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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Dynamic Thermal Performance Analysis of PCM Products Used for Energy Efficiency and Internal Climate Control in Buildings

PCMs are attractive for the future generation of buildings, where energy efficiency targets and thermal comfort expectations are increasingly prioritized. Experimental analysis of local thermal processes in these dynamic components and whole-building energy consumption predictions are essential for the proper implementation of PCMs in buildings. This paper discusses the experimental analysis of the thermophysical characteristics of both a latent heat storage material (PCM) and a product containing this PCM. The prototype product under investigation is a panelized PCM technology containing inorganic, salt-hydrate-based PCM. The thermal analysis includes studies of melting and freezing temperatures, enthalpy changes during phase change processes, nucleation intensity, sub-cooling effects, and PCM stability. The PCM’s stability is also investigated, as is the ability of PCM products to control local temperatures and peak load transmission times. Two inorganic PCM formulations based on calcium chloride hexahydrate (CaCl 2 .6H 2 O) were prepared and tested in laboratory conditions. Material-scale testing results were compared with outcomes from the system-scale analysis, using both laboratory test methods as well as field exposure in test huts. This work demonstrates that PCM technologies used in buildings can effectively control both the magnitude of thermal storage capacity as well as the time of the peak thermal load. It was found that commonly used material-scale testing methods may not always be beneficial in assessing the dynamic thermal performance characteristics of building technologies containing PCMs.

42 ENGINEERING↗
DOE OSTI2021

Optimal Demand Response Incorporating Distribution LMP with PV Generation Uncertainty

The utilization of aggregated demand-side flexibility via demand response (DR) has become a promising pathway for the integration of renewable energy resources in power systems. Nowadays, there are several management strategies for DR such as the price-based transactive control strategies. However, many of such existing price-based control strategies neglect the physics and operational constraints of the underlying distribution networks when computing the price, raising concerns regarding their theoretical and practical values. This paper studies this issue and investigates optimal DR (ODR) by incorporating the distribution locational marginal price (DLMP). In particular, we discuss DR in connection with DLMPs and propose a multiperiod bilevel optimization problem to find the ODR strategy. Here, the objective is to minimize the peak load, load fluctuation, and payments of load aggregators. In addition, a robust bilevel ODR model is formulated to provide a robust ODR strategy while minimizing operating costs under the worst-case realization of uncertainties; this mitigates the impact of forecasting errors on renewable energy resources. Then, we propose an efficient solution approach by employing the Karush-Kuhn-Tucker conditions and strong duality. Simulation results are presented to illustrate the mutual impacts of the interaction between DR and DLMP and the benefits of the robust ODR strategy.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Real-Time Implementation of Smart Wireless Charging of On-Demand Shuttle Service for Demand Charge Mitigation

This paper presents a smart charge management strategy for an on-demand electric shuttle operating at the National Renewable Energy Laboratory (NREL) campus and supported by an inductive charger at the vehicle's waiting spot. A new control algorithm has been proposed for mitigating the demand charges incurred from the wireless charger. It monitors the shuttle, wireless charger, renewable energy generation, and other loads and regulates charging behavior for demand charge mitigation. Within the control algorithm, an energy prediction is made to estimate the mobility needs of the vehicle and maintain uninterrupted service during operation while still minimizing peak demand. The proposed controller is designed and optimized using a Simulink model for the entire system. It is then implemented and tested in real time at the NREL campus using online cloud services. Two vehicle-use cases' charge-sustaining and charge-depletion operation' are tested under different campus power profiles and drive cycles to assess the controller's performance. In this work, the proposed controller showed a robust performance under different driving scenarios, with high correlation between simulation and experimental data. The results show that proper demand response can be achieved, with an average of 94% reduction of charging loads during peak demand events.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Quantifying the Financial Impacts of Electric Vehicles on Utility Ratepayers and Shareholders [Slides]

Widespread electric vehicle (EV) adoption is critical for meeting economy-wide decarbonization goals and, as a result, states are considering enabling policies and rate designs to accelerate EV deployment. EVs can provide possible financial upside to electric utilities and ratepayers in several ways. For example, from the utility perspective, EVs could drive increased electricity sales and new earnings opportunities through increased capital investments. From the ratepayer perspective, increased electric loads from EVs could reduce average all-in retail rates. The degree to which there are net benefits or costs to shareholders and/or ratepayers depends on how EVs are integrated and managed through enabling grid investments and charging strategies. Using Berkeley Lab’s Financial Impacts of Distributed Energy Resources (FINDER) model that mimics the electric utility investment planning and ratemaking processes, we estimate the utility earnings and customer rate impacts of EVs using a bookend approach of “managed” (i.e., best case) and “mismanaged” (i.e., worst case) charging strategies for a generic summer-peaking, investor-owned, and vertically integrated utility. The analysis also examines the sensitivity of results to different assumptions of EV deployment characteristics, EV impacts on retail electricity sales, incremental distribution system costs, EV charging location, and utility EV enablement costs (i.e., utility costs to invest in EV charging, controls, and communication to deliver and administer EV programs). The results are intended to inform EV policies and deployment strategies that maximize utility system benefits and minimize ratepayer costs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Peer-to-peer communication control for resilient operations of networked cyberphysical systems

This report includes two main accomplishments of the peer-to-peer communication control for resilient operation of networked microgrids project in FY24, which include a scheme for cyberattack-aware coordination of networked microgrids for supporting voltages of bulk power systems and a scheme for price signal-based operations of EV-rich networked microgrids with mixed ownership. First, the cyberattack-aware scheme enables networked microgrids to distributedly determine the amount of reactive power injection to support the voltage of bulk power system (BPS) in a fair manner. In this scheme, a risk-informed algorithm is presented to generate the peer-to- peer (P2P) communication graph with minimal risk of attack on communication links. To deal with cyberattacks on MG controllers, the resilient consensus algorithm (CA) is utilized for MG controllers to robustly estimate the total reactive power headroom, from which the MGs can accurately provide the needed amount of reactive power injection for supporting the voltage of BPS. The CA implementation and performance within the P2P communication framework are demonstrated on the IEEE 39-bus system with 6 microgrids contained in the distribution feeder under different cyberattack scenarios. Second, the price-based scheme enables the usage of the real-time price signal for the operations of electric vehicle (EV)-rich networked-microgrids with mixed ownership, in which not all the microgrids can communicate with the distribution system operator (DSO). In this scheme, a max consensus is introduced to enable the real-time price signal to be propagated from the DSO to all the microgrids, from which each microgrid controller will manage the DERs to balance the load demand and the power injection from the EV charging stations within its microgrid. Numerical results over one day with 288 slots of 5-minute intervals on the modified 123-node test feeder including 3 microgrids with high penetration of EV are presented to evaluate how the price signal affects the operations of networked microgrids under different charging strategies of the EV charging stations. The result indicates that our proposed EVCS (dis)charging strategy, which leverages the flexibility of EVs to support the grid through discharging during peak demand, proves to be a cost-effective solution that reduces operational costs while improving the social welfare of EV charging.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

US building energy efficiency and flexibility as an electric grid resource

Buildings consume 75% of U.S. electricity; therefore, improving the efficiency and flexibility of building operations could increase the reliability and resilience of the rapidly-changing electricity system. We estimate the technical potential near- and long-term impacts of best available building efficiency and flexibility measures on annual electricity use and hourly demand across the contiguous U.S. Co-deployment of building efficiency and flexibility avoids up to 742 TWh of annual electricity use and 181 GW of daily net peak load in 2030, rising to 800 TWh and 208 GW by 2050; at least 59 GW and 69 GW of the peak reductions are dispatchable. Implementing efficiency measures alongside flexibility measures reduces the potential for off-peak load increases, underscoring limitations on load shifting in efficient buildings. Overall, however, we find a substantial building-grid resource that could reduce future fossil-fired generation needs while also reducing dependence on energy storage with increasing variable renewable energy penetration.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Genetic algorithm for demand response: a stackelberg game approach

Demand response (DR) has gained a significant recent interest due to its potential for mitigating many power system problems. Game theory is a very effective tool to be utilized in DR management. In this paper, the DR between a distribution system operator (DSO) and load aggregators (LAs) is designed as a Stackelberg game, where the DSO acts as the leader and LAs are regarded as the followers. Due to the limitations of the centralized solution approaches, a genetic algorithm-based decentralized approach is proposed. To demonstrate the proposed approach, a case study concerning a day-ahead optimization for a real-time pricing market with a single DSO and three LAs is designed and optimized. The proposed approach is able to shift the demand peaks and prove that it has a great potential to be used for the Stackelberg game between a DSO and multiple LAs to fully exploit the potential of DR.

Amasyali, Kadir↗
DOE OSTI2022

Multiphysics modeling of a critical dual-purpose canister in a saturated geological repository

Dual purpose dry cask storage canisters for spent nuclear fuel are designed for storage and transportation, but are not licensed for permanent disposal in a geological repository. If dual purpose canisters were to be used to dispose of spent nuclear fuel in a geological repository, they would be expected to eventually breach and be flooded with groundwater, and it is shown that some fraction of these canisters will achieve criticality. In order to evaluate the consequences of canisters going critical in a repository, an initial capability has been developed for estimating the quasi-static power level of a critical canister using loosely coupled multiphysics simulations. The low power level in a critical canister enables coupling through precomputed physics proxies. This calculated power level is then used to compute the change in the critical canister’s isotopic inventory as a function of time. Three as-loaded canisters are evaluated and two were found to have power levels below 4 kW, with a modest effect on the radiological inventory over time. This effort also shows that although some DPCs will have extremely peaked power shapes, the relatively low power and long-time scales result in relatively homogenized thermohydraulic properties in the water within the DPC.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Optimal Storage Response to Utility Tariff Structures and Potential Use of Capacity Charges

Energy storage is increasingly being deployed in behind-the-meter use cases, partially in response to falling lithium-ion prices and new utility tariff structures. Utilities are grappling with new tariff design in the presence of distributed energy resources (DER), trying to motivate, fairly price the contribution of, and, in some cases, discourage, certain operation of DER. There are opportunities for energy storage under emerging tariff structures, but utility net load management objectives for storage remain unclear. Diverse tariff structures motivate the use of energy storage to provide one or a combination of: energy arbitrage, energy shifting, solar self-consumption, and import shaving. This paper formulates a linear optimization to demonstrate the optimal storage tariff response, examining customer net load metrics under diverse utility tariff structures, such as time-of-use, net metering, feed-in tariffs, zero export tariffs, and demand charges. A key contribution of this paper is the introduction and examination of a capacity charge as mechanism that can both motivate reductions in both peak import, and exports, along with greater load levelling.

behind-the-meter↗
DOE OSTI2022

Building energy efficiency and load flexibility optimization using phase change materials under futuristic grid scenario

This work investigates the energy savings and load flexibility capacity of phase change material (PCM) integrated into building envelope under a future energy generation scenario, where 80% of the energy load comes from renewable sources. Using community-scale modeling, we first determine the net thermal storage required to fully manage the demand variability at a utility grid, and then using whole-building simulations we optimize operating parameters - such as PCM thickness, latent heat, interior setpoint profile, PCM distribution, and heat transfer coefficient - to determine the optimal conditions required for maximum energy reduction and load flexibility without compromising occupants' thermal comfort. The optimal PCM-integrated envelope proposed in this study can provide annual load flexibility up to 33.6% and annual energy savings up to 10.8% in a lightweight residential building located in Baltimore, MD. This study is relevant given the increasing contributions of renewable energy in the total energy generation mix, leading to a significant time-imbalance between peak energy demand and peak energy production. While thermal energy storage using PCM is a recognized technique, there is no known prior study dedicated to examining the thermal performance of PCM-integrated envelope under future energy generation scenarios. This study bridges the research gap by investigating a practical way to implement PCMs in the buildings and maximize the energy efficiency as well as load flexibility related benefits.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

A Scalable and Distributed Algorithm for Managing Residential Demand Response Programs using Alternating Direction Method of Multipliers (ADMM)

For effective engagement of residential demand-side resources and to ensure efficient operation of distribution networks, we must overcome the challenges of controlling and coordinating residential components and devices at scale. To overcome this challenge, we present a distributed and scalable algorithm with a three-level hierarchical information exchange architecture for managing the residential demand response programs. First, a centralized optimization model is formulated to maximize community social welfare. Then, this centralized model is solved in a distributed manner with alternating direction method of multipliers (ADMM) by decomposing the original problem to utility-level and house-level problems. The information exchange between the different layers is limited to the primary residual (i.e., supply-demand mismatch), Lagrangian multipliers, and the total load of each house to protect each customer’s privacy. Simulation studies are performed on the IEEE 33 bus test system with 605 residential customers. The results demonstrate that the proposed approach can save customers’ electricity bills and reduce the peak load at the utility level without much affecting customers’ comfort and privacy. Finally, a quantitative comparison of the distributed and centralized algorithms shows the scalability advantage of the proposed ADMM-based approach, and it gives benchmarking results with achievable value for future research works.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Improving the economics of battery storage for industrial customers: Are incentives enough to increase adoption?

As adoption of behind-the-meter battery energy storage increases across the United States, implementation continues to lag in the industrial sector. This analysis considers two manufacturing facilities with potential for load shifting to reduce peak demand. Although both facilities have load profiles that demonstrate great potential for regular and programmed demand reduction during peak hours, battery energy storage was deemed prohibitively expensive. A review of several existing utility and state-level policies and incentives determined that few may be rightsized for the industrial customer class. Furthermore, this analysis further considers multiple incentive structures and finds that although incentives increase viability of energy storage, developers must also consider optimization, unique load profiles, and use case to effectively increase adoption of battery energy storage by industrial customers.

25 ENERGY STORAGE↗
DOE OSTI2025

Potential Impacts of Dynamic Electricity Pricing in California: Load Shape and Customer Bill Impacts Under Elastic Customer Response

The increasing penetration of renewable energy in California has intensified grid management challenges, exemplified by the “duck curve” and the resulting need for steep ramping and curtailment of renewables. To address these issues, dynamic electricity tariffs that vary in near-real time are being considered to incentivize customers to shift demand and support the grid. This study extends previous work on the bill impacts of such tariffs in the absence of load response by quantifying the system-level and customer impacts of load response based on customer price elasticity. Customer-level load response modeling was conducted using meter data from 411,000 customers across residential, commercial, and industrial sectors. Customer demand elasticity was estimated using literature-based values, with scenarios ranging from low to high elasticity, including an automation-enhanced scenario. Results indicate that universal adoption of, and response to, dynamic tariffs can significantly reduce peak net load (by 15%) and maximum ramping requirements (by 20%) with moderate elasticity, delivering demand response resources comparable to or exceeding current programs at all elasticity levels. Bill analysis shows that, when responding elastically to dynamic prices, most non-PV customers experience modest savings, while PV customers may see higher effective rates due to lower compensation for exports during low-price periods. Emissions analysis reveals a reduction in per-kWh emissions system-wide, with a total absolute load increase of 2% accompanied by a negligible absolute emissions increase. The study concludes that while dynamic tariffs offer substantial grid benefits, customer bill savings under modeled response behaviors may be too modest to drive widespread adoption without additional incentives or enabling technologies. Future research should model flexible loads and advanced control technologies with greater fidelity to better represent the potential opportunities of dynamic tariffs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Optimal operation of multi-plant steam district heating systems for enhanced efficiency and sustainability

Despite their crucial role in supplying heat and power to universities, industries, and healthcare facilities, many steam-based district heating systems rely on outdated control methods. Among these, multi-central plant districts are particularly challenging due to the complexities of coordinating multiple plants, optimizing load distributions, and managing system downtime. In response, new operational strategies are developed to enhance the efficiency and sustainability of steam districts while utilizing existing resources. These strategies include reducing plant operational pressure without compromising the reliable supply to buildings and optimizing load allocation across multiple plants. The load allocation considers boiler part-load efficiency, runtime, network losses, and building pressure set points, and is compared with traditional multi-boiler controls. To support this exploration, new dynamic Modelica models are developed. In addition, methods to reduce modeling complexities are incorporated, enhancing their suitability for practical applications. A holistic district-wide analysis using a real university case study demonstrates a 4.7% fuel savings by lowering boiler operational pressure from 900 kPa to 600 kPa, along with a 13.3% reduction in condensation losses across the distribution network. Furthermore, the load allocation approach results in a 13.1% reduction in fuel consumption during peak winter periods and 15.3% during shoulder periods, with corresponding decreases in carbon emissions and fuel costs. This approach can also save maintenance costs by reducing the boiler runtime by 49.6%. In conclusion, this research underscores the benefits of retrofitting aging steam district heating systems, offering immediate operational improvements by enhancing efficiency, meeting regulatory compliance, and extending infrastructure lifespans while delaying costly overhauls.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Advanced blade-shaped thermal energy storage device: Development and application

Thermal energy storage (TES) using phase change materials (PCMs) is a promising approach for capturing and reusing excess thermal energy, yet widespread adoption is limited by low thermal conductivity, bulky configurations, and inadequate scalability. Here, this study presents a modular, blade-shaped TES prototype designed to address these challenges. The device integrates a lightweight aluminum shell, an embedded serpentine coil for active or passive heat exchange, and a cost-effective corrugated metal mesh for enhanced PCM thermal conductivity. With thickness-to-length and thickness-to-width ratios of 0.03 and 0.08, respectively, the blade-shaped TES achieves a compact, modular form factor suitable for space-constrained applications. Experimental testing demonstrated the efficient charge and discharge behavior of blade-shaped TES, capturing PCM superheating, phase-change transitions, and subcooling dynamics, with charging and discharging efficiencies of 94.9% and 94.6%, respectively. Also, the system can potentially achieve higher energy density than that of conventional TES designs. When integrated into a household refrigerator during the study, three blade-shaped TES modules successfully shifted 100% of peak-time compressor operation to off-peak hours, reducing energy consumption while maintaining more stable compartment temperatures. The blade-shaped TES's thin geometry, modularity, and enhanced thermal performance support scalable deployment across residential, commercial, and industrial applications, providing a versatile, cost-effective solution for high-efficiency, demand-flexible thermal energy management.

Blade-shaped↗
DOE OSTI2023

Differences In High Burnup Fuel Management Strategies to Minimize FFRD and Increase Economic Viability

The nuclear industry is pursuing approval of an increase in the length of the pressurized water reactor (PWR) cycle from 18 months to 24 months to reduce reactor downtime and enhance the economic competitiveness of nuclear energy. Such an increase in reactor cycle length will require that the maximum rod average burnup exceeds the current regulatory limit of 62 GWd/MTU, and it could peak at approximately 75 GWd/MTU, posing potential reactor safety and performance concerns. One such concern is that fuel fragmentation, relocation, and dispersal (FFRD) could occur during a severe loss-of coolant accident (LOCA) in which a fuel rod balloons and bursts, and pulverized fuel fragments are dispersed throughout the reactor’s primary coolant system. Previous analyses have identified which reactor operating conditions leave the core more susceptible to FFRD and have shown that FFRD susceptibility is strongly linked to fuel rod burnup and linear heat rate (LHR) history. The work described in this report uses an optimization strategy known as parallel simulated annealing (PSA) and a coarse mesh Purdue Advanced Reactor Core Simulator (PARCS) reactor physics model to develop two core fuel loading patterns, each with a different optimization objective. One core optimization maximized the core’s cycle length while still respecting regulatory limits on the radial peaking factor and soluble boron concentration with a peak rod average burnup of 75 GWd/MTU. The second optimization was aimed at minimizing FFRD susceptibility while still targeting a 24-month cycle length and respecting regulatory limits. PARCS model predictions were verified using the high-fidelity Virtual Environment for Reactor Applications (VERA). The two core designs were compared to highlight core design strategies to minimize FFRD susceptibility and to maximize economic viability.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

California Price Response Potential Study

California's energy landscape is undergoing a significant transformation, driven by the increasing integration of renewable energy sources, the increased adoption of distributed energy resources, the electrification of end-use loads, and the growing need for grid efficiency. To address these challenges, recent revisions to the State’s Load Management Standards (LMS) require all of California’s large utilities and community choice aggregators (CCAs) to offer dynamic electricity pricing options to customers by 2027. Dynamic pricing, which involves varying electricity rates based on real-time supply and demand conditions, offers a promising solution for optimizing grid operations, reducing costs, and incentivizing efficient use of grid capacity. Effective implementation of dynamic pricing requires understanding the potential impacts on customer bills, system load, and the cost-effectiveness of automation technologies. This study aims to evaluate the load response of various end-use devices to hourly dynamic prices. The end-uses studied here are space cooling, space heating, water heating, crop irrigation, pool and spa pumps, and electric vehicle (EV) charging, all for both residential and commercial applications, except for crop irrigation. In 2030, these end uses are forecasted to account for 18% of annual electricity demand in the state, but 40% of demand in the peak net load hour. By modeling possible price-responsive load dispatch algorithms and assessing the resulting impacts on both individual bills and the overall grid, we seek to inform policymakers and utilities about the potential benefits and challenges associated with dynamic pricing, and considerations for the design of dynamic pricing tariffs. Additionally, we will explore the cost effectiveness of adopting automation technologies to enable devices to respond more effectively to real-time price signals. This study considers a range of price profiles, accounting for differences across utilities and customer classes, and presents scenarios for dynamic price design via variation in the percentage of total customer electric costs that are allocated dynamically (versus constituting a fixed portion of the hourly volumetric price). We present results focused primarily on 2030, forecasting electricity prices under both low and high-cost scenarios, to inform longer-term tariff design considerations. We design tariffs by starting with 2019 prices that were calculated according to CalFUSE guidance (CPUC, 2022) and that have been used in recent studies; these prices are all-in volumetric rates that vary by utility and are revenue-neutral to each customer class. They are developed by considering six electricity cost components that are allocated hourly based on system load indicators (gross and net load, and wholesale prices). These prices are forecasted to 2030 for low and high cost scenarios, considering recent trends in total electricity costs with and without years of substantial wildfire mitigation investments. These tariffs, which allocate all costs on an hourly basis, are considered our “Full” dynamic tariff design scenario, while two additional scenarios explore allocating a portion of costs as a flat volumetric charge: the “Medium” scenario allocates 50% of revenue dynamically (and keeps 50% flat), while the “Mild” scenario allocates 20% of revenue dynamically. The 20% dynamic allocation on the Mild scenario aims to represent a case where only the marginal operating costs of the grid are included in the dynamic price.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Efficiency and Demand Flexibility in Large Office Buildings: The Potential for Cost Savings and CO 2 Reductions from Lighting and Cooling Measures

This report presents the estimated impact of lighting and cooling efficiency and demand flexibility measures in large office buildings in each state in the contiguous United States. It provides modeled results for three different metrics: bill savings, regional grid operational costs savings, and carbon dioxide (CO 2 ) emissions reductions. Lighting efficiency and demand flexibility are estimated to reduce load by up to 80 MWh/yr in a single large office building. These load reductions result in customer bill savings of up to $8,800/yr per building, with the highest savings in southern and midwestern states. Grid operating cost savings are estimated at up to $3,240/yr/building, with greatest benefit in southern and northeastern states. CO 2 emissions reduction potential is highest in the Dakotas, Nebraska, across the Midwest, in West Virginia, and in Mississippi (<48,200 kg/yr/building). Comparatively, cooling measures are found to have less load reduction potential (<28.5 MWh/yr/building), with the greatest potential in southern states including Texas, which ranks top of the list across several of the metrics studied. In numerous states, shifting cooling load to off-peak hours is found to increase costs and CO 2 emissions because precooling results in increased load during high-cost or high-CO 2 emissions periods. In general, focusing on cooling efficiency and load shedding has the potential for more savings. In all cases, the specific rate structure is a significant determinant in actual bill savings, which are up to $4,000/yr/building. To realize the full potential for bill savings through an energy measure, building operators must identify how the measure will change the building load pattern and the interaction of this load change with the applicable rate tariff. To realize CO 2 emissions reductions, industry and state coordination is needed to verify which fuel source is on the margin and then to create incentives for end users to reduce load during high-CO 2 emissions hours. Regular updates to data sets and analyses are critical. Regulators and policymakers are well positioned to facilitate the necessary coordination between the electric industry and building energy managers to develop appropriate price signals and incentives.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗