The integration of variable renewable energy sources into power grids presents significant challenges and opportunities. Among the most promising solutions is the deployment of Battery Energy Storage Systems (BESS), which can offer multiple services such as peak shaving, load shifting, ancillary services, backup for critical loads, among others. However, the installation of BESS has been limited by their high costs (despite a sustained year-over-year reduction) and by regulatory challenges, particularly regarding the remuneration mechanisms for each service they provide. In this context, this work examines regulatory frameworks and market incentives, and evaluates the operational and financial indicators of photovoltaic (PV) power plants with BESS to determine their economic viability in the utility-scale market. A two-stage power system cost optimization model is proposed (BESS installation and operation), which considers energy and reserve allocation, installation of DC-coupled batteries, and the expansion of existing solar plants. In addition, the model incorporates battery state of charge and state of health, based on a PV+BESS simulation implemented using the System Advisor Model (SAM) software, in order to capture the nonlinearities and degradation behavior of BESS. The model is applied to the Chilean power system as a case study, using a 26 node network for the BESS installation and DC solar capacity expansion stage, and a 212 node network for the operation stage, with 311 solar plants and over 1000 generating units in total, considering the most relevant nodes of the national electric grid. Several scenarios were simulated, mainly with different probabilities of reserve activation. This study provides valuable insights into how PV+BESS plants can be effectively integrated into the energy market, primarily by participating in the provision of reserves for frequency control, thereby supporting broader adoption of renewable energy technologies. The results show that the vast majority of BESS installations are used to provide ancillary services rather than for energy arbitrage, and that most solar plants expand their DC capacity, enhancing implicit storage due to its lower cost compared to explicit storage (BESS). Additionally, battery installations are distributed across the country, indicating that distributed storage is needed to operate the power system at minimum cost, rather than concentrating storage in a specific region. Furthermore, under the regulatory frameworks and remuneration schemes considered, location and solar resource do not significantly affect economic feasibility; the solutions preferred by the Independent System Operator (ISO) may not be optimal for individual plant operators (who are often the owners of the plants); and battery degradation varies considerably across plants and scenarios. Therefore, detailed studies considering both centralized and independent perspectives are required for the effective deployment of energy storage systems across an electric grid.

Climatic phenomena, particularly hydrological droughts, have led to significant changes in reservoir operation strategies. Multipurpose reservoir operations are essential for effectively managing stored water resources for various activities like electricity generation and agricultural irrigation. Despite considerable efforts to support decision making for each economic activity, there remains a weak integration across these sectors in joint analyses. To address this, an integrated approach combining a model of a large power system and a model at the basin scale is proposed to analyze the operation of both power and agricultural systems. This approach allows evaluation of the operating policies of a multipurpose reservoir and its performance at both the local and regional scales under different hydrological scenarios. A modification is implemented whereby the priority of water extraction to agricultural users is increased. Its effects are assessed for different hydrological trajectories in a case study in the Laja Lake basin in southern Chile, the biggest Chilean basin with a capacity of up to 5,500 Hm3. The Laja Lake, a multipurpose reservoir with substantial hydroelectric generation capacity and extensive agricultural areas plays a crucial role in the operation of the national power system. Based on an analysis of 2025, it is demonstrated that hydrological changes directly impact electrical and agricultural performance. Drought conditions increase thermal generation, costs, emission intensity, and water deficits. Furthermore, the policy modification reveals tradeoffs between the power sector's emissions and agricultural water deficits. For drier scenarios, increasing agricultural extraction priority results in low additional operational costs and emissions from the power system, which supports adopting a policy aligned with netzero objectives.

Many regions of the planet are exposed to seismic hazards that can cause devastating consequences on power systems. Due to these systems' crucial role, the evaluation and planning for their safe and reliable operation are paramount. This paper develops a novel data -driven optimization framework to assess the power network's seismic resilience and plan cost-effective investments for its enhancement. Under a robust optimization scheme, an earthquake attacker-defender model finds the worst -case realization of random earthquake network contingencies within an uncertainty set defined with a large number of scenarios generated by state-of-the-art engineering methods. Moreover, data -driven stochastic -robust optimization is employed in a two -stage seismic -resilient power network planning model, leveraging multiple seismic sources' distributional information. Transmission line expansions and siting and sizing of battery energy storage systems are decided in the first stage, while the second stage decides operational variables. Experiments on a 281 -node Chilean power system provide insights for seismic -resilient planning and demonstrate the efficiency of the proposed approach.

The stochastic dual dynamic programming (SDDP) algorithm introduced by Pereira and Pinto in 1991 has sparked essential research in the context of water resources management, mainly due to its ability to address large-scale multistage stochastic problems. This paper aims to provide a review of 32 years of research since the publication of the SDDP algorithm. A systematic academic literature search identified 174 scientific papers on water resource management published in 96 different journals. A bibliometric analysis is conducted to identify the main methods used to tackle this type of problem and to determine recent and future research trends. Our analysis reveals that stochastic dynamic programming, which was initially the most used approach, has now been replaced by multistage stochastic programming. Risk-averse and robust approaches are also gaining strength in recent years due to uncertainty related to climate change. Water inflows have been the main source of uncertainty considered in the literature by far, followed by, e.g., electricity demand, electricity prices, fuel costs, and renewable energy availability. In addition, as computational capacity continues to increase, aspects of nonlinearities, disaggregated networks, and different water management strategies are increasingly considered to make modeling more realistic. This work suggests there is still a need for tractable stochastic optimization models for large-scale power and water systems that deal with multiple uncertainty sources and nonlinearity approximations.

Large CO2 emissions constitute a significant problem today due to their effect on climate change, and the need to design appropriate energy policies to mitigate their consequences and reduce emissions requires a detailed analysis of one of the main sources of such emissions: the electricity system. Thus, this paper presents a study on the effects of energy policies on decarbonization by comparing the detailed phase-out of coal-fired power plants across a range of cases with the implementation of a carbon tax to meet Nationally Determined Contributions (NDCs). The case study focuses on the Chilean electricity system, using a long-term generation and transmission expansion planning model (GTEP) that incorporates a wide range of generation technologies. The study examines the long-term effects of these policies, including costs, investments, and CO2 emissions, as well as their impact on consumer prices reflected in the marginal costs of the system. The transmission system modeling covers various regions of Chile and significant projections for renewable energy sources. It evaluates three economic scenarios based on generation technology costs, fuel prices, and electricity demand under four different closure schemes and fourteen different carbon tax levels. The results indicate that implementing a carbon tax can be more cost-effective for the system than the implementation of a phase-out schedule for coal plants, taking the form of reduced CO2 emission and overall system costs, with an optimal carbon tax value of 37 USD/tCO2. Additionally, the study reveals significant effects on consumer prices, showing that a carbon tax as an energy policy leads to lower prices compared to a phase-out scheme.

The water pump scheduling problem is an optimisation model that determines which water pumps will be turned on or off at each time period over a given time horizon for a given water supply system. This problem has received considerable attention in mining and desalination due to the high power consumption of water pumps and desalination plants and the complicated dynamics of water flows and the power market. Motivated by this, in this paper we solve the optimal operation of a desalinated water supply system consisting of interconnected tanks and pumps that transport water to high-altitude reservoirs. The optimisation of this process encounters several difficulties arising from (i) the nonlinearities of the equations for the frictional losses along the pipes and pumps, which makes the problem a nonlinear mixed-integer model, and (ii) many possible combinations of pressure head and flow rates, which quickly leads to high computational costs. These limitations prevent the problem from being solved in a reasonable computational time in high-altitude water supply systems with more than six pumps and reservoirs, as in many networks worldwide. Therefore, in this work we develop new exact methods for the optimal pump scheduling problem that use a binary expansion approach to efficiently account for the existing nonlinearities by reducing the computational difficulties of the original problem while keeping an excellent representation of the physical phenomena involved. We also extensively tested the proposed approach in different network topologies and a case study for a real-world copper mine water network, and we conclude that the binary expansion approach significantly reduces the computational time for solving the problem with high precision, which can be very relevant for the practical daily operation of real-world water supply systems.

The evolving landscape of electrical grids has presented formidable challenges to energy systems, which need adaptable solutions for reliability, supply adequacy, and demand balance, where demand flexibility arises as a pivotal operational asset for these needs. In recent decades, desalination technology, particularly reverse osmosis, has been employed to purify seawater and brackish water by removing salt and impurities, effectively addressing water scarcity in some regions of the world. However, its energy-intensive nature, driven by highpressure pump usage, leads to significant operational expenses and places additional strain on the power grid. Our study introduces a novel holistic model for the Chilean electricity system, incorporating reverse osmosis desalination facilities to comprehensively confront water scarcity through strategies that leverage the flexibility of high-pressure pumps. The model considers dispatch coordination, unveiling the synergy's value given mainly by the desalination system and the energy generation infrastructure. The study underscores the influence of desalination on power system dynamics, transmission, and generation expansion throughout the time horizon of 2025 to 2040. The principal findings underscore the advantages derived from co-optimizing desalination and the electrical system, the enhanced system performance from flexible desalination plant operations, and the synergistic potential of solar, battery energy storage, and the desalinated water system. These insights help to inform investment decisions, decarbonization policies, and the water-energy nexus interplay, charting a course towards comprehensive sustainability.

Soiling on solar modules stands as a primary source of energy yield loss, causing reflection of radiation. This paper presents a novel cleaning scheduling model for the maintenance strategy of photovoltaic plants, focused on adequately representing the soiling and cleaning processes. We employ a novel methodology based on cleaning sectors to make the model scalable in real-world applications. Computational experiments in a case study of three operating solar power plants in Chile, on three different scales of energy generation, show that the proposed methodology can achieve better results than traditional maintenance models in the literature, increasing revenue an average of 0.8% with respect to an optimized baseline. Additional experiments also show that the proposed model allows for a more efficient use of cleaning resources and makes possible the coordination of multiple large scale power plants, where conventional strategies result on infeasible policies.

As the world tries to decarbonize, industrial sectors with high-intensity energy needs struggle to find carbon-neutral energy carriers. In this context, electricity-based fuels (e-fuels) emerge as a solution to store and use renewable energy. This paper presents an optimization model that addresses the challenge of designing e-fuel plants, integrating renewable energy variability with specific technical challenges. Our study provides a literature review of existing tools for modeling energy systems and then proposes a mixed-integer linear optimization model for designing chemical plants for e-fuel production. This model aims to minimize the total annual cost of the system considering capital and operational costs, commodity prices, and the sale of products. Extensive computational experiments for a case study based on reference data were conducted. The results show significant improvement over existing tools on both computational and overall economic performance. Several design options are proposed and simulated for an e-fuel plant in south Chile.

As countries continue to update their climate ambitions, they are seeking cost-effective solutions for decarbonizing energy use. In this context, low-carbon hydrogen production and use presents relevant opportunities for emissions reductions and economic development, and recent studies show important potential benefits from integrating electricity and hydrogen networks. Based on a novel mathematical optimization model, we conduct a case study for Chile in 2020–2050 to assess the least-cost evolution of the integrated hydrogen–electricity system, testing different carbon prices, 100% renewable mandates, and incorporating domestic and international hydrogen demand. By optimizing over various scenarios, we find that, due to the country’s significant renewable potential and the flexibility that electrolyzers can provide, adding hydrogen exports to domestic hydrogen use may enhance renewable integration, while not necessarily increasing average wholesale electricity prices for typical end-users (relative to our baseline scenario), but also make battery deployment unattractive. Further, we conclude that climate policies such as a high carbon price or a 100% renewable mandate may be crucial to achieve a fully renewable system by 2050 and reduce cumulative emissions, resulting in 3%–14% higher net present system costs. Finally, we discuss that various concerns such as water and land use must be addressed by policymakers.

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.

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.

A novel solution approach is developed for the scheduling of chemotherapy sessions at cancer treatment centers. The problem is divided into two subproblems determining the day (interday scheduling) and the time slots (intraday scheduling), respectively. The interday subproblem is solved by a model that allows for effective treatment center capacity choices while the intraday subproblem is addressed using two optimization models. New patient arrivals and treatment protocols specifying the latest starting date and session spacing are sources of uncertainty. Unlike other existing approaches, the proposed method incorporates the concept of effective treatment capacity which facilitates the interaction between the interday and intraday subproblems allowing them to be solved sequentially and iteratively to thus achieve much more resource-efficient solutions. A case study using real data from a Chilean cancer center to conduct comparative simulations of its manual scheduling methods and the proposed methodology found that the latter almost always performed better, often significantly so, on makespan, resource utilization, overtime, and patient diversion metrics.

As the green hydrogen industry develops, hybrid renewable power supply configurations emerge as a solution to increasing electrolyzer load factor. In particular, solar photovoltaics (PV) paired with Battery Energy Storage Systems (BESS) will provide a suitable option for continuous power supply as their investment costs continue to decline during the next decade. An opportunity arises to use the plant’s energy resources to provide grid services without hindering hydrogen production. Motivated by this, this paper presents a chance-constrained stochastic model for the optimal operation scheduling of a PV-BESS-Electrolyzer system participating in energy and ancillary services markets. Scenarios are used to account for uncertainty in solar power availability and energy content of the frequency regulation signal, and a degradation model is included to properly consider the effect of repeated battery cycling. Computational experiments using PJM market data and a realistic time frame show the benefits of the proposed model, achieving 10.2% more profits than the deterministic benchmark problem, while maintaining low failure rates in the provision of the regulation service and a similar hydrogen production compared to an off-grid standalone operation.

In support of the Climate Action Teams initiative, we evaluate Chile's potential for greenhouse gas (GHG) mitigation beyond its Nationally Determined Contributions (NDCs) through implementing ambitious actions. An open multisector model is used to project GHG emissions. The results indicate that additional efforts are required to meet Chile's NDC commitments. However, ambitious actions could yield a significant mitigation surplus at a reasonable cost, with a maximum potential of 75 (65–82) MtCO2e beyond the committed carbon budget. About one-third of this potential can be achieved at a mean abatement cost of less than 20 USD/tCO2e, and an additional 65% can be obtained with a mean abatement cost ranging between 20 and 50 USD/tCO2e. The estimated capital cost required for implementing these actions is 5.3 (4.9–5.3) billion USD from 2020 to 2030. In addition to mitigating GHG emissions, these actions also have significant health co-benefits, with an estimated avoidance of up to 2,250 (2,180–2,320) premature PM2.5-induced deaths between 2020 and 2030. The health co-benefit between 2020 and 2030 is estimated to be around 1.5 billion USD. The study also suggests that an early coal-power phase-out is not the most efficient mitigation action in the power sector. We estimate that a carbon tax between 40–45 USD/tCO2e could achieve the same level of emissions reduction as closing coal-fired power by 2025 but at a significantly lower cost.

The current water scarcity faced by many countries increases the need to consider an appropriate representation of future hydro inflows in power system operation and planning models. Hydrothermal scheduling is the problem that seeks to use the water stored in reservoirs throughout time in order to find an optimal dispatch policy between hydro and thermal power plants. Due to both the inherent randomness of water inflows and the intertemporal decision process, this problem has been typically approached through multistage stochastic optimization, minimizing the total expected operational cost over the entire planning horizon. However, this approach has some practical disadvantages. Among the main ones we highlight (i) the complexity of balancing the statistical representativeness of the stochastic processes and the computational efficiency of the optimization model; (ii) the need to employ computationally intensive decomposition methods for its solvability; and (iii) the need to carry out network simplifications to tackle tractability issues arising in large networks. As an alternative, we propose a multistage adaptive robust optimization model for the hydrothermal scheduling problem. Robust optimization is useful to prevent the previous disadvantages because it does not make any distributional assumption and it works with the so-called uncertainty sets instead of carrying out sampling processes. In particular, we propose an efficient formulation based on linear decision rules and vector autoregressive models to represent the uncertainty in hydro inflows. Our experiments, based on the Chilean electric power system with hundreds of hydro nodes and connections, show the proposed model’s efficiency for large-scale systems and provide insights into the adequate balance between cost-effectiveness and reliability that robust optimization models guarantee.

This paper presents a novel methodology based on Principal Components Analysis (PCA) and Affine Policies (AP) for self-scheduling of a price-taker Compressed Air Energy Storage (CAES) facility operating under uncertainties. The proposed PCA-AP model is developed from the facility owner's perspective, which partakes in energy, spinning, and idle reserve markets. A methodology is proposed to select the required price uncertainty intervals from actual data based on a Box Cox technique. For a more realistic representation, the detailed thermodynamic characteristics of the CAES facility are considered, taking into account as well modern CAES facilities that may charge and discharge concurrently. To validate the proposed PCA-AP model and approach, the results obtained are compared with an existing Affine Arithmetic (AA) model, which is also based on an affine approach, and Monte Carlo Simulations (MCS), which can be considered as the benchmark for comparison purposes. The input data, forecast prices and intervals of uncertainty, are taken from the Ontario-Canada electricity market for 2015-2019. From the studies presented, it can be observed that the new PCA-AP approach provides less conservative results as compared to the AA approach, and hence can be considered an adequate methodology for day-ahead operations in systems with significant sources of uncertainty.

This study uses a water-food-energy nexus model, which connects water-based productive activities and allocation policies, to simulate water resources management strategies for adapting the Maule Basin in Chile to climate change impacts. Two strategies are considered: introducing linear hedging rules to a reservoir and establishing adaptative water rights linked to irrigation efficiency of crops in the area. The simulations are run over 14 different climatic scenarios that project different water availability conditions. A multiobjective optimization of environmental, agricultural, and energy outcome indicators is conducted to generate noninferior portfolios that combine the strategies proposed. Simulation results, though limited to a deterministic approach and pessimistic climate scenarios, show that 79 out of the 84 combinations of objective-scenarios increase their performance because of the strategies, with notable increases in agricultural resilience (64%), outflow (92%), and total benefits (26%). We find considerable trade-offs between the food/energy production and water outflow, alongside agricultural vulnerability and resilience.