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Results for “cost of saved electricity”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

The Cost of Saving Electricity: A Multi-Program Cost Curve for Programs Funded by U.S. Utility Customers

This study analyzed the cost performance of electricity efficiency programs implemented by 116 investor-owned utilities between 2009 and 2015 in 41 states, representing about three-quarters of the total spending on U.S. efficiency programs. We applied our typology to characterize efficiency programs along several dimensions (market sector, technology, delivery approach, and intervention strategy) and report the costs incurred by utilities and other program administrators to achieve electricity savings as a result of the programs. Such cost performance data can be used to compare relative costs of different types of efficiency programs, evaluate efficiency options alongside other electricity resources, benchmark local efficiency programs against regional and national cost estimates, and assess the costs of meeting state efficiency policies. The savings-weighted average cost of saved electricity for the period was $0.025/kilowatt-hour (kWh). The cost of saved electricity for programs that targeted residential customers was $0.021/kWh, compared to $0.025/kWh for programs for commercial and industrial customers. Ultimately, we developed an aggregate program savings “cost curve” for the actual electricity efficiency resource during the period that provides insights into the relative costs of various types of efficiency programs and the savings contribution of each program type to the efficiency resource at a national level.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Estimating the Drivers of the Cost of Saved Electricity in Utility Customer-Funded Energy Efficiency Programs

Energy efficiency programs funded by utility customers provide an electricity resource in most U.S. states, but their scale and cost of saving electricity varies significantly by state. In this paper, we explore the drivers of the cost of saved electricity in these programs with an econometric model and nearly a decade of data reported by efficiency program administrators. We found strong evidence for economies of scale and weak evidence for diseconomies of scale, which suggests that states with low levels of efficiency savings relative to retail sales can increase the size of their efficiency programs without large increases to the cost of saved electricity. We discuss examples of energy efficiency forecasting and potential modeling in light our econometric analysis and identify methodological improvements relevant to utilities and grid operators. This paper provides insights into the economics of customer-funded efficiency programs that will support regulators, utilities, and policymakers to utilize energy efficiency as a resource.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Reinforcement-Learning-Based Smart Water Heater Control: An Actual Deployment

Utilizing smart control algorithms for electric water heaters (EWHs) is essential for fully harnessing the demand response (DR) potential of EWHs. For this reason, the use of reinforcement learning (RL) algorithms for EWHs has received increasing attention in recent years. However, existing RL approaches are either simulation-based or use pretrained RL agents. To this end, this paper presents the real-world deployment of a set of model-free RL approaches that aim to minimize the electricity cost of a EWH under a time-of-use electricity pricing policy using standard DR commands (e.g., shed, load up). The experiment results showed that the RL agents can help save electricity cost in the range of 11% to 14% compared to the baseline operation. This study demonstrated that RL-based EWH controllers can be deployed in real world without any prior training and can still save electricity cost.

deep learning↗
DOE OSTI2022

Savings in Action: Lessons from Observed and Modeled Residential Solar Plus Storage Systems

The electric grid is rapidly evolving as small-scale, demand-side resources play increasingly important roles in grid operations and decarbonization. Maximizing the potential of demand-side resources involves incentivizing electricity customers to use those resources in ways that benefit the broader electrical grid. These incentives depend largely on the electricity cost savings that customers can realize from demand-side resource adoption. Determining these potential cost savings is a complex task. Cost savings depend on numerous factors, including the characteristics of different technologies, the algorithms that control these devices, system performance, customer behavior, electricity rate structures, and climatic factors. Another challenge is that estimated cost savings are frequently based on modeled rather than observed system performance, particularly in the academic literature. In this study, we begin to fill the gap in empirical research of demand-side resources using data from a new construction residential community equipped with rooftop solar and storage (S+S) in Arizona. We use these data to analyze the factors that determine customer electricity cost savings and emissions impacts of S+S in the real world. We then compare these data to modeled system performance to understand how models deviate from real-world outcomes. Based on these findings, we explore ways to improve such models and, conversely, use modeled results to suggest improvements to actual S+S deployment. The results of these analyses can be summarized in four key findings: 1) rate structures play a central role in the grid and customer value of demand-side resources; 2) certain customers can benefit more from demand-side resource adoption than others; 3) modeled battery dispatch and sizing reveals opportunities for additional cost savings; and 4) optimal dispatches can reduce grid emissions while maximizing bill savings.

14 SOLAR ENERGY↗
DOE OSTI2022

Savings in Action: Lessons from Observed and Modeled Residential Solar Plus Storage Systems [Slides]

The study performed two related analyses using data from a new-construction residential community equipped with rooftop solar and storage (S+S) in Arizona. The study analyzed the factors that determine customer electricity cost savings from S+S adoption. The research compared the Arizona case study data to modeled system performance to understand how models deviate from real-world outcomes. Based on these findings, NREL explored ways to improve such models and, conversely, use modeled results to suggest improvements to S+S dispatches.

14 SOLAR ENERGY↗
DOE OSTI2021

Deep Reinforcement Learning for Autonomous Water Heater Control

Electric water heaters represent 14% of the electricity consumption in residential buildings. An average household in the United States (U.S.) spends about USD 400–600 (0.45 ¢/L–0.68 ¢/L) on water heating every year. In this context, water heaters are often considered as a valuable asset for Demand Response (DR) and building energy management system (BEMS) applications. To this end, this study proposes a model-free deep reinforcement learning (RL) approach that aims to minimize the electricity cost of a water heater under a time-of-use (TOU) electricity pricing policy by only using standard DR commands. In this approach, a set of RL agents, with different look ahead periods, were trained using the deep Q-networks (DQN) algorithm and their performance was tested on an unseen pair of price and hot water usage profiles. The testing results showed that the RL agents can help save electricity cost in the range of 19% to 35% compared to the baseline operation without causing any discomfort to end users. Additionally, the RL agents outperformed rule-based and model predictive control (MPC)-based controllers and achieved comparable performance to optimization-based control.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Model predictive control for demand flexibility: Real-world operation of a commercial building with photovoltaic and battery systems

Hundreds of studies have investigated Model Predictive Control (MPC) for the optimal operation of building energy systems in the past two decades. However, MPC field tests are still uncommon, especially for small- and medium-sized commercial buildings and for buildings integrated with onsite renewables. This paper describes the implementation and the long-term performance evaluation of an MPC controller in a small commercial building equipped with behind-the-meter photovoltaics and electrochemical batteries. MPC controls space conditioning, commercial refrigeration, and the battery system. We tested two types of demand flexibility applications in the field: electricity bill minimization under time-of-use tariffs and responses to grid flexibility events. Results show that the proposed controller achieves 12% of annual electricity cost savings and 34% peak demand reduction against the baseline, while respecting thermal comfort and food safety. The field tests also demonstrate the ability of the MPC controller to provide a multitude of grid services including real-time pricing, demand limiting, load shedding, load shifting, and load tracking, using the same optimization framework.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Online transfer learning strategy for enhancing the scalability and deployment of deep reinforcement learning control in smart buildings

In recent years, advanced control strategies based on Deep Reinforcement Learning (DRL) proved to be effective in optimizing the management of integrated energy systems in buildings, reducing energy costs and improving indoor comfort conditions when compared to traditional reactive controllers. However, the scalability and implementation of DRL controllers are still limited since they require a considerable amount of time before converging to a near-optimal solution. This issue is currently addressed in literature through the offline pre-training of the DRL agent. However this solution results in two main critical issues: (1) the need to develop a building surrogate model to perform the training task, and (2) the need to perform a fine-tuning process over several training episodes to obtain a near-optimal control policy. In this context, this paper introduces an Online Transfer Learning (OTL) strategy that exploits two knowledge-sharing techniques, weight-initialization and imitation learning, to transfer a DRL control policy from a source office building to various target buildings in a simulation environment coupling EnergyPlus and Python. A DRL controller based on discrete Soft Actor–Critic (SAC) is trained on the source building to manage the operation of a cooling system consisting of a chiller and a thermal storage. Several target buildings are defined to benchmark the performance of the OTL strategy with that of a Rule-Based Controller (RBC) and two DRL-based control strategies, deployed in offline and online fashion. The strategy adopted for OTL emulates the real world implementation with a simulation process by implementing the transferred DRL agent for a single episode in the target buildings. Target buildings have the same geometrical features and are served by the same energy system as the source building, but differ in terms of weather conditions, electricity price schedules, occupancy patterns, and building envelope efficiency levels. The results show that the OTL strategy can reduce the cumulated sum of temperature violations on average by 50% and 80% respectively when compared to RBC and online DRL while enhancing the energy system operation with electricity cost savings ranging between 20% and 40%. Furthermore, the OTL agent performs slightly worse than the offline DRL controller but it does not require any modeling effort and can be implemented directly on target buildings emulating a real-world implementation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Model-based predictive control of multi-stage air-source heat pumps integrated with phase change material-embedded ceilings

This paper presents a model-based predictive control strategy to optimize the operations of phase change material (PCM) ceiling panels coupled with a multi-stage air-source heat pump. A three-stage prototype heat pump unit has been built and tested in the laboratory, with the low and medium stages designed for space heating/cooling and the high compression stage dedicated to charging of the PCM energy storage. To facilitate optimal control of the integrated heat pump system, a mixed-integer linear programming formulation is derived through linearization of the heat pump model and a mixed-integer reformulation of the PCM dynamic governing equations. A predictive control strategy is synthesized based on the resultant control formulation and implemented in a receding horizon scheme that optimizes the PCM charging and the zone temperature schedules simultaneously to leverage both the passive (associated with building construction materials) and active (PCM) storage capacities of a building. The control strategy has been tested along with three benchmarking control scenarios using a co-simulation platform for a prototypical detached house in Atlanta, GA. In this study, test results showed that application of the proposed control strategy to the PCM-integrated heat pump could provide 27.1% electricity cost savings while a fine tuned rule-based control strategy could achieve cost savings of 20.4%, compared to a baseline case without PCM storage, under a time-of-use rate tariff.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Multifold Insights for Power System Dynamics From Data Assimilation: Meeting Current Challenges

The world’s electric power grids have evolved over the last 120 years from a single power line to today’s large networks. The evolution will continue at an accelerated rate with extensive smart grid development worldwide. Here for example, the U.S. government has set a goal of reaching 100 percent carbon pollution-free electricity by 2035, while the Department of Energy (DOE) shared a goal to deploy 30 GW and 110 GW of offshore wind by 2030 and 2050, respectively. To meet such ambitious goals, in the years to come, a significant percentage of electricity will come from intermittent renewable sources, electric vehicles (EVs), and be supplied to a vast number of loads that will actively respond to grid conditions and incentive signals. This development is largely driven by environmental and economic factors, such as reducing carbon emissions and saving electricity cost for consumers. These energy resources lead to new uncertain behaviors and dynamics which the grid has never seen and were not considered in its design. Operating such a dynamic grid with sufficient reliability and efficiency is a monumental challenge.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Bishop Paiute Single Family Solar Home Project: Phase 4 (Final Technical Report)

The Bishop Paiute Tribe is committed to energy efficiency, renewable energy, and environmental protection and has a vision to install solar energy systems on all buildings on the Reservation where technically feasible. Through completion of the Bishop Paiute Tribe Residential Solar Program – Phase 4, Bishop Paiute Tribe continued its successful model partnering with non-profit solar installer GRID Alternatives to advance its vision and build energy self-sufficiency on the Bishop Paiute Reservation. The Bishop Paiute Tribe Residential Solar Program – Phase IV deployed 67 kW-AC of new clean, renewable energy through grid-tied, net-metered rooftop solar electric systems installed on a total of 20 (one more than the planned 19) existing owner-occupied, single-family homes for low-income families on the Reservation. This represents nearly 14% of the Reservation’s total “unsolarized” homes. These systems were provided at no cost to the homeowners, who will also receive energy efficiency education to further reduce energy consumption and lower electric bills. GRID led a solar installation for three (3) of the homes where Tribal/community members, were given an opportunity in participating in on-the-roof trainings to gain new skills while installing solar for their low-income community members. Subcontractors through GRID Sub Contractor Partnership Program (GRID SPP) led solar installations for seventeen (17) of the homes. Each installation provided paid employment for a solar trainee trainees hired by the SPP subcontractors as entry level solar installers. It is estimated that the installed 67 kW-AC of solar will produce at least 120,000 kWh/year, displacing at least 30-75% of the 20 homes’ total electricity use for a combined system lifetime electricity cost savings for the low-income homeowners of about $\$$500,000. It is further estimated that the installed renewable energy systems installed will prevent greenhouse gases by about 1,000 tons, equivalent to planting approximately 25,000 trees. Following the successful completed installations, it is expected that the Reservation and surrounding communities will benefit from improved air quality via reduced wood stove use for heating. Cumulatively with other initiatives, the project will result in nearly 40% of the Reservation’s solarizable homes being solarized. Overall, the triple impact of the phase 4 of the Bishop Paiute Tribe Residential Solar Program – Phase 4 is: 1) affordable energy for low-income families; 2) on-site clean energy production, 3) hands on training and paid solar installation work for local tribal/community workers. These impacts will support and grow the Tribe’s energy, economic, environmental, and social self-sufficiency and sovereignty amongst the neediest on the Reservation.

14 SOLAR ENERGY↗
DOE OSTI2023

Demonstrating Load-Shaping Capabilities of Cost Minimizing Heat Pump Water Heater Controls with Varying Price Profiles

Increased penetration of photovoltaics and electrification of traditionally gas appliances are exacerbating existing challenges in cost-effectively balancing electricity grid supply and demand. Decarbonization without incurring expensive transmission and distribution system capacity increases requires shifting building loads from peak demand times to peak renewable production times. Grid operators are evaluating new ways of encouraging load shifting, including using time-varying price structures to provide a financial incentive. If devices incorporate price-responsive controls, a price profile could be designed to yield a wide variety of load curves as needed to optimize grid functionality. Heat pump water heaters (HPWHs) are an ideal device for price-responsive controls because the storage tank enables them to optimize the timing of electricity consumption without impacting hot water delivery service. This paper presents work demonstrating how price-responsive controls for HPWHs can provide different load profiles, as needed to stabilize the grid, in response to different price profiles. HPWH manufacturers now include web API and CTA-2045 communication capabilities which enable sending load shaping control signals. Pilot studies and preliminary programs have utilized these capabilities with uniform control strategies to reduce 4-9 PM electricity consumption. However, no studies have developed flexible controls capable of both a) responding to constantly varying price profiles and b) customizing logic to match the needs of each HPWH. Berkeley Lab's CalFlexHub project is pioneering price-driven load flexibility by developing and deploying cost-minimizing controls utilizing setpoint setting signals for fleets of HPWHs in response to varying price profiles. Control development is based on simulations using the Flexible Heat Pump Water Heater Performance Predictor which captures the control decisions of a residential, integrated HPWH manufacturer’s on-board controller. The proposed cost-reducing controls respond to constantly changing price profiles, providing the ability to change the price profile to generate load curves as needed to maintain grid stability. Preliminary simulations studying the load shaping capabilities of price-responsive controls on a fleet of 60 HPWHs have demonstrated an average of a) 135.4% increases in load during low-price periods, b) >36.8% reductions in electricity peak-price period, and c) 6.2% electricity cost savings.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Optimizing Price-Informed Operation of a Battery Storage System in an Office Building

New prescriptive efficiency requirements amended to US model energy codes historically have been evaluated using an average, blended electricity rate, which obscures demand charges. Post 2019, new measures can be evaluated using a representative time-of-use (TOU) tariff yet more sophisticated analysis methods are needed to consider a variety of TOU tariffs and assess price-informed control. To address these needs, this study couples building prototype simulation model with varying-in-sophistication battery storage operating strategies for different electricity TOU tariffs. The analysis compares the impact of operating a battery storage system following simpler rule-of-thumb methods versus a semi-optimized priced-informed heuristic approach. The investigation demonstrates that the heuristic approach results in greater electricity cost savings and is practical to implement.

Lei, Xuechen↗
NASA NTRS1977

Potential benefits of a ceramic thermal barrier coating on large power generation gas turbine

Thermal barrier coating design option offers benefit in terms of reduced electricity costs when used in utility gas turbines. Options considered include: increased firing temperature, increased component life, reduced cooling air requirements, and increased corrosion resistance (resulting in increased tolerance for dirty fuels). Performance and cost data were obtained. Simple, recuperated and combined cycle applications were considered, and distillate and residual fuels were assumed. The results indicate that thermal barrier coatings could produce large electricity cost savings if these coatings permit turbine operation with residual fuels at distillate-rated firing temperatures. The results also show that increased turbine inlet temperature can result in substantial savings in fuel and capital costs.

Clark, J. S.↗
DOE OSTI2022

Port of New York and New Jersey Drayage Electrification Analysis

The National Renewable Energy Laboratory (NREL) evaluated the potential for drayage electrification in the Port of New York and New Jersey (PoNYNJ), with a focus on operators: Harbor Freight Transport (HF), Safeway Trucking (SWT), and International Motor Freight Inc (IMF). This report summarizes the data collection and electrification evaluation of all three drayage operators, includes detailed operational data, and identifies the performance requirements for battery electric tractors (BETs) and corresponding infrastructure operated within the context of PoNYNJ drayage operation. This report also details a methodology to evaluate opportunities, strategies, and challenges associated with future expansions of BETs in meeting PANYNJ emissions goals. The Port Authority has established a goal of achieving Net Zero carbon emissions by 2050 across all facilities, including from tenant and stakeholder sources such as drayage trucks. NREL used real-world performance data collected on the three PoNYNJ drayage operations, along with modeling and analysis tools to compare BET to diesel trucks. From March to July 2021, NREL collected 1Hz vehicle and engine data from 46 drayage trucks at the three operators totaling nearly 121,000 miles of operation, providing enough information to assess vehicle operations for electrification potential. A Future Automotive Systems Technology Simulator (FASTSim) electric truck powertrain model was validated using PoNYNJ data and scenarios were run to evaluate drayage truck electrification requirements over the real-world cycles. The first scenario examined BET viability with minimal changes to existing operations. This assumes the trucks charge when stopped for two hours or longer, have a functional battery size of 375 kWh, and can charge at 270 kilowatts (kW) average which are the specification of the commercially available Freightliner eCascadia. The second scenario looked at what operational, charging infrastructure, and BET technology changes would be needed to fully electrify. Finally, detailed analysis was run on charging rate structure to understand operational costs to the fleets. The studied drayage trucks averaged 5.1 MPG, spent roughly 9% of their energy at idle, and drove an average of 140 miles per day with a maximum daily distance of 573 miles. The FASTSim model results indicate a comparable BET would use 417 kWh of energy per day on average accounting for cargo weight, which is close to the full usable capacity of the eCascadia currently available on the market. Based on the daily average operating data, partial fleet electrification is possible with current technology. However, some specific days of operation would require over 1,600 kWh of energy due to longer distances traveled by the trucks and more intense operation. Trucks used for long distance and intense operation cannot be readily electrified with current technology without operational changes. Full adoption of BETs could reduce CO 2 emissions from these fleets by roughly 75% today, eliminating 76 metric tons of CO 2 (MTCO 2 ) per vehicle each year, which equates to 24,100 MTCO 2 per year for all three operators. Commercially available direct current fast chargers (DCFC) have charge rates up to 350 kW. Based on the average daily modeled energy use for each operator, current industrial rate structures, and the assumption of 350 kW peak charging, full drayage electrification would increase electricity consumption. In addition, peak demand usage would increase with unmanaged charging along with cost of electricity having a direct impact on cost per mile for electric vehicles. The resulting cost per mile for BETs along with comparable cost per mile for conventional diesel trucks are also examined at $\$$4.00 per gallon of diesel. It will be important for PANYNJ and the drayage operators within the PoNYNJ to consider these load impacts to their existing electrical infrastructure and devise operational strategies that avoid coincident charging of vehicles to mitigate demand charges. Despite these electricity cost increases, savings from reductions in diesel consumption will help offset the costs of this increased electricity consumption. However, prices of both electricity and diesel are subject to change based on various factors meaning the realized savings will vary over time. This shows BETs could be cost-competitive on an energy cost per mile basis for all scenarios while diesel is above $\$$3.00/gal. Further, if diesel prices dropped to the 15-year low of $2.33/gal, it would still be cost competitive to operate the EVs with electricity costs of 16.3 ¢/kWh or less.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Economic, Exergy, and Environmental Analyses of the Energy Assessments for U.S. Industries

Abstract This paper highlights the expected versus actual outcomes of 152 energy assessments that were performed between 2011 and 2020. The 1317 energy-assessment recommendations (ARs) are grouped into eight categories. This study adopted four measures per each category of recommendations: annual electricity savings, annual gas savings, annual cost savings, and annual CO2 emission reduction. The first part of the analysis compares the expected to the actually implemented values of the measures applied to each recommendation’s category. It was found that the percentages of the actual to the expected electricity, gas, and cost savings are 26.6%, 11.4%, and 17.1%, respectively, while the percentage of the actual to the expected CO2 reduction is 22%. Moreover, the second part of the analysis presents each category's implementation rate and the reasons for rejecting the unimplemented ARs. Cash flow and expensive initial investment resulted in rejecting 25% of ARs. Furthermore, the study proposes techniques and strategies to increase ARs’ implementation rate and improve private energy services companies’ implementation rate. Finally, exergy analysis is added to show the improvement that energy assessment achieves regarding exergy and exergy efficiencies of different industrial applications.

Energy & Fuels↗
DOE OSTI2022

Irrigation Modernization Task 4: Accelerating Energy Solutions (FY2022 Final Report)

This report offers insights on energy solutions for irrigation modernization that serve the needs of farmers as well as residents and industry in the local community. Solutions are found in the irrigation districts where local generation from renewable energy sources – solar, hydro, and wind – can be combined with energy storage and customer loads. These combined resources configured in microgrids can lower costs during peak loads and provide resiliency by maintaining electricity supply during outages. The goal of this project, as stated in the FY2022 AOP, is to promote realization of energy solutions that are tailored to physical location, community, infrastructure, and energy value streams. Further, it is to develop examples of how to increase value of, and overcome barriers to, energy solutions in the context of irrigation modernization. In pursuit of that goal, the project looked at options for: 1) Reducing the cost of energy consumed in irrigation systems, 2) Increasing the revenue from surplus power generation, and 3) Deploying new power generation configured as part of a local microgrid. Potential solutions to reducing energy costs and increasing surplus power revenue revolve around addressing regulatory and legal constraints tied to how power is purchased by and sold to the irrigator or irrigation district. Some headway was made in identifying barriers and ways to push utilities to be more accommodating to distributed energy sources; however, for the most part, real progress hinges on changes at the regulatory and legislative level. Deployment of local, renewable generation systems can be implemented provided the economics of the project and the location are favorable. In concert with Famers Conservation Alliance and Energy Trust of Oregon, a number of approaches were studied in the past year, including off-grid solar powered pumps, grid-tied community solar projects, and in-conduit canal hydropower systems. Ultimately three viable projects were identified: 1) North Unit Irrigation District/City of Redmond, Oregon Critical Facility Microgrid – offers combined in-conduit hydro and solar power generation. 2) Wallowa County/Joseph, Oregon Irrigation System Upgrades – centered on upgrades to a non-powered dam that will add a turbine as well as in-conduit power in canal feeders downstream. 3) Medford, Oregon Wastewater Treatment Plant Biogas Cogeneration System – centered on building out biogas storage and grid upgrades to power the plant, sell excess power, and provide emergency backup power (supplanting a diesel generator). Each of these projects has characteristics that broaden the understanding of the value of microgrids employing renewable energy to achieve resiliency and net-zero carbon goals. The first two projects are centered on new hydropower systems. Although the Medford project is only tangentially tied to an irrigation district, it was selected for study analysis as it was the only one mature enough (with sufficient data) to complete an analysis within this project year. Thus, we chose to move forward developing a case study, in concert with the Community Water-Power Resilience project, to demonstrate a method for evaluating such projects. Essentially, this case study serves as a template for studies to be carried out next year that more directly involve irrigation system hydropower, e.g., the project at the Wallowa County/Joseph, Oregon Irrigation System. Lastly, it is recognized that the locations and case studies in this report are all located in Oregon. We recognize this as a limitation. While the intent is not to ignore other states or regions, this result is driven by the fact that we have cultivated a collaboration with non-profit entities in Oregon that focus on these topics – Farmers Conservation Alliance and Energy Trust of Oregon. These partners were central to identifying projects that may be good fits for this program. A goal in the coming year is to establish collaboration with entities in other states/regions that, similarly, can connect us to potential projects in their geographic area. The potential benefits from the WPTO’s support for demonstration projects as energy solutions in irrigation districts include: (1) Alternative power supplies for communities and farms using renewable, carbon-free energy resources, (2) Cost savings for electricity for communities and farms, (3) Resiliency of power supplies for critical loads when electricity from the grid is not available, (4) Resiliency of power supplies for critical infrastructure in the event of catastrophic events, and (5) Demonstration of irrigation modernization projects that provide resilience and a reduced carbon footprint to irrigation districts and nearby communities. These deployments can serve as vanguards/archetypes spurring similar projects in other districts and states.

13 HYDRO ENERGY↗