Improving Sustainable Hydropower Design & Operations
Topic Area 3 Projects
Project Area 3 Lead:
Soroosh Sorooshian, UC Irvine
CV | Website
Given the increasing potentials of hydropower to meet energy demands especially in the modernized grid, and the large number of hydropower dams being built globally, improving the sustainability in hydropower operation is a pressing research issue in water energy nexus. This is not only because non-stationary hydroclimates alter fundamental engineering design and operation of dams, but also because such designs and operations require a deeper understanding of how hydropower impacts ecosystem and economic functioning. More specifically these goals are achieved through: (i) providing hydropower facility operators with short-term operation strategies for optimal ecosystem and water-temperature management; (ii) developing both weather-scale and climate-scale precipitation forecasts to support short-term hydropower scheduling; and (iii) the assessment of hydropower dispatch on the electric grids.
Project 3.1: Optimizing hydropower operations while sustaining temperatures and ecosystem functions
Joshua Viers, UC Merced
Jay Lund, UC DavisContext: Hydropower operations have many effects on downstream ecosystems through changes in flow patters, temperature, sediment, migratory patterns, and flood pan connection and inundation. Thus, modeling and optimization of reservoir and hydropower operations must incorporate criteria and objectives for sustaining native ecosystems.
Objective: In this project, the objectives are: (1) articulate functional flows for hydropower operations that benefit downstream ecosystems; (2) develop thermal regime criteria for selected use cases; (3) use realtime downstream sensors and forecasts for temperature, stream flow, and other conditions to allow greater flexibility and reliability in operations for environmental objectives; (4) analyze fish passage facilities and operations at dams.
Hydropower operations have many effects on downstream ecosystems through changes in flow patterns, temperature, sediment, migratory patterns, and floodplain connection and inundation. Thus, modeling and optimization of reservoir and hydropower operations must incorporate criteria and objectives for sustaining native ecosystems. Such criteria and objectives are urgently needed at the interface of hydropower operations, non-stationary hydroclimates, and environmental policy. This project will incorporate environmental objectives into hydropower operations by formulating and implementing hydropower optimization methods that explicitly include ecosystem objectives. We will: 1) articulate functional flows for hydropower operations that benefit downstream ecosystems; 2) develop thermal regime criteria for selected use cases; 3) incorporate downstream sensors and forecasts for temperature, stream flow, and other conditions to allow greater flexibility and reliability in operations for environmental objectives; and 4) analyze opportunities for fish passage and/or improved operations at dams. This information, along with risk analysis on ecosystem performance, will be used to optimize hydropower operations and fish population performance.
Though some hydropower systems can manage downstream temperatures through infrastructure operations, little is known about how to optimally re-operate these systems to achieve temperature management objectives in existing and new infrastructure. To address this gap, our research will focus on managing hydropower for temperature variability by incorporating ecologically beneficial water temperature objectives for ecosystems. We will develop and demonstrate methods for incorporating ecologically beneficial water temperature objectives as well as for integrating physically-based river and reservoir temperature simulation and hydropower modeling techniques into the operations of multi-reservoir hydropower systems. We will use a combination of physically-based river and reservoir temperature simulations to determine the best hydropower data modeling techniques, as well as to incorporate non-stationary hydroclimates.
Publications
Project 3.2: Assessing climate change impacts on optimal hydropower design and investment strategies
Xiaogang Gao, UC IrvineContext: Hydropower facility design and operation inherently depend on the variability of climates, and the quantity and frequency of streamflows in this project, we will (1) use risk-based design approach and probability distributions to evaluate the impacts of climates over the life of the hydropower facilities, (2) investigate the optimal capital investment, sizing, and locations of hydropower facilities, and (3) develop optimal reservoir operation strategies in a multi-objectives context, considering ecosystem objectives, economics water and energy demands, and water quality, etc.
Objective: Assess the impacts of both long-term climate variation trends and extreme events, i.e., storms, on optimal hydropower facility design, investment strategy, and optimal reservoir operation strategies.
Hydropower systems inherently depend on climate as an energy source, and their ability to regulate supply varies significantly with climate. Environmental system demands on hydropower systems, particularly flow and temperature-related objectives downstream of dams, also vary with climate. We will examine the effects of climate change and projections on capital investment in and the optimal design, sizing, and location of hydroelectric power facilities. We will use an optimized risk-based design approach based on a probability distribution of likely long-term climates (streamflow and temperatures) over the life of the hydropower facility, forecasts of energy demands and economic values, and forecasts of ecosystem water and temperature demands, along with a statewide economic reservoir system model (CALVIN) to simulate the environmental flow scenarios. These resources will be coupled with state-of-art global optimization algorithms to examine the probabilistic performance of suggested designs (simulation mode) and the Pareto-optimized risk-based design for different weighting of economic, air quality, and ecosystem objectives.
Publications
Yang, T., Asanjan, A.A., Faridzad, M., Hayatbini, N., Gao, X. and Sorooshian, S."An Enhanced Artificial Neural Network with A Shuffled Complex Evolutionary Global Optimization with Principal Component Analysis. Information Sciences." Information Sciences, 418, (2017) 302-316.
Project 3.3: Improving hydrologic and energy demand forecasts for hydropower operations with climate change
Kuolin Hsu, UC IrvineContext: Hydropower is increasingly being called upon to mediate electrical grid operations, given the facts that wind and solar electrical supplies are intermittent due to weather conditions, and day-night shift, respectively. A confident short-term precipitation and streamflow forecast allows decision makers and dam operators to produce hydro-electrical power in an efficient manner, which facilitates (1) the power exchanges in the electrical markets, (2) reduce unnecessary consumptions from non-renewable energy sources, and (3) stabilize regional electricity prices.
Objective: Improve the accuracy of an existing near real-time Precipitation Estimate from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) product, and develop a forecast mode of the PERSIANN products with a lead-time of 1-3 days and an extended time for 1-7 days in support of short-term hydropower scheduling in both western U.S. and southern China.
Hydropower is increasingly being called upon to mediate electrical grid operations, a use that is particularly sensitive to energy demand forecasts and hydrologic inflow forecasts, especially where much of the hydropower consists of run-or-river plants. Hydropower scheduling, particularly short-term scheduling, is one of the most crucial issues in reservoir operation and clean energy supply. Reservoir modeling and optimal reservoir operations have been well studied. The next step for delivering confident short-term hydropower scheduling to decision makers is to examine the credibility of forecasts. We will develop grid-wide forecasts of inflows and electricity demands based on ground sensors and remotely sensed data, attending particularly to temperature effects on electricity demands, hydrologic conditions, and grid performance.
Our primary focus will be on improving the accuracy of an existing near real-time Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) product, originally developed at UC Irvine’s Center for Hydrogeology and Remote Sensing. It will be used as the main input to a distributed hydrological model to generate near real-time streamflow information. As the information on streamflow becomes more current and accurate, it will increase confidence in making efficient hydropower scheduling decisions generated by reservoir and hydropower dispatch models. The proposed prediction lead time is 1-3 days with the extended time for 1-7 days, commonly termed “short-term scheduling.”
The hydropower scheduling/prediction will facilitate power exchanges in the electricity markets, reduce unnecessary consumption of other energy sources, and help stabilize electricity prices.
The project team has conducted research on the development of hydrologic models for more than three decades and has extensive experience with the most widely used, global-scale, near-real time satellite-based precipitation products for hydrologic modeling applications.
Publications
Yang, Tiantian, et al. "Multi-criterion model ensemble of CMIP5 surface air temperature over China." Theoretical and Applied Climatology (2017): 1-16.
Yang, T., Asanjan, A. A., Welles, E., Gao, X., Sorooshian, S., & Liu, X. "Developing reservoir monthly inflow forecasts using artificial intelligence and climate phenomenon information." Water Resources Research, (2017) 53(4), 2786-2812.
Nasrollahi, N., AghaKouchak, A., Cheng, L., Damberg, L., Phillips, T. J., Miao, C., ... & Sorooshian, S. "How well do CMIP5 climate simulations replicate historical trends and patterns of meteorological droughts?." Water Resources Research, (2015) 51(4), 2847-2864.
Project 3.4: Electric grid reliability and greenhouse gas implications of climate change-impacted hydropower resources
Scott Samuelsen, UC Irvine
CV | Website
Context: Hydropower fulfills two key services for the electric grid: 1) the provision of low emissions, fossil fuel-free electricity generation and 2) flexible, dispatchable electricity generation that can manage electric load variability and ensure the robustness of electric service. These characteristics become more important in the context of helping to fully utilize renewable resources by maintaining grid reliability in a system with variable generation. However, changes in regional hydrology due to climate change can impact the ability of hydropower to provide these services.
Objective: Assess how the hydrologic impacts of climate change in different regions impact the ability of hyrdropower resources to provide low-carbon electricity generation and anciliary services to manage grid variability in the context of increasing renewable resource integration.
Hydropower has historically provided carbon-free and pollutant-free load-following electric generation. During drought conditions, which are expected to increase under climate change, the lack of hydropower generation is typically replaced with conventional generation, which, in California, largely uses natural gas. Hydropower has also provided ancillary services to support and maintain the reliability of the electric grid during normal operation and contingency events. In California, large hydropower provided up to 80% of the annual spinning reserve capacity and 30-45% of the annual regulation up and regulation down capacity in non-drought years, according to Federal Energy Regulatory Commission data.
The ability of hydropower to fulfill these roles has depended on consistent historical climate patterns governing precipitation and runoff; these, too, will be altered under climate change. Climate change will alter the total amount of precipitation and runoff in a region, affecting total hydropower generation and reservoir fill patterns, which will in turn affect the ability to provide spinning reserve and frequency regulation services for the grid. In many regions, this can jeopardize local grid reliability and increase emissions, given that generation types that provide ancillary services emit greenhouse gases.
The objectives of this research are to determine: (1) the impact of climate change on electric grid reliability through its impact on hydropower resources in key regions of interest; (2) the emissions implications of replacing the role of hydropower on the grid in regions where it is necessary; and (3) potential alternatives to conventional generation in replacing hydropower generation. Climate change-impacted hydrological patterns generated by the research team will be used as input to our integrated model of climate change-impacted hydrological patterns and electric grid resource dispatch (including hydropower), the Holistic Grid Resource Integration and Deployment (HiGRID). This model will capture the impacts of climate change-impacted hydrological patterns on the potential generation and ancillary service provision for the future electricity system. Preliminary work using HiGRID has been able to project the limits of hydropower dispatch and their impacts on electric grid greenhouse gas emissions for the California electricity system with a high renewable penetration level in 2050. Our research team has also studied the interactions between climate, water resources, and energy systems emissions and will leverage this work to support the proposed project. Hydropower has historically provided carbon-free and pollutant-free load-following electric generation. During drought conditions, which are expected to increase under climate change, the lack of hydropower generation is typically replaced with conventional generation, which, in California, largely uses natural gas. Hydropower has also provided ancillary services to support and maintain the reliability of the electric grid during normal operation and contingency events. In California, large hydropower provided up to 80% of the annual spinning reserve capacity and 30-45% of the annual regulation up and regulation down capacity in non-drought years, according to Federal Energy Regulatory Commission data.
The ability of hydropower to fulfill these roles has depended on consistent historical climate patterns governing precipitation and runoff; these, too, will be altered under climate change. Climate change will alter the total amount of precipitation and runoff in a region, affecting total hydropower generation and reservoir fill patterns, which will in turn affect the ability to provide spinning reserve and frequency regulation services for the grid. In many regions, this can jeopardize local grid reliability and increase emissions, given that generation types that provide ancillary services emit greenhouse gases.
The objectives of this research are to determine: (1) the impact of climate change on electric grid reliability through its impact on hydropower resources in key regions of interest; (2) the emissions implications of replacing the role of hydropower on the grid in regions where it is necessary; and (3) potential alternatives to conventional generation in replacing hydropower generation. Climate change-impacted hydrological patterns generated by the research team will be used as input to our integrated model of climate change-impacted hydrological patterns and electric grid resource dispatch (including hydropower), the Holistic Grid Resource Integration and Deployment (HiGRID). This model will capture the impacts of climate change-impacted hydrological patterns on the potential generation and ancillary service provision for the future electricity system. Preliminary work using HiGRID has been able to project the limits of hydropower dispatch and their impacts on electric grid greenhouse gas emissions for the California electricity system with a high renewable penetration level in 2050. Our research team has also studied the interactions between climate, water resources, and energy systems emissions and will leverage this work to support the proposed project.
Publications
Project 3.5: Comparison of reservoir management and hydropower operation standards between the U.S. and China
Tiantian Yang, University of Oklahoma
CV | Website
Context: The scope of work of this project focuses on the comparison of reservoir management and hydropower operation standards between the U.S. and China. .
Objective:The research objectives of this project are to (1) investigate the regulation and standards of hydropower generation and reservoir operation; (2) conduct multi-dimensional analysis and case studies; (3) review the major challenges in the water-energy system operation; (4) implement advanced machine learning and statistical approaches into sustainable reservoir modeling and decision making, and (5) identify new future researches in support of flexible and sustainable reservoir system operation.
Hydropower generation is one of the fundamental functions of reservoirs and dams. The sustainability of hydropower generation is subject to hydrological conditions of the river systems, human-defined water and energy regulations, as well as the operating rules of the reservoirs in compliance with other functionalities, such as flood control, water quality management, water supplies, recreation, navigation, and ecosystem functioning. As more frequent and severe weather, and climatic conditions influence water viabilities world wide, the designed reservoir management and hydropower operation standards need continuous improvement to enable decision-makers and operators to better prepare for and respond to possible environmental changes and climatic variabilities.
Based on the successful collaborations of other projects under topic area 3, both U.S. and China team will jointly (1) investigate the historical hydrological trends over key river basins and identify emerging challenges for reservoir management and hydropower operation, (2) test out advanced machine learning and statistical approaches in support of effective and sustainable reservoir operation, (3) identify new research topics to improve the reliability, and flexibility of reservoir operation for a sustainable future.
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