Local Flexibility Markets (LFMs) play a crucial role in modern electrical grids as they can address grid congestion by efficiently exploiting demand-side flexibility through market-clearing algorithms and incentive signals. However, demo projects worldwide offer limited access to detailed information that enables a good understanding of market design, estimating computational requirements, and exploring different algorithms at multiple levels. Accordingly, This paper introduces ORFLEX, an open-source platform for rapid testing and deploying local flexibility markets. ORFLEX exhibits salient features, including scalability, agile deployment, and secure interactions by leveraging cloud and edge infrastructure and Functions as a Service (FaaS). The platform aims to provide a go-to guide for LFM implementation. The proposal was validated using a straightforward market with a hierarchical configuration involving a market operator, a spot market aggregator, and many flexumers. Experimental results revealed the systems’s ability to assist in large-scale deployments while increasing the number of participants on the demand side. The methodical simulation insights will help market designers and regulators to properly develop LFMs and analyze the impacts of different clearing and demand-side flexibility estimations.

The escalating energy demand and peak consumption periods during winter pose strenuous challenges. The rapid growth of electricity demand intensifies the risk of energy shortages. Strategic measures are imperative to mitigate the impact of winter peak periods on the grid and the increasing electrical load from conventional heating systems. To address such a challenge, this paper presents a methodology for determining the optimal size and operation of electric thermal storage (ETS) in multi-zone residential apartment buildings by forming an optimization problem. The economic implications of employing ETS with time-of-use (TOU) price signals and its impact on load shifting are explored. A synthetic energy consumption dataset of a residential building in Quebec, Canada, is utilized to evaluate the proposed method. Simulation results demonstrate that the judicious use of ETS, especially with dynamic electricity rates, enhances consumer economic savings by 66.54% and satisfies the energy demand.

Distributed generation and energy storage technologies have helped SmartGrid projects gain great momentum over the last decade. However, despite a large number of pilot and demonstration projects, low-level information is often unavailable. Therefore, tools for defining and building different operation scenarios are required. These tools can facilitate adopting novel approaches to multi-domain energy management. This paper proposes a distributed, flexible co-simulation framework to integrate simulators from separate domains and platforms. Particularly, the proposed scheme enables the development of hybrid thermal-electric systems for smart buildings. In this study, an object-oriented approach to modeling electrical thermal storage (ETS) units is also suggested. The evaluation process is carried out using real-world data. A case study is practiced by designing a residential agent that performs model predictive control (MPC) of residential heating load in the presence of ETS. The results show that proper integration of ETS into Home Energy Management Systems (HEMSs) can achieve economic savings of up to 45 %. The findings of this study demonstrate ETS's high potential for reducing customer bills while satisfying users' comfort. Furthermore, they recommend practical strategies for short-term planning of smart grids by increasing their flexibility based on ETS-integrated Demand Response (DR) programs. © 2022 IEEE.

A global strategy which aims at providing a method to manage a PEMFC thermal behavior of an electric vehicle is proposed. The Energy Management System (EMS) plays the role of the power flow supervisor and provides the time-to-start the fuel cell based on the battery energy depletion during the trip. Taking into account the freezing operating conditions and given this time-to-start estimation, the fuel cell temperature management system calculates the most appropriate time to start heating the stack in order to reduce heat loss through the natural convection. When the stack is started, the exothermic reaction heat is used as a self-heating power source to further increase the stack temperature. The simulation results have shown that the proposed approach is efficient and can be implemented in real-time in Fuel Cell Electric Vehicles.