Demand Response Contextual Remuneration of Prosumers with Distributed Storage

Prosumers are emerging in the power and energy market to provide load flexibility to smooth the use of distributed generation. The volatile behavior increases the production prediction complexity, and the demand side must take a step forward to participate in demand response events triggered by a community manager. If balance is achieved, the participants should be compensated for the discomfort caused. The authors in this paper propose a methodology to optimally manage a community, with a focus on the remuneration of community members for the provided flexibility. Four approaches were compared and evaluated, considering contextual tariffs. The obtained results show that it was possible to improve the fairness of the remuneration, which is an incentive and compensation for the loss of comfort. The single fair remuneration approach was more beneficial to the community manager, since the total remuneration was lower than the remaining approaches (163.81 m.u. in case study 3). From the prosumers’ side, considering a clustering method was more advantageous, since higher remuneration was distributed for the flexibility provided (196.27 m.u. in case study 3).This work is supported by FEDER Funds through COMPETE program and by National Funds through FCT under projects UIDP/00760/2020, UIDB/00760/2020, and CEECIND/01423/2021. Cátia Silva is supported by national funds through Fundação para a Ciência e a Tecnologia (FCT) with Ph.D. grant reference SFRH/BD/144200/2019.info:eu-repo/semantics/publishedVersio

Local Energy Markets: Paving the Path Towards Fully Transactive Energy Systems

Triggered by the increased fluctuations of renewable energy sources, the European Commission stated the need for integrated short-term energy markets (e.g., intraday), and recognized the facilitating role that local energy communities could play. In particular, microgrids and energy communities are expected to play a crucial part in guaranteeing the balance between generation and consumption on a local level. Local energy markets empower small players and provide a stepping stone towards fully transactive energy systems. In this paper we evaluate such a fully integrated transactive system by (1) modelling the energy resource management problem of a microgrid under uncertainty considering flexible loads and market participation (solved via two-stage stochastic programming), (2) modelling a wholesale market and a local market, and (3) coupling these elements into an integrated transactive energy simulation. Results under a realistic case study (varying prices and competitiveness of local markets) show the effectiveness of the transactive system resulting in a reduction of up to 75/% of the expected costs when local markets and flexibility are considered. This illustrates how local markets can facilitate the trade of energy, thereby increasing the tolerable penetration of renewable resources and facilitating the energy transition

Making Legacy Thermal Storage Heating fit for the Smart Grid

Collaborative paper with Oxford University Environmental Change Institute and Energy Local Ltd. The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Thermal storage heaters, charged using overnight off-peak electricity, have been used for domestic space heating in the UK and other countries since the 1980s. However, they have always been difficult for consumers to manage efficiently and, with the advent of a high proportion of renewables in the electricity generation mix, the time of day when they are charged needs to be more flexible. There is also a need to reduce peaks in the demand profile to allow distribution networks to support new sources of demand such as electric vehicles. We describe a trial of a smart control system that was retrofitted to a group of six dwellings with this form of heating, with the objectives of providing more convenient and efficient control for the users while varying the times at which charging is performed, to flatten the profile of demand and make use of locally-generated renewable electricity. The trial also employs a commercially-realistic combination of a static time-of-day tariff with a real time tariff dependent on local generation, to provide consumers with the opportunity and incentive to reduce their costs by varying times of use of appliances. Results from operation over the 2015-16 heating season indicate that the objectives are largely achieved. It is estimated that on an annualised and weather-adjusted basis most of the users have consumed less electricity than before intervention and their costs are less on the trial tariffs. Critical factors for success of this form of system are identified, particularly the need to facilitate hands-on control of heating by thrifty users and the importance of an effective and sustained user engagement programme when introducing the technology, to ensure users gain confidence through a readily-accessible source of support and advice

Bi-directional coordination of plug-in electric vehicles with economic model predictive control

© 2017 by the authors. Licensee MDPI, Basel, Switzerland. The emergence of plug-in electric vehicles (PEVs) is unveiling new opportunities to de-carbonise the vehicle parcs and promote sustainability in different parts of the globe. As battery technologies and PEV efficiency continue to improve, the use of electric cars as distributed energy resources is fast becoming a reality. While the distribution network operators (DNOs) strive to ensure grid balancing and reliability, the PEV owners primarily aim at maximising their economic benefits. However, given that the PEV batteries have limited capacities and the distribution network is constrained, smart techniques are required to coordinate the charging/discharging of the PEVs. Using the economic model predictive control (EMPC) technique, this paper proposes a decentralised optimisation algorithm for PEVs during the grid-To-vehicle (G2V) and vehicle-To-grid (V2G) operations. To capture the operational dynamics of the batteries, it considers the state-of-charge (SoC) at a given time as a discrete state space and investigates PEVs performance in V2G and G2V operations. In particular, this study exploits the variability in the energy tariff across different periods of the day to schedule V2G/G2V cycles using real data from the university's PEV infrastructure. The results show that by charging/discharging the vehicles during optimal time partitions, prosumers can take advantage of the price elasticity of supply to achieve net savings of about 63%

Spatial flexibility options in electricity market simulation tools: Deliverable D4.3

Project TradeRES - New Markets Design & Models for 100% Renewable Power Systems: https://traderes.eu/about/ABSTRACT: Deliverable D4.3 addresses the spatial flexibility options that are being considered by TradeRES models. D4.3 presents a report describing the spatial flexibility-related modelling components that are already implemented and those that are being designed for integration in TradeRES agent-based models. This report includes the main definitions, concepts and terminology related to spatial flexibility, as means to support the presentation of the specific models that are being developed by the project, namely about flow based market coupling, market spliting, nodal pricing, dynamic line rating, cross border intraday market, cross border reserve market, cross border capacity market, consumer flexibility aggregation, renewable energy aggregation, storage aggregation, electric vehicle aggregation and grid capacity.N/

A review of community electrical energy systems

This paper is aimed at new entrants into the field of electrical community energy. It reviews some of the work that is underway into community electrical energy projects. This review includes a summary of key issues and components which need consideration including some or all of the following; demand side management, energy storage (including vehicle to grid) and renewable generation. The paper looks further into the energy management schemes of these projects and summarises previously published methodology in the area

Modeling the controlled delivery power grid

Competitive energy markets, stricter regulation, and the integration of distributed renewable energy sources are forcing companies to reengineer energy production and distribution. The Controlled Delivery Power Grid is proposed as a novel approach to transport energy from generators to consumers. In this approach, energy distribution is performed in an asynchronous and distributed fashion. Much like the Internet, energy is delivered as addressable packets, which allow a controlled delivery of energy. As a proof-of-concept of the controllable delivery grid, two experimental test beds, one with integrated energy storage and another with no energy storage, were designed and built to evaluate the efficiency of a power distribution and scheduling scheme. Both test beds use a request-grant protocol where energy is supplied in discrete quantities. The performance of the system is measured in terms of the ability to satisfy requests from consumers. The results show high satisfaction ratios for distribution capacities that are smaller than the maximum demand. The distribution of energy is modelled with graph theory and as an Integer Linear Programming problem to minimize transmission losses and determine routes for energy flows in a network with distributed sources and consumers. The obtained results are compared with a heuristic approach based on the Dijkstra\u27s shortest path algorithm, which is proposed as a feasible approach to routing the transmission of packetized energy