In this work, we address the problem of quadrotor flexible formation control. To this end, a control algorithm is proposed to achieve geometric platoon formations based exclusively on similarity transformations (rotation, translation, and scale) rather than rigid structures with fixed geometric references for the quadrotors. Firstly, the dynamic model of the quadrotor is established along with a nonlinear stabilizing control that uses saturated inputs. Then, we introduce the flexible formation behavior into the resulting quadrotor’s lateral and longitudinal subsystems using a control law that combines the transformation of a chain of integrators into a system described by a single integrator, and a shape optimization cost function. The optimization criterion consists of a geometric deformation measure term inspired by the Procrustes problem, which is a shape-alignment problem. Results are validated using the nonlinear quadrotor model in a simulated environment based on platforms such as ROS 2 and Gazebo, providing a realistic approximation to experiments with physical hardware.

Isolated power systems, even in renewable-rich areas, remain heavily dependent on imported fossil fuels. Regulatory reforms largely target national interconnected grids, leaving these systems overlooked, and few regulatory frameworks extend beyond techno-economic analysis. This paper introduces a novel and comprehensive methodological framework to update the regulation of isolated power systems and enable their energy transition. It delivers a step-by-step, interdisciplinary, multi-actor process for diagnosing regulatory and market barriers, designing reforms, and producing legislation-ready recommendations. The framework comprises five main stages: (1) Principles definition and team formation, (2) Interdisciplinary and multi-actor diagnosis, (3) Interdisciplinary analysis and proposals development, (4) Synthesis and verification, and (5) Final proposal. Its objectives aim to accelerate the adoption of renewable and emerging technologies, reduce investor uncertainty, and involve local participation. We demonstrate the framework's application by analyzing Chile's isolated power systems and aligning regulatory proposals with local needs, regional and national energy-environmental goals. Resulting reform proposals include: expansion of regulatory principles; a new taxonomy for classifying power systems, plus a transition pathway between categories; an auction-based co-planning process; a revised tariff structure; and mechanisms for early citizen participation and transparency. Beyond offering Chilean policymakers concrete regulatory reforms for isolated power systems and their energy transition, the framework is readily adaptable to other countries facing similar challenges.

This work investigates the multi-agent bipartite consensus problem in the presence of switching antagonistic interactions. For leaderless multi-agent systems communicating over a structurally balanced sign graph with switching antagonistic interactions, we show that, whenever the sum of the agents’ initial conditions on both subsets of a bipartite graph are identical, the agents converge to the origin. For multi-agent systems communicating over balanced bipartite graphs, that have achieved or are initially in consensus, we show that bipartite consensus is not admissible. We demonstrate that switching antagonistic interactions in the multi-agent bipartite consensus problem results in a multi-agent consensus to the origin. For leader-based multi-agent systems with antagonistic interactions, the bipartite consensus problem becomes a forced bipartite consensus problem with applications in robotics, for example, Unmanned Aerial Vehicle (UAV) flight formation. An application of forced bipartite consensus with switching antagonistic interactions to a quadrotor formation is presented. The results are validated using simulations.

Chile enjoys one of the largest solar irradiance levels globally. Although large-scale photovoltaic (PV) plants have flourished, the integration of medium-scale PV plants in medium-voltage (MV) distribution networks, only account for a quarter of Chile’s installed PV capacity. One of the main barriers for its deployment is the sluggish connection assessment process. To breach the gap, this paper presents a computational platform, VisionDERRED, that leverages the capabilities of open-source software Julia and OpenDSS, for the connection assessment of distributed generation (DG) projects to MV networks, following specific regulations from Chile. This contributes not only to automating the connection assessment process, but also by enabling finding the theoretical maximum admissible DG size per MV node. This allows building a nodal hosting capacity map, useful for project developers and policy makers. The capabilities of the platform are demonstrated in a real MV feeder case study in Osorno, Chile.

The high integration of variable renewable sources in electric power systems entails a series of challenges inherent to their intrinsic variability. A critical challenge is to correctly value the water available in reservoirs in hydrothermal systems, considering the flexibility that it provides. In this context, this paper proposes a medium-term multistage stochastic optimization model for the hydrothermal scheduling problem solved with the stochastic dual dynamic programming algorithm. The proposed model includes operational constraints and simplified mathematical expressions of relevant operational effects that allow more informed assessment of the water value by considering, among others, the flexibility necessary for the operation of the system. In addition, the hydrological uncertainty in the model is represented by a vector autoregressive process, which allows capturing spatio-temporal correlations between the different hydro inflows. A calibration method for the simplified mathematical expressions of operational effects is also proposed, which allows a detailed short-term operational model to be correctly linked to the proposed medium-term linear model. Through extensive experiments for the Chilean power system, the results show that the difference between the expected operating costs of the proposed medium-term model, and the costs obtained through a detailed short-term operational model was only 0.1%, in contrast to the 9.3% difference obtained when a simpler base model is employed. This shows the effectiveness of the proposed approach. Further, this difference is also reflected in the estimation of the water value, which is critical in water shortage situations.

Hybrid AC/DC microgrids (H-MGs) are a prominent solution for integrating distributed generation and modern AC and DC loads. However, controlling these systems is challenging as multiple electrical variables need to be controlled and coordinated. To provide flexibility to the control system, these variables can be regulated to specific values or within secure bands. This paper proposes a set of distributed model predictive control schemes for the secondary control level to control certain variables to specific values and other variables within secure pre-defined bands into H-MGs. Specifically, optimal dispatch of active and reactive power is achieved while frequency and voltages are regulated within secure bands in H-MGs. Dynamic models of AC generators, DC generators and interlinking converters along with their novel multi-objective cost functions are developed in constrained distributed predictive optimisation problems to simultaneously achieve the aforementioned objectives via information sharing. Extensive simulation work validates the performance of this proposal.

Soiling in solar panels causes a decrease in their ability to capturing solar irradiance, thus reducing the module’s power output. To reduce losses due to soiling, the panels are cleaned. This cleaning represents a relevant share of the operation and maintenance cost for solar farms, for which there are different types of technologies available with different costs and duration. In this context, this paper proposes a method that allows scheduling the dates on which cleaning generates greater utility in terms of income from energy sales and costs associated with cleaning. For this, two optimization models that deliver a schedule of dates where the best income-cost balance is obtained, are proposed and compared: a deterministic Mixed Integer Linear Problem and a stochastic Markov Decision Process. Numerical results show that both models outperform the baseline case by ∼ 4.6%. A simulator was built and both models were compared to the baseline case for 10,000 rainfall and irradiance scenarios. The stochastic model outperformed both models for all scenarios, thus proving that modeling rainfalls increases profitability in the face of uncertainty.

This paper investigates the role of fast-acting energy storage systems in transmission expansion planning, by allowing higher transfers through the network during normal operation. This is achieved by considering the ability of energy storage systems to provide real and reactive power reserves after forced single-circuit outages to prevent line overloading and voltage level violations in post-contingency states, and by applying the corrective N−1 security criterion. A computational tool is presented to solve the multi-year transmission expansion problem with multiple scenarios of availability of renewable energy sources. The model is solved using the FICO Xpress software. The 2022–2037 Chilean transmission expansion plan is used as a case study, given the high need for flexibility to integrate 29.5 GW of new solar and wind generation capacity several hundred kilometers from its load center, with a system peak demand of 16.5 GW. The results obtained show that fast-acting energy storage systems reduce the cost of the investment plan by USD 712 million (-18%) mainly because it requires 5 GWh less of conventional storage capacity (-19%), allowing the system operator to increase the usage of the existing transmission network, and providing the central planner with a deferral option for the construction of new transmission lines.

To technically inform policymakers about the potential of Chilean distribution networks to accommodate distributed generation, this work presents a computational platform developed for the first Industrial Research Chair between CENTRA at Universidad Adolfo Ibáñez and ACESOL. The platform enables automated modeling of integrated real MV-LV distribution networks, ensuring electrical connectivity and data completeness, particularly developed and tested for the distribution network database of Chile's Superintendency of Electricity and Fuels. The platform also proposes a clustering tool for allocating real load profiles, minimizing differences against aggregated power records at the head of the modeled MV feeder. The platform is used to assess the hosting capacity for residential PV systems in real MV-LV feeders from Valdivia and Osorno in Chile, demonstrating its capabilities and functionalities, but also quantifying the potential for integrating large amounts of distributed generation in the south of Chile. and the technical barriers that limit them.

Second-to-second renewable power fluctuations can severely hinder the frequency regulation performance of modern isolated microgrids, as these typically have a low inertia and significant renewable energy integration. In this context, the present paper studies the coordinated control of Thermostatically Controlled Loads (TCLs) for managing short-term power imbalances, and their integration in microgrid operations through the use of aggregate TCL models. In particular, two computationally efficient and accurate aggregate TCL models are developed: a virtual battery model representing the aggregate flexibility of TCLs considering solar irradiance heat gains and wall/floor heat transfers, and a frequency transient model representing the aggregate dynamics of a TCL collection considering communication delays and the presence of model uncertainty and time-variability. The proposed aggregate TCL models are then used to design a practical Energy Management System (EMS) integrating TCL flexibility, and study the impact of TCL integration on microgrid operation and frequency control. Computational experiments using detailed frequency transient and thermal dynamic models are presented, demonstrating the accuracy of the proposed aggregate TCL models, as well as the economic and reliability benefits resulting from using these aggregate models to integrate TCLs in microgrid operations.

This paper analyzes the impacts of the first wave of COVID-19 (March 2020 -September 2020) on the electricity demand of different types of consumers in Chile, including residential, commercial, and industrial demand. We leverage data from 230 thousand smart meters of residential and commercial consumers in 32 communes of Santiago (the capital city of Chile), which allows us to investigate the evolution of their demands with an hourly temporal resolution. Additionally, we use demand data of large industrial consumers provided by the Chilean system operator to study the impact of the pandemic on different economic sectors. This paper demonstrates that the COVID-19 pandemic, and the associated containment measures, have featured a drastically different impact on the various types of consumers in Chile. In particular, we show that the demand of residential consumers has increased throughout the first wave, even when we isolate the effects of the pandemic from those related to weather. Furthermore, we study how these effects change in different communes of Santiago, contrasting our findings with the socio-economic levels of the population. In effect, we find different demand response patterns depending on the socio-economic background of consumers. We also show that commercial demand has significantly declined due to the containment measures implemented and that the hospitality and construction economic sectors have been the most affected in the country.

Second-to-second power imbalances stemming from renewable generation can have a large impact on the frequency regulation performance of isolated microgrids, as these are characterized by low inertia and, more commonly nowadays, significant renewable energy penetration. Thus, the present paper develops a novel frequency-constrained Energy Management System (EMS) that takes into account the impact of short-term power fluctuations on the microgrid's operation and frequency regulation performance. The proposed EMS model is based on accurate linear equations describing frequency deviation, rate-of-change-of-frequency, and regulation provision in daily microgrid operations. Dynamic simulations on a realistic CIGRE benchmark test system show the economic and reliability benefits of the presented EMS model, highlighting the need of incorporating fast power fluctuations and their impact on frequency dynamics in EMSs for sustainable isolated microgrids.

Several countries are adopting plans to reduce the contaminant emissions from the energy sector through renewable energy integration and restrictions on fossil fuel generation. This process poses important computational and methodological challenges on expansion planning modeling due to the operational details needed for a proper analysis. In this context, this paper develops a planning model including an effective representation of the operational aspects of the system to understand the key role of flexible resources under strong decarbonization processes in highly renewable power systems. A case study is developed for the Chilean power system, which is currently undergoing an ambitious coal phase-out process, including the analysis of a scenario that leads to a completely renewable generation mix. The results show that highly renewable generation mixes are feasible, but rely on an effective balance of the key flexibility attributes of the system including ramping, storage, and transmission capacities. Further, such balance allows for faster decarbonization goals to remain in a similar cost range, through the deployment of flexible capacity in earlier stages of the planning horizon.

The massive growth in the integration of variable renewable energy sources is producing several challenges in the operation of power systems and its associated markets. In this context, flexibility has become a critical attribute to allow the system to react to changes in generation or demand levels. Thus, it is critical for market signals at both short and long term scales to include flexibility features, to align agents' incentives with systemic flexibility requirements. In this paper, different pricing schemes for short-term markets are studied, based on various relaxations of the unit commitment problem, including convex-hull approximations, with the aim of representing operational flexibility requirements in a more explicit way. Extensive simulations illustrate the performance of the proposed schemes, as compared to conventional ones, in terms of the capability of the system to properly incentivize flexibility attributes, resulting in better agents' cost recovery and more variable renewable energy utilization. The results show that short-term pricing schemes considered improve the long-term signals for flexible investments but additional changes to market design are still required. Thus, there is a need to revisit historical practices for pricing rules by incorporating additional flexibility-related attributes into them. Several alternatives are discussed and policy recommendations based on these considerations are provided.

A low-complexity decision model for a Home Energy Management System is proposed to follow demand trajectory sets received from a Demand Side Response aggregator. This model is designed to reduce its computational complexity and being solved by low performance processors using available Single-Board Computers as a proof of concept. To decrease the computational complexity is proposed a two-stage model, where the first stage evaluates the hourly appliance scheduling using a relaxed set of restrictions, and the second stage evaluates a reduced set of appliances in a intra-hourly interval with a detailed characterization of the scheduled appliance properties. Simulations results show the effectiveness of the proposed algorithm to follow trajectories for different sets of home appliances and operational conditions. For the studied cases, the model presents deviations in the demand for the 3.2% of the cases in the first-stage and a 12% for the second-stage model. Results show that the proposed model can schedule available appliances according to the demand aggregator requirements in a limited solving time with diverse hardware.

Due to the low-inertia and significant renewable generation variability in isolated microgrids, short time-scale fluctuations in the order of seconds can have a large impact on a microgrid’s frequency regulation performance. In this context, the present paper presents a mathematical model for an Energy Management System (EMS) that takes into account the operational impact of the short-term fluctuations stemming from renewable generation rapid changes, and the role that renewable curtailment and batteries, including their degradation, can play to counter-balance these variations. Computational experiments on the real Kasabonika Lake First Nation microgrid and CIGRE benchmark test system show the operational benefits of the proposed EMS, highlighting the need to properly model short-term fluctuations and battery degradation in EMS for isolated microgrids with significant renewable integration.

Demand aggregators are expected to have a key role in future electricity systems. More specifically, aggregators can facilitate the harnessing of consumers’ flexibility. This paper focuses on understanding the value of the aggregator in terms of aggregation of both flexibility and information. We consider the aggregation of flexibility as the ability to exercise a direct control over loads, while the aggregation of information refers to knowledge of the flexibility characteristics of the consumers. Several game theory formulations are used to model the interaction between the energy provider, consumers and the aggregator, each with a different information structure. We develop a potential game to obtain the Nash equilibrium of the non-cooperative game with complete information and we analyze the system dynamics of consumers using the adaptive expectations method in an incomplete information scenario. Several key insights about the value of aggregators are found. In particular, the value of the aggregator is mainly related to the aggregation of information rather than flexibility, and flexibility is valuable only when it can be coordinated. In this sense, prices are not enough to guarantee an effective coordination.

Wildfires can pose a major threat to the secure operation of power networks. Chile, California, and Australia have suffered from recent wildfires that have induced considerable power supply cuts. Further, as power systems move to a significant integration of variable renewable energy sources, successfully managing the impact of wildfires on the power supply can become even more challenging due to the joint uncertainty in wildfire trajectories and the power injections from wind and solar farms. Motivated by this, this paper develops a practical decision-support approach that concatenates a stochastic wildfire simulation method with an attacker-defender model that aims to find a worst-case realization for (i) transmission line and generator contingencies, out of those that can potentially be affected by a given wildfire scenario, and for (ii) wind and solar power trajectories, based on a max-min structure where the inner min problem represents a best adaptive response on generator dispatch actions. Further, this paper proposes an evaluation framework to assess the power supply security of various power system topology configurations, under the assumption of limited transmission switching capabilities, and based on the simulation of several wildfire evolution scenarios. Extensive computational experiments are carried out on two representations of the Chilean power network with up to 278 buses, showing the practical effectiveness of the proposed approach for enhancing wildfire resilience in highly renewable power systems. (c) 2021 Elsevier B.V. All rights reserved.

Chile presents an extended use of low-quality firewood for residential heating. This raises serious concerns, mainly related to health problems due to particulate emissions from firewood combustion. Several initiatives have been considered by the Chilean government to address this, including heat electrification goals. In addition, the Chilean power system is undergoing a decarbonization process. This paper analyzes the impacts of firewood heat electrification over the Chilean power system’s decarbonization process until 2044, based on a capacity expansion planning model and an estimation of firewood heat demand, to account for major changes in load shape and spatial distribution. The results show that there is a large increase in the power system total cost (ranging from 5.6% to 19.4%), with flexibility requirements becoming larger in the south. Technologies that strongly depend on local resource availability, such as wind and concentrated solar power, present changes in their systemic value, altering the need for storage and flexibility. Also, aspects such as household insulation, electrification efficiency, and smart heating technologies would significantly impact the process in terms of cost reduction and optimal generation mix. The policy implications for the Chilean power system are presented, within the broader discussion of the overall firewood problem.

As electric vehicles surpassed the 5.1 million-vehicle threshold in 2018, charging stations are becoming a growing necessity to allow electric vehicles charging while parked away from home. In addition, by harnessing the inherent flexibility of the charging process, charging stations can also provide services to the grid, such as frequency and voltage regulation. However, given that full-charge using state-of-the-art technology can take a minimum of nearly 30 min, drivers may face queues and uncertainty on the availability of charging sockets. To address this issue this work presents a model to determine the optimal management of charging stations using advance reservations. In the model, charging stations are assumed to participate in the energy and regulation markets, in addition to providing charging services to the vehicles. On the other hand, users are modeled using Satisficing decision model. The optimal management strategies are characterized by different charging tariffs and reservation fees, and their impact is analyzed in terms of the resulting charging profiles and welfare of electric vehicle users. (C) 2020 Elsevier Ltd. All rights reserved.