Aggregation and remuneration in Demand-Response with a blockchain-based framework

This paper describes the possibility to use the blockchain technology for load and generation aggregation in a new distributed Demand Response (DR) service and customers remuneration system. The blockchain technology and the use of smart contracts for DR allow the creation of a distributed system in which customers can communicate directly, in a transparent, secure and traceable way, with the grid operator to provide their flexibility. In this paper, the DR problem formulation takes into account several aspects, which are periodically executed. First, the blockchain records customers’ energy consumption or production, then, the smart contract starts calculating the baseline and the potential support provided by each customer to fulfil the requested load adaptation. Customers’ availability for generation and load profile modulation is also taken into account, as well as their privacy and an updated definition of the roles of grid and market operators in a new Demand-Response scenario supported by the blockchain technology. The blockchain used is Hyperledger Fabric, since it turned to be flexible for smart contracts implementation while supporting multi-tenancy. Results show the possibility to successfully apply the blockchain technology to this particular topic, even considering privacy-preserving issues

A cluster-based baseline load calculation approach for individual industrial and commercial customer

Demand response (DR) in the wholesale electricity market provides an economical and efficient way for customers to participate in the trade during the DR event period. There are various methods to measure the performance of a DR program, among which customer baseline load (CBL) is the most important method in this regard. It provides a prediction of counterfactual consumption levels that customer load would have been without a DR program. Actually, it is an expected load profile. Since the calculation of CBL should be fair and simple, the typical methods that are based on the average model and regression model are the two widely used methods. In this paper, a cluster-based approach is proposed considering the multiple power usage patterns of an individual customer throughout the year. It divides loads of a customer into different types of power usage patterns and it implicitly incorporates the impact of weather and holiday into the CBL calculation. As a result, different baseline calculation approaches could be applied to each customer according to the type of his power usage patterns. Finally, several case studies are conducted on the actual utility meter data, through which the effectiveness of the proposed CBL calculation approach is verified

Achieving energy efficient districts: contributions through large-scale characterization and demand side management.

Buildings are increasingly expected to be more efficient and sustainable since they are essential to energy policies and climate change mitigation efforts. For this reason, it is very important to develop new energy models, with special attention to the residential sector. The present Thesis aims to justify the selection of the district scale as the optimal one to improve the energy performance of the built environment. In this way, renewable energy integration may be increased and innovative approaches such as demand side management may be carried out through the accurate characterization of districts. Several applications are shown to evaluate the solar potentials and the energy demands for entire regions by using 3D city models. The advantages offered by demand side management approaches in buildings and districts are investigated, presenting two applications that benefit from dynamic pricing strategies or the participation in reserve markets. The drawbacks of most current approaches on a large scale are highlighted, and a new tool capable of performing dynamic simulations of whole districts in a user-friendly and accurate way is presented. In addition, a methodology for a proper characterization of districts through monitoring is developed, validated, and used for two applications. The first one characterizes a district consisting of buildings with a limited use of air-conditioning, and the second one evaluates the benefits that could be obtained from the exploitation of the synergies between the buildings of a district. As a last contribution of this Thesis, a new comprehensive methodology for the characterization and optimization of any existing district is proposed.Se espera que los edificios sean cada vez más eficientes y sostenibles, puesto que son esenciales para las políticas energéticas y los esfuerzos hacia la mitigación del cambio climático. Por esta razón, es muy importante desarrollar nuevos modelos energéticos, con especial atención al sector residencial. La presente Tesis parte de que la escala de distrito es la óptima para mejorar el comportamiento de la edificación. Además, permite aumentar la integración de energías renovables y llevar a cabo planteamientos innovadores como la gestión de la demanda a través de una precisa caracterización de los distritos. Se muestran varias aplicaciones para la evaluación de los potenciales solares y las demandas energéticas de regiones enteras, usando modelos 3D de ciudades. Las ventajas ofrecidas por los procedimientos de gestión de la demanda en edificios y distritos también son investigadas, presentando dos aplicaciones que se benefician de estrategias de tarificación dinámica o de la participación en los mercados de reserva. Las desventajas de la mayoría de procedimientos actuales a gran escala también son destacadas, y se presenta una nueva herramienta capaz de llevar a cabo simulaciones dinámicas de distritos completos de forma simple y precisa. Además, se desarrolla una metodología para la caracterización apropiada de distritos a través de monitorización, validada y empleada en dos aplicaciones. La primera trata la caracterización un distrito compuesto por edificios con un uso limitado de la climatización, y la segunda la evaluación de los beneficios que podrían obtenerse de la explotación de las sinergias entre los edificios de un distrito. Como última contribución de la Tesis, se propone una nueva metodología completa para la caracterización y optimización de cualquier distrito existente.Premio Extraordinario de Doctorado U

Improvement of customer baselines for the evaluation of demand response through the use of physically-based load models

Demand Response (DR) is an opportunity and a concern for markets as well as power system flexibility. The deployment of DR depends on both knowledge on its performance and how to measure it effectively to provide adequate economic feedback. DR verification requires a baseline reference. This paper introduces a new baseline that provides an evaluation of response based on simple adjustment factors through physically-based models, tools which are also used in DR. The approach includes the detection of licit and gaming responses before and after DR. Results show that errors decrease by 10–15% with respect to conventional approaches.This work was supported by the Agencia Estatal de Investigación, Ministerio de Ciencia e Innovación (projects ENE-2016-78509-C3-2 P, RED2018-102618-T and ENE-2016-78509-C3-3P/AEI/10.13039/ 501100011033); the Ministerio de Educación (Spanish Government) under grant FPU17/02753, and EU-ERDF funds

Enabling Future Sustainability Transitions: An Urban Metabolism Approach to Los Angeles Pincetl et al. Enabling Future Sustainability Transitions

Summary: This synthesis article presents an overview of an urban metabolism (UM) approach using mixed methods and multiple sources of data for Los Angeles, California. We examine electric energy use in buildings and greenhouse gas emissions from electricity, and calculate embedded infrastructure life cycle effects, water use and solid waste streams in an attempt to better understand the urban flows and sinks in the Los Angeles region (city and county). This quantification is being conducted to help policy-makers better target energy conservation and efficiency programs, pinpoint best locations for distributed solar generation, and support the development of policies for greater environmental sustainability. It provides a framework to which many more UM flows can be added to create greater understanding of the study area's resource dependencies. Going forward, together with policy analysis, UM can help untangle the complex intertwined resource dependencies that cities must address as they attempt to increase their environmental sustainability

Data-Driven Key Performance Indicators and Datasets for Building Energy Flexibility: A Review and Perspectives

Energy flexibility, through short-term demand-side management (DSM) and energy storage technologies, is now seen as a major key to balancing the fluctuating supply in different energy grids with the energy demand of buildings. This is especially important when considering the intermittent nature of ever-growing renewable energy production, as well as the increasing dynamics of electricity demand in buildings. This paper provides a holistic review of (1) data-driven energy flexibility key performance indicators (KPIs) for buildings in the operational phase and (2) open datasets that can be used for testing energy flexibility KPIs. The review identifies a total of 81 data-driven KPIs from 91 recent publications. These KPIs were categorized and analyzed according to their type, complexity, scope, key stakeholders, data requirement, baseline requirement, resolution, and popularity. Moreover, 330 building datasets were collected and evaluated. Of those, 16 were deemed adequate to feature building performing demand response or building-to-grid (B2G) services. The DSM strategy, building scope, grid type, control strategy, needed data features, and usability of these selected 16 datasets were analyzed. This review reveals future opportunities to address limitations in the existing literature: (1) developing new data-driven methodologies to specifically evaluate different energy flexibility strategies and B2G services of existing buildings; (2) developing baseline-free KPIs that could be calculated from easily accessible building sensors and meter data; (3) devoting non-engineering efforts to promote building energy flexibility, such as designing utility programs, standardizing energy flexibility quantification and verification processes; and (4) curating datasets with proper description for energy flexibility assessments.Comment: 30 pages, 14 figures, 4 table

A Minimal Incentive-based Demand Response Program With Self Reported Baseline Mechanism

In this paper, we propose a novel incentive based Demand Response (DR) program with a self reported baseline mechanism. The System Operator (SO) managing the DR program recruits consumers or aggregators of DR resources. The recruited consumers are required to only report their baseline, which is the minimal information necessary for any DR program. During a DR event, a set of consumers, from this pool of recruited consumers, are randomly selected. The consumers are selected such that the required load reduction is delivered. The selected consumers, who reduce their load, are rewarded for their services and other recruited consumers, who deviate from their reported baseline, are penalized. The randomization in selection and penalty ensure that the baseline inflation is controlled. We also justify that the selection probability can be simultaneously used to control SO's cost. This allows the SO to design the mechanism such that its cost is almost optimal when there are no recruitment costs or at least significantly reduced otherwise. Finally, we also show that the proposed method of self-reported baseline outperforms other baseline estimation methods commonly used in practice