of these, historically dominant, infrastructure provisioning strategies to understand the implications of managing more distributed information. The first chapter experimentally examines information search and sharing strategies under different information protection rules. The second and third chapters focus on strategies to model and compare distributed energy production effects on shared electricity grid infrastructure. Finally, the fourth chapter dives into the literature of co-production, and explores connections between concepts in co-production and modularity (an engineering approach to information encapsulation) using the distributed energy resource regulations for San Diego, CA. Each of these sections highlights different aspects of how information rules offer a design space to enable a more adaptive, innovative and sustainable energy system that can more easily react to the shocks of the twenty-first century.Dissertation/ThesisDoctoral Dissertation Sustainability 201

Internet of things (IoT) based adaptive energy management system for smart homes

PhD ThesisInternet of things enhances the flexibility of measurements under different environments, the development of advanced wireless sensors and communication networks on the smart grid infrastructure would be essential for energy efficiency systems. It makes deployment of a smart home concept easy and realistic. The smart home concept allows residents to control, monitor and manage their energy consumption with minimal wastage. The scheduling of energy usage enables forecasting techniques to be essential for smart homes. This thesis presents a self-learning home management system based on machine learning techniques and energy management system for smart homes. Home energy management system, demand side management system, supply side management system, and power notification system are the major components of the proposed self-learning home management system. The proposed system has various functions including price forecasting, price clustering, power forecasting alert, power consumption alert, and smart energy theft system to enhance the capabilities of the self-learning home management system. These functions were developed and implemented through the use of computational and machine learning technologies. In order to validate the proposed system, real-time power consumption data were collected from a Singapore smart home and a realistic experimental case study was carried out. The case study had proven that the developed system performing well and increased energy awareness to the residents. This proposed system also showcases its customizable ability according to different types of environments as compared to traditional smart home models. Forecasting systems for the electricity market generation have become one of the foremost research topics in the power industry. It is essential to have a forecasting system that can accurately predict electricity generation for planning and operation in the electricity market. This thesis also proposed a novel system called multi prediction system and it is developed based on long short term memory and gated recurrent unit models. This proposed system is able to predict the electricity market generation with high accuracy. Multi Prediction System is based on four stages which include a data collecting and pre-processing module, a multi-input feature model, multi forecast model and mean absolute percentage error. The data collecting and pre-processing module preprocess the real-time data using a window method. Multi-input feature model uses single input feeding method, double input feeding method and multiple feeding method for features input to the multi forecast model. Multi forecast model integrates long short term memory and gated recurrent unit variations such as regression model, regression with time steps model, memory between batches model and stacked model to predict the future generation of electricity. The mean absolute percentage error calculation was utilized to evaluate the accuracy of the prediction. The proposed system achieved high accuracy results to demonstrate its performance

Evaluación de impacto económico de las redes eléctricas inteligentes en el usuario residencial de Colombia

Con la creciente demanda de energía mundial y las soluciones de Redes inteligentes adaptándose en los mercados eléctricos y en mayor medida en el consumidor residencial, surge la opción de implementar Hogares Inteligentes para alcanzar la visión 2030 de las Redes eléctricas Inteligentes en el sector eléctrico colombiano. Las estrategias gubernamentales están enfocadas en la adopción de infraestructura de medición avanzada (AMI), Generación distribuida (DG) con Fuentes No Convencionales de Energía Renovable (FNCER) e inclusión del Vehículo Eléctrico en la red (V2G), con el fin de proporcionar un óptimo Sistema de Administración de la Demanda (DMS) a través de Sistemas Administradores de Energía en el Hogar (HEMS) para lograr obtener beneficios como aplanamiento de la curva de carga, conservación de la energía y eficiencia energética en la red eléctrica. El objetivo de esta tesis de maestría es evaluar el impacto económico de las Redes eléctricas Inteligentes aplicables al consumidor residencial e integrado en el concepto de Hogar Inteligente, para cumplir este objetivo se caracterizarán los usuarios residenciales con sus patrones de consumo, seguido de esto se identificarán las tecnologías aplicables de las Redes eléctricas Inteligentes al Hogar Inteligente y por último se desarrollará un modelo de evaluación financiera para determinar el impacto económico al adoptar dichas tecnologías. Se obtuvieron resultados optimistas y alentadores para los usuarios residenciales, de manera que puedan implementar tecnologías provenientes de las Redes eléctricas Inteligentes en sus hogares contemplando beneficios e incentivos gubernamentales para contribuir con la mejora constante y confiabilidad de la red eléctrica.Abstract: With the growing demand for global energy and Smart Grid solutions adapting in the electricity markets and to a greater extent in the residential consumer, emerges the option of implementing Smart Homes to reach the 2030 vision of the Smart Grids in the Colombian electricity sector. Government strategies are focused on the adoption of advanced measurement infrastructure (AMI), Distributed Generation (DG) with Unconventional Sources of Renewable Energy (FNCER) and inclusion of the Electric Vehicle in the grid (V2G), in order to provide an Optimal Demand Management System (DMS) through Home Energy Management Systems (HEMS) to achieve benefits such as flattening the load curve, conserving energy and energy efficiency in the electricity grid. The objective of this master’s thesis is to evaluate the economic impact of Smart Grids applicable to the residential consumer and integrated into the concept of Smart Home, in order to reach this objective, residential users will be characterized with their consuming patterns, followed by the identification of Applicable technologies of Smart Grids to the Smart Home; and finally, a financial evaluation model will be developed to determine the economic impact of adopting these technologies. It was obtained optimistic and encouraging results for residential users, so that they can implement technologies from the Smart Grids in their homes contemplating government benefits and incentives to contribute to the constant improvement and reliability of the electric grid.Maestrí