Two-stage algorithm for the management of distributed energy resources included in an aggregator's activities

The growing number of distributed energy resources in power systems, leads to the appearance of new entities and roles for the existing ones that affect the operation of the network. One of these entities with more relevance, is the aggregator, either independent or represented by public organizations. An aggregator manages small-sizemdistributed energy resources, creating a virtual amount of energy flexibility that can be used by it, to enablem participation in energy markets and capitalize the integration of distributed energy resources. This paperm proposes a two-stage optimization methodology for the operation of an aggregator regarding distributed energy mresources. In a first stage, the network part managed by the aggregator is scheduled, meaning at a macrom perspective, while in the second stage, it is assumed that the distributed energy resources are also scheduled mconsidering their operation. It is assumed that this second stage is enabled due to an aggregator’s communication infrastructure and interconnected management systems.This work has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 641794 (project DREAMGO) and from FEDER Funds through COMPETE program and from National Funds through FCT, under the project UID/EEA/00760/2013.info:eu-repo/semantics/publishedVersio

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

Definition of Remuneration in an Aggregator Using Clustering Algorithms

Currently, the use of demand response programs and renewable energy resource are a reality in the power distribution network. Therefore, an efficient and optimal energy resource management is required for fully benefit from these new concepts of power system as well as avoiding energy wasting. In this paper, a methodology is represented in order to support the aggregator activities, with the aim of participation in the electricity market negotiations using aggregated distributed energy resources and demand response programs. Moreover, the presented model demonstrates the benefits of aggregator participation while promoting their inclusion. Additionally, a case study will test and validate the proposed methodology, which considers a university campus distribution network as aggregator network including 20 consumers and 26 renewable producers

Demand response performance and uncertainty: A systematic literature review

The present review has been carried out, resorting to the PRISMA methodology, analyzing 218 published articles. A comprehensive analysis has been conducted regarding the consumer's role in the energy market. Moreover, the methods used to address demand response uncertainty and the strategies used to enhance performance and motivate participation have been reviewed. The authors find that participants will be willing to change their consumption pattern and behavior given that they have a complete awareness of the market environment, seeking the optimal decision. The authors also find that a contextual solution, giving the right signals according to the different behaviors and to the different types of participants in the DR event, can improve the performance of consumers' participation, providing a reliable response. DR is a mean of demand-side management, so both these concepts are addressed in the present paper. Finally, the pathways for future research are discussed.This article is a result of the project RETINA (NORTE-01-0145- FEDER-000062), supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). We also acknowledge the work facilities and equipment provided by GECAD research center (UIDB/00760/2020) to the project team, and grants CEECIND/02887/2017 and SFRH/BD/144200/2019.info:eu-repo/semantics/publishedVersio

Definition of Remuneration in an Aggregator Using Clustering Algorithms

Currently, the use of demand response programs and renewable energy resource are a reality in the power distribution network. Therefore, an efficient and optimal energy resource management is required for fully benefit from these new concepts of power system as well as avoiding energy wasting. In this paper, a methodologyis represented in order to support the aggregator activities, with the aim of participation in the electricity market negotiations using aggregated distributed energy resources and demand response programs. Moreover, the presented model demonstrates the benefits of aggregator participation while promoting their inclusion. Additionally, a case study will test and validate the proposed methodology, which considers a university campus distribution network as aggregator network including 20 consumers and 26 renewable producers.This work has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 641794 (project DREAMGO) and from FEDER Funds through COMPETE program and from National Funds through FCT, under the project UID/EEA/00760/2013.N/

Application of distinct demand response program during the ramping and sustained response period

The environmental concerns around energy, namely electricity, have driven attention to innovative approaches to fostering consumers participation in the whole energy system management. Accordingly, the concept of demand response provides incentives and signals no consumers to change the normal consumption patterns to increase the use of renewables, for example. The problem is that such response of consumers has a large amount of uncertainty. This paper proposes a methodology in which different demand response programs are activated and deactivated during an event to cover the demand response deviations from the target. Even after achieving the response target, if the actual response of consumers is reduced to a critical level, additional programs are activated. The proposed approach considers consumers participating in an aggregate way, supported by an aggregator. The case study in this paper accommodates three demand response programs, showing how different consumers are activated and remunerated for the provision of consumption reduction. It has been seen that the proposed methodology is flexible as desired to accommodate the uncertainty of consumers’ responses.This work has received funding from FEDER Funds through COMPETE program and from National Funds through (FCT) under the project COLORS (PTDC/EEI-EEE/28967/2017). The work has been done also in the scope of projects UIDB/00760/2020, and CEECIND/02887/2017, financed by FEDER Funds through COMPETE program and from National Funds through (FCT) . The authors would like to acknowledge the contribution of Omid Abrishambaf to this workinfo:eu-repo/semantics/publishedVersio

Evolution of the Electricity Distribution Networks : Active Management Architecture Schemes and Microgrid Control Functionalities

The power system transition to smart grids brings challenges to electricity distribution network development since it involves several stakeholders and actors whose needs must be met to be successful for the electricity network upgrade. The technological challenges arise mainly from the various distributed energy resources (DERs) integration and use and network optimization and security. End-customers play a central role in future network operations. Understanding the network’s evolution through possible network operational scenarios could create a dedicated and reliable roadmap for the various stakeholders’ use. This paper presents a method to develop the evolving operational scenarios and related management schemes, including microgrid control functionalities, and analyzes the evolution of electricity distribution networks considering medium and low voltage grids. The analysis consists of the dynamic descriptions of network operations and the static illustrations of the relationships among classified actors. The method and analysis use an object-oriented and standardized software modeling language, the unified modeling language (UML). Operational descriptions for the four evolution phases of electricity distribution networks are defined and analyzed by Enterprise Architect, a UML tool. This analysis is followed by the active management architecture schemes with the microgrid control functionalities. The graphical models and analysis generated can be used for scenario building in roadmap development, real-time simulations, and management system development. The developed method, presented with high-level use cases (HL-UCs), can be further used to develop and analyze several parallel running control algorithms for DERs providing ancillary services (ASs) in the evolving electricity distribution networks.© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Model for the integration of distributed energy resources in energy markets by an aggregator

The increase of distributed energy resources in energy systems and current legislation concerning the participation in energy markets, is causing a high wasted potential of energy supply and flexibility services. In this paper, it is proposed a methodology for the management and integration of distributed energy resources in energy systems and markets, through the application of an aggregator. Also, the aggregator provides demand response programs based on tariffs, thus enabling different types of participations. Aggregation is performed using K-Means clustering algorithm, and serves as basis for remuneration, where the aggregated energy and cost of resources is obtained. Given this methodology, the aggregator obtains the energy available and the minimum sell cost to negotiate in market, with the intent of obtaining profit in its operation. The methodology is validated through a case study, with 20 consumers and 25 distributed generators.This work has received funding from the following projects: NETEFFICITY Project (ANI | P2020 – 18015); and from FEDER Funds through COMPETE program and from National Funds through FCT under the project UID/EEA/00760/2013.info:eu-repo/semantics/publishedVersio

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

Economic Impact of Demand Response in the Scheduling of Distributed Energy Resources

Demand Response (DR) allows consumers to participate in energy markets, thus assuming an active role. However, the need of an aggregator capable of managing these resources and making decisions accordingly with the objectives of such resources has not been fully addressed. The aggregator activities are complex, and therefore, in the need of intelligent support to accomplish reasonable solutions. This paper proposes a methodology to evaluate the advantages of using DR programs in the resource rescheduling while classification and regression trees are introduced to support the aggregator in terms of scheduling and tariffs definition. Often these techniques are used to help the aggregator decide, as they also learn through training. Focus is given to the use of trees to predict and decide, the consumers' prices and reduction levels to apply, respectively. The case study has 548 distributed generators, 10 external suppliers and 20310 consumersThe present work was done and funded in the scope of the following projects: EUREKA - ITEA2 Project SEAS with project number 12004; ELECON Project, REA grant agreement No 318912 (FP7 PIRSES-GA-2012- 318912); H2020 DREAM-GO Project (Marie Sklodowska-Curie grant agreement No 641794); and UID/EEA/00760/2013 funded by FEDER Funds through COMPETE program and by National Funds through FCT.info:eu-repo/semantics/publishedVersio