Dra. Matamala-Aguayo, Yolanda

Renewable energy resources are crucial for decarbonizing the energy sector. Distributed energy resources, such as renewable generation by microgrids, aid the transition to net-zero systems. As microgrids proliferate, renewable generation often exceeds self-supply capacities, necessitating either the sale of excess electricity or the implementation of demand-response strategies. The optimal approach for microgrids (selling or shifting demand) hinges on electricity prices. This article examines the interplay between microgrids, demand response, photovoltaic investments, and pricing mechanisms. A novel Stackelberg model is proposed to minimize microgrid investment and operational costs. The upper-level problem determines energy community investments and demand response levels based on electricity prices, while the lower-level problem addresses market dispatch and endogenous pricing for microgrids. Case studies with varying battery storage levels, demand response limits, and pricing mechanisms are conducted. The research results illustrate the responsiveness of market transactions to supply conditions and pricing. Key findings show that microgrid flexibility in demand response reduces grid dependency, prompting significant investments in solar energy and battery storage, driven by economic incentives, especially under spot market conditions. Investments in solar energy more than double when microgrids trade energy at spot prices compared to flat prices.

Detecting and characterizing malicious operations in electricity markets presents substantial challenges due to the innate complexity of power networks. This paper explores adversarial attacks in electricity markets, i.e., we consider the scenario in which malicious market participants make undetectable changes to the input data of other participants, resulting in significant negative impacts on their operations. We propose a novel methodology to generate adversarial attacks, which leverages linear optimization theory and statistical learning to generate suboptimal, feasible solutions or super-optimal, infeasible solutions that convincingly resemble optimal solutions. The methodology is general and applicable for generating adversarial attacks on optimization-based decision systems. We use it to analyze the potential economic losses, policy making, and social consequences resulting from compromised demand-side operations. In particular, we focus on scenarios where certain demand side-players, i.e., microgrids, or prosumers in a general sense, engage in malicious activities by targeting other microgrids and the independent system operator. We model the electricity market dynamics using an equilibrium problem with equilibrium constraints (EPEC). To make the model easier to solve, the EPEC is reformulated using a combination of the Fisher–Burmeister function and strong duality theorem. Results show that microgrids can artificially decrease its consumption by up to 12% without triggering alarms. Additionally, this amount can be increased by an additional 14% to 22%, contingent on the strategy employed to monitor the observable variables. We also study the implications of the increasing utilization of solar generation, showing its potential to mitigate the impact of adversarial attacks while also introducing new risks to the system.

Countries worldwide are transforming their energy systems to achieve Carbon-Neutrality. Investing in renewable resources-based technologies and implementing Carbon Capture and Storage (CCS) are common strategies to achieve higher sequestration levels. Negative emissions through Bioenergy with CCS are expected to play an essential role in the transition to full decarbonization. On top of that, biomass is a limited resource that depends on environmental factors, which create uncertainties related to the amount that can be sustainably provided to the energy system. Therefore, this study emphasizes the relevance of variability in carbon sequestration for achieving climate targets by 2050. This paper proposes a probabilistic approach that integrates the Global Change Analysis Model for Latin America (GCAM-LA) with a chance constraint approach. GCAM-LA is used to assess the impact on the energy sector of different limits of sustainable biomass and carbon budget scenarios. The risk associated with exceeding the sequestration capacity of a given region is modeled via Chance Constraint. Results show that electrification is an appropriate long-term decarbonization strategy. It smoothes the effects of uncertainty in sequestration capacity and responds to end-user demands. For this case study, higher levels of electrification are obtained at likelihood levels>66% for end-use sectors.

In order to reduce greenhouse gas emissions, countries worldwide are transforming their energy systems with higher shares of renewable energy and smart technologies for demand response. Microgrids play an essential role in the transformation of electric grids to smart grids. However, renewable sources present new challenges, particularly those of high variability, which creates uncertainties in the supply side that can affect the security of electricity access at affordable prices. This paper proposes a novel Stackelberg stochastic model to account for different sources of uncertainty. The Stackelberg model considers microgrids as leaders (upper-level problem) with uncertainty regarding the availability of wind and solar sources and electricity prices. Availability of renewable sources is modeled via chance constraints, which allows assessing the risk of microgrids over-committing supply levels. Uncertainty in electricity prices is modeled via a set of demand scenarios with a given probability distribution. The lower-level problem of the Stackelberg problem considers an electricity dispatch problem for each demand scenario. The proposed model allows measuring the strategic actions of microgrids when facing different types of uncertainties and how the smart grid should adapt to guarantee that demand levels are supplied. The results show the effectiveness of the proposed method. We find that microgrids risk levels above 30% do not correlate with further benefits, such as reduced electricity prices. We also identified that in average, depending on the social cost of carbon and demand level, microgrids can cover their own demand and supply 15% of the electricity demand in the grid.

Implementing nationally determined contributions is more challenging for developing countries given potential economic consequences. Chile, a developing economy, is committed to reaching carbon neutrality by 2050. To do so, Chile announced multiple mitigation strategies such as phasing out coal by 2040, peaking emissions by 2025, and developing renewable energies. Fortunately, Chile holds a prominent renewable potential, standing out for solar, but it also has a significant challenge decarbonizing an economy that heavily relies on fossil fuels. The contribution of this paper is twofold. First, the first country-level disaggregated version of GCAM Latin America (GCAM-LA) was developed, where all South American countries are modeled as an independent energy-economy region. This model includes Chile as a separated region and incorporates interactions among the energy, water, agriculture and land use, economy, and climate systems. Second, different decarbonization strategies to reach carbon neutrality by 2050 were obtained, considering technology availability and high renewable energy potential. Results indicate that carbon neutrality is feasible when enforcing different combinations of the current Chilean mitigation strategies, even delaying coal phase-out by five years and failure of developing advanced solar technologies. If some policies are not fully implemented, such as delaying coal phase-out by five years or failure of developing advanced solar technologies, carbon neutrality can be achieved by incurring in a higher capital cost in the power sector. Moreover, decarbonization is mainly driven by high electrification levels in the final demand sector, reaching 53.7%–62.9% of the total consumption. However, such levels of electrification are reduced, particularly in the transport sector, when Chile relies on negative emissions from the land use and forestry sector.

Technologies providing flexibility options to power systems, such as Power-To-X (PtX) technologies, have become more important with the increasing deployment of distributed energy resources, particularly microgrids. However, uncertainty in renewable resources creates ambiguity regarding the necessary PtX capacity to install. This paper proposes a two-stage stochastic Stackelberg approach for multi-energy microgrids, focusing on long-term investment decisions in PtX technologies and hourly operational strategies. The Stackelberg problem considers microgrids as leaders (upper level) and the independent system operator as a follower (lower level). In the first stage, investment levels for various PtX technologies are determined as one-time decisions. The second stage focuses on hourly operational decisions, including the integration of microgrids with the independent system operator with marginal endogenous prices. The results provide insights into how uncertainty in renewable generation and electric battery levels affect investment levels. Larger hydrogen and thermal storage volumes lead to more flexible and self-sufficient microgrid systems. In scenarios with higher flexibility, microgrids can: (1) satisfy up to 10% of the independent system operator demand using renewable electricity and (2) regulate supply variability by storing excess generation during peak periods and releasing it during low generation periods.