A Framework for Flexible Loads Aggregation

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Data-Driven Aggregation Control for Thermoelectric Loads in Demand Response

Within the concept of a smart grid, aggregators have the task of coordinating the behavior of large sets of Distributed Energy Resources, each of them offering small power/energy capacities, which help to balance the power grid and can serve as providers of services. Adequate coordination strategies are required to optimally exploit these resources in the ancillary services market. However, deriving model-based control policies for them is complex due to the heterogeneity and uncertainty related to the large set of associated agents. Then, a data-driven model is an adequate solution for this sort of situation. This paper presents the application of the Youla-Kucera Data-Driven Control strategy for the development of an aggregator to regulate the power consumption of a set of thermoelectric refrigerators, avoiding the modeling process and directly designing a controller from data. A detailed simulation framework was executed to verify the validity of the proposed methodology. It is shown that the derived aggregator is able to offer frequency containment reserves service, achieving the required settling time of 30 seconds and with a tracking error below 4.7%. Copyright (c) 2022 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/

Enhanced EV charging algorithm considering data-driven workplace chargers categorization with multiple vehicle types

The increasing penetration of Electric Vehicles (EVs) presents significant challenges in integrating EV chargers. To address this, precise smart EV charging strategies are imperative to prevent a surge in peak power demand and ensure seamless charger integration. In this article, a smart EV charging pool algorithm employing optimal control is proposed. The main objective is to minimize the charge point operator's cost while maximizing its EV chargers’ flexibility. The algorithm adeptly manages the charger pilot signal standard and accommodates the non-ideal behavior of EV batteries across various vehicle types. It ensures the fulfillment of vehicle owners’ preferences regarding the departure state of charge. Additionally, we develop a data-driven characterization of EV workplace chargers, considering power levels and estimated battery capacities. A novel methodology for computing the EV battery's arrival state of charge is also introduced. The efficacy of the EV charging algorithm is evaluated through multiple simulation campaigns, ranging from individual charger responses to comprehensive charging pool analyses. Simulation results are compared with those of a typical minimum-time strategy, revealing cost reductions and significant power savings based on the flexibility of EV chargers. This novel algorithm emerges as a valuable tool for accurately managing the power demanded by an EV charging station, offering flexible services to the electrical grid

Characterization and Flexibility of a ThermoElectric Refrigeration Unit

The ThermoElectric Refrigerator (TER) is a solid-state energy-conversion technology which exploits the Peltier effect to convert electricity into thermal energy for heating or cooling. This system has been used in residential and commercial sectors. Therefore, TERs are potentially useful to offer energy services required by the electrical grid. In this article, a model of a TER is developed and characterized by experimental data. It is shown that the TER can operate as a flexible load by modifying the internal temperature set point. A Proportional-Integral controller able to follow the set-point change is used. Finally, a TER flexibility analysis is developed, achieving downward and upward flexibility in energy consumption

Remuneration Sensitivity Analysis in Prosumer and Aggregator Strategies by Controlling Electric Vehicle Chargers

The efficient use of energy resources is profoundly changing power grid regulation and policy. New forms of power generation coupled with storage and the presence of new, increasingly flexible loads such as electric vehicles enable the development of multi-agent planning systems based on new forms of interaction. For instance, consumers can take advantage of flexibility by interacting directly with the grid or through aggregators that bridge the gap between these end-users and traditional centralised markets. This paper aims to provide insight into the benefits for aggregators and end-users from a financial perspective by proposing a methodology that can be applied to different scenarios. End-users may provide flexibility services related to private vehicle charging stations or battery storage systems. The paper will analyse different remuneration levels for end-users by highlighting the most beneficial scenarios for aggregators and end-users and providing evidence on potential conflict of interests. The numerical results show that some consumers may benefit more from aggregation. This is because if taken individually, consumption habits do not allow the same flexibility when considering clusters of consumers with different behaviour. It is also shown that there are cases in which consumers do not seem to benefit from the presence of intermediate parties. We provide extensive numerical results to gain insight for better decision making

Optimal Strategy to Exploit the Flexibility of an Electric Vehicle Charging Station

The increasing use of electric vehicles connected to the power grid gives rise to challenges in the vehicle charging coordination, cost management, and provision of potential services to the grid. Scheduling of the power in an electric vehicle charging station is a quite challenging task, considering time-variant prices, customers with different charging time preferences, and the impact on the grid operations. The latter aspect can be addressed by exploiting the vehicle charging flexibility. In this article, a specific definition of flexibility to be used for an electric vehicle charging station is provided. Two optimal charging strategies are then proposed and evaluated, with the purpose of determining which strategy can offer spinning reserve services to the electrical grid, reducing at the same time the operation costs of the charging station. These strategies are based on a novel formulation of an economic model predictive control algorithm, aimed at minimising the charging station operation cost, and on a novel formulation of the flexibility capacity maximisation, while reducing the operation costs. These formulations incorporate the uncertainty in the arrival time and state of charge of the electric vehicles at their arrival. Both strategies lead to a considerable reduction of the costs with respect to a simple minimum time charging strategy, taken as the benchmark. In particular, the strategy that also accounts for flexibility maximisation emerges as a new tool for maintaining the grid balance giving cost savings to the charging stations

Co-simulation Management Algorithm for Distribution System Operation with Real-Time Simulator

This article presents a co-simulation framework consistent with the real-time simulation for operational analysis of electrical distribution networks. Realtime simulators have become a fundamental tool for testing and optimising control strategies in a safe and controlled environment. The proposed methodology outlines the steps required for setting up, controlling, and monitoring an electrical grid using a real-time simulator. The framework proposes the use of the Message Queuing Telemetry Transport communication between the electrical grid module and an external coordinator. An algorithm based on the Python programming language is proposed to manage the real-time simulation, create the grid topology, and communicate with the external coordinator. The implementation of the electrical network and the validation of the real-time simulator network are also presented. The article concludes that the proposed framework can improve the performance and flexibility of co-simulation for studies on the penetration of power electronics-based renewable sources

Co-simulation Management Algorithm for Distribution System Operation with Real-Time Simulator

This article presents a co-simulation framework consistent with the real-time simulation for operational analysis of electrical distribution networks. Realtime simulators have become a fundamental tool for testing and optimising control strategies in a safe and controlled environment. The proposed methodology outlines the steps required for setting up, controlling, and monitoring an electrical grid using a real-time simulator. The framework proposes the use of the Message Queuing Telemetry Transport communication between the electrical grid module and an external coordinator. An algorithm based on the Python programming language is proposed to manage the real-time simulation, create the grid topology, and communicate with the external coordinator. The implementation of the electrical network and the validation of the real-time simulator network are also presented. The article concludes that the proposed framework can improve the performance and flexibility of co-simulation for studies on the penetration of power electronics-based renewable sources

Coordination of specialised energy aggregators for balancing service provision

In the present context of evolution of the power and energy systems, more flexibility is required on the generation and demand side, to cope with the increasing uncertainty mostly introduced by variable renewable energy resources. This paper presents a conceptual framework that encompasses different types of aggregators, including local network aggregators, demand-side general aggregators, specialised energy aggregators (SEAs), and energy community aggregators. In this framework, this paper focuses on the coordination of SEAs to provide balancing services to the system operator. Each SEA manages a specific type of load, so that these loads can be managed by exploiting their control capabilities in a detailed way considering response time, dynamics and available flexibility. Moreover, the presence of the SEAs increases the privacy protection of the users, as only the information on a specific type of user's load is sent to the SEA. The SEA Coordinator interacts with the Balancing Service Provider aimed at procuring frequency containment, frequency restoration and replacement reserve services. This paper contains the SEA Coordinator formulation, information exchange and control operation strategies. Case study applications are presented by using SEAs for three specific types of loads (thermoelectric refrigerator, water booster pressure systems and electric vehicle charging stations). The results show how the control algorithm of the SEA Coordinator is effective in providing balancing services at different timings with the different types of loads. Various scenarios are considered, comparing an ideal situation without command propagation delays with realistic situations that take into account the command propagation delays

Omecamtiv mecarbil in chronic heart failure with reduced ejection fraction, GALACTIC‐HF: baseline characteristics and comparison with contemporary clinical trials

Aims: The safety and efficacy of the novel selective cardiac myosin activator, omecamtiv mecarbil, in patients with heart failure with reduced ejection fraction (HFrEF) is tested in the Global Approach to Lowering Adverse Cardiac outcomes Through Improving Contractility in Heart Failure (GALACTIC‐HF) trial. Here we describe the baseline characteristics of participants in GALACTIC‐HF and how these compare with other contemporary trials. Methods and Results: Adults with established HFrEF, New York Heart Association functional class (NYHA) ≥ II, EF ≤35%, elevated natriuretic peptides and either current hospitalization for HF or history of hospitalization/ emergency department visit for HF within a year were randomized to either placebo or omecamtiv mecarbil (pharmacokinetic‐guided dosing: 25, 37.5 or 50 mg bid). 8256 patients [male (79%), non‐white (22%), mean age 65 years] were enrolled with a mean EF 27%, ischemic etiology in 54%, NYHA II 53% and III/IV 47%, and median NT‐proBNP 1971 pg/mL. HF therapies at baseline were among the most effectively employed in contemporary HF trials. GALACTIC‐HF randomized patients representative of recent HF registries and trials with substantial numbers of patients also having characteristics understudied in previous trials including more from North America (n = 1386), enrolled as inpatients (n = 2084), systolic blood pressure < 100 mmHg (n = 1127), estimated glomerular filtration rate < 30 mL/min/1.73 m2 (n = 528), and treated with sacubitril‐valsartan at baseline (n = 1594). Conclusions: GALACTIC‐HF enrolled a well‐treated, high‐risk population from both inpatient and outpatient settings, which will provide a definitive evaluation of the efficacy and safety of this novel therapy, as well as informing its potential future implementation