Estimación de costos de la energía eléctrica no utilizada en micro-redes mediante métodos estocásticos basado en árboles de decisión

El crecimiento de la población y el desarrollo industrial a obligado a un aumento en la demanda de energía, lo que a obligado a las empresas generadoras de energía a incrementar su infraestructura de producción de energía, la cual es muy costosa, por lo que se busca implementar políticas para atender la demanda, incluida la generación distribuida (GD). De esta forma, se consigue un consumo de energía más eficiente, porque la carga que no representa el tiempo de consumo máximo de energía se puede transferir a otros períodos de tiempo. Además, se puede implementar un sistema de gestión energética (GE), es decir, la estrategia de formular un plan óptimo de consumo energético y aplicarlo a toda la industria o al sistema eléctrico completo ayudará a la empresa a mejorar su competitividad porque tiene más eficiencia. Este documento plantea estimar los costos de la energía no utilizada en una micro-red aislada, utilizando programas de respuesta de la demanda para lo cual se va analizar el comportamiento de la demanda en dicha micro-red para posteriormente estimar los costos asociados de la energía no utilizada y para finalmente implementar precios mediante procesos estocásticos a los usuarios de la micro-red y venta de energía.Population growth and industrial development have forced an increase in energy demand, forcing energygenerating enterprises to increase their very expensive energy production infrastructure, Therefore, it seeks to implement policies to meet demand, including distributed generation (GD). In this way, a more efficient energy consumption is achieved, because the load that does not represent the maximum energy consumption time can be transferred to other periods of time. In addition, an energy management system (GE) can be implemented, that is, the strategy of formulating an optimal energy consumption plan and applying it to the entire industry or to the entire electrical system will help the company to improve its competitiveness because it has more efficiency. This document proposes to estimate the costs of energy not used in a micro-grid order to assess the behaviour of demand in the micro-grid, to estimate the associated costs of unused energy and finally to implement prices through stochastic processes users of the micro-network and sale of energ

日射量予測を考慮した太陽光発電コミュニティにおけるエネルギーシェアリングに関する研究

The power sector plays an important role in energy conservation and emission reduction. Renewable energy, especially solar PV, has been growing steadily in recent years. The development of solar energy can not only reduce the use of fossil energy, but also increase the energy self-sufficiency rate. After the implementation of the FiT system in 2011, there has been an explosive growth in the import of solar PV. However, solar power generation exhibits unstable output characteristics as it is affected by weather conditions. Large-scale introduction can affect the stability of the grid. Therefore, this study considers the unstable weather conditions (mainly, solar radiation) and proposes the concept of energy sharing to increase the chances of local energy self-consumption and renewable energy penetration in the future. At the same time, we aim to explore the interactions between smart grids, smart buildings, and distributed energy storage to achieve better energy management practices.北九州市立大

ENERGY & STORAGE SHARING STRATEGIES IN AN ELECTRICITY MARKET ENVIRONMENT

The rapid growth of renewable energy generation (REG) and energy storage systems (ESS) has created a need to further develop the electricity market for distributed energy, to stimulate the technology and application of REG and battery energy storage systems (BESS). Considering that the investment cost is still high at this stage, a window of opportunity exists for the development of a sharing economy. In light of this, this thesis focuses on energy and storage sharing strategies in an electricity market environment. A distributed energy sharing strategy is proposed for a peer-to-peer (P2P) model on a microgrid. In addition, the pricing model for users in this proposed strategy has been optimised using game theory—with the Bayesian Nash Equilibrium (GM-BNE) algorithm. Based on the basic call auction trading model, the energy trading mechanism has been modified. Meanwhile, an energy sharing cloud service is proposed based on a decentralised approach, in which the cloud energy management strategy can be customised for each participant. Rigorous proofs are also given. A detailed energy storage sharing strategy of the hybrid electricity and gas energy is proposed in the distribution network, which considers the energy operation of BESS and thermal energy storage system (TESS). The techno-economic analysis based on the BESS and TESS sizing model is conducted for storage sharing between users. When considering the battery firm in the joint storage sharing strategy, a novel sharing model is proposed based on the classic per-use sharing economy business model. Rigorous mathematical proofs are given for the application of the sharing economy model to BESS, in which the sharing pricing model is validated for technical feasibility and accuracy. The proposed energy and storage sharing strategies are applicable to distributed users, in the cases of the hospitality industry and smart home. The proposed sharing strategies are also beneficial for investors, as demonstrated in the case for a battery firm. In the case of the battery firm, this per-use rental service can open new benefits. The case studies results show that the proposed energy and storage sharing strategies provide a 'win-win' situation for customers, the battery sales firm and energy networks

Pervasive Data Analytics for Sustainable Energy Systems

With an ever growing population, global energy demand is predicted to keep increasing. Furthermore, the integration of renewable energy sources into the electricity grid (to reduce carbon emission and humanity's dependency on fossil fuels), complicates efforts to balance supply and demand, since their generation is intermittent and unpredictable. Traditionally, it has always been the supply side that has adapted to follow energy demand, however, in order to have a sustainable energy system for the future, the demand side will have to be better managed to match the available energy supply. In the first part of this thesis, we focus on understanding customers' energy consumption behavior (demand analytics). While previously, information about customer's energy consumption could be obtained only with coarse granularity (e.g., monthly or bimonthly), nowadays, using advanced metering infrastructure (or smart meters), utility companies are able to retrieve it in near real-time. By leveraging smart meter data, we then develop a versatile customer segmentation framework, track cluster changes over time, and identify key characteristics that define a cluster. Additionally, although household-level consumption is hard to predict, it can be used to improve aggregate-level forecasting by first segmenting the households into several clusters, forecasting the energy consumption of each cluster, and then aggregating those forecasts. The improvements provided by this strategy depend not only on the number of clusters, but also on the size of the customer base. Furthermore, we develop an approach to model the uncertainty of future demand. In contrast to previous work that used computationally expensive methods, such as simulation, bootstrapping, or ensemble, we construct prediction intervals directly using the time-varying conditional mean and variance of future demand. While analytics on customer energy data are indeed essential to understanding customer behavior, they could also lead to breaches of privacy, with all the attendant risks. The first part of this thesis closes by exploring symbolic representations of smart meter data which still allow learning algorithms to be performed on top of them, thus providing a trade-off between accurate analytics and the protection of customer privacy. In the second part of this thesis, we focus on mechanisms for incentivizing changes in customers' energy usage in order to maintain (electricity) grid stability, i.e., Demand Response (DR). We complement previous work in this area (which typically targeted large, industrial customers) by studying the application of DR to residential customers. We first study the influence of DR baselines, i.e., estimates of what customers would have consumed in the absence of a DR event. While the literature to date has focused on baseline accuracy and bias, we go beyond these concepts by explaining how a baseline affects customer participation in a DR event, and how it affects both the customer and company profit. We then discuss a strategy for matching the demand side with the supply side by using a multiunit auction performed by intelligent agents on behalf of customers. The thesis closes by eliciting behavioral incentives from the crowd of customers for promoting and maintaining customer engagement in DR programs

Proceedings of the 6th International Conference EEDAL'11 Energy Efficiency in Domestic Appliances and Lighting

This book contains the papers presented at the sixth international conference on Energy Efficiency in Domestic Appliances and Lighting. EEDAL'11 was organised in Copenhagen, Denmark in May 2011. This major international conference, which was previously been staged in Florence 1997, Naples 2000, Turin 2003, London 2006, Berlin 200h9a s been very successful in attracting an international community of stakeholders dealing with residential appliances, equipment, metering liagnhdti ng (including manufacturers, retailers, consumers, governments, international organisations aangde ncies, academia and experts) to discuss the progress achieved in technologies, behavioural aspects and poliacineds , the strategies that need to be implemented to further progress this important work. Potential readers who may benefit from this book include researchers, engineers, policymakers, and all those who can influence the design, selection, application, and operation of electrical appliances and lighting.JRC.F.7-Renewable Energ

Proceedings of the 8th International Conference on Energy Efficiency in Domestic Appliances and Lighting

At the EEDAL'15 conference 128 papers dealing with energy consumption and energy efficiency improvements for the residential sector have been presented. Papers focused policies and programmes, technologies and consumer behaviour. Special focus was on standards and labels, demand response and smart meters. All the paper s have been peer reviewed by experts in the sector.JRC.F.7-Renewables and Energy Efficienc

Electricity Demand: Measurement, modelling and management of UK homes

The need to achieve a transition to a low carbon economy has renewed interest in "energy efficiency" and what has become known as "demand side management". This thesis investigates the role of measurement and modelling in the management of domestic electricity demand. Practice and policy have, since the 1950s, tended to favour a "supply paradigm" centred on the imperative of increasing energy supply. Despite the upheaval of market liberalisation, and twenty years of climate change debate, the domestic electricity "culture" has changed very little. The first half of this thesis contributes to this subject by describing the complex development of the electricity system that we are familiar with today. Drawing upon technical, social and political themes, the current and emerging practices of measurement, modelling, and management are critiqued. It is argued that current practices require revaluation, if alternative, decentralised approaches are to receive a fair analysis. The thesis contributes in empirical terms by extending the evidence base and developing modelling tools for the analysis of domestic electricity use. Field data collected by the author concerning the power flow characteristics of domestic appliances are presented which identify the dynamic nature of domestic electrical loads. A modelling framework is then introduced that combines social and technical aspects of domestic energy demand, allowing synthesis of domestic load profiles and allowing comparison between localised interventions