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Hybrid Energy Storage System has been widely applied in aerospace, electric vehicle, and microgrid applications. The advantages are that they include complimentary technologies with both high power and energy capabilities. HESS have the potential to be useful to the bulk power systems, for example to increase the value of energy produced by variable generation resource through enabling participation in ancillary service markets. Actualizing the benefits of HESSs requires optimizing the combined ramping capability of aggregated HESS resources. To provide quality ancillary service, source of HESSs locating at multiple sites of variable generation resource must be optimally sized and cohesively controlled. Optimizing aggregated resources can be formulated as an optimization problem. Successfully solving this problem not only requires using effective solution methods but also depends on accurately and rapidly setting the necessary parameters in the problem formula. This article proposes a novel framework with double-layer structure to solve the optimization problem of aggregating ramping capability. Program developed on the upper layer focuses on solving the optimization of aggregating ramping capability among multiple HESSs and is compatible with most existing optimization algorithms. Program on lower layer of the framework targets at optimizing the local control of a single HESS based on a thorough analytics of HESS operation strategy presented in this article. Result of local optimization is also provided to upper layer program for updating the parameters in formula of optimization problem. Real time hardware-in-the-loop test is conducted to verify the performance of the optimization framework developed. The work presented is expected to provide guidance for implementing this framework in practical operation.

An integrated testbed using digital real-time simulator (DRTS) and a high-performance computing (HPC) cluster is presented here to compare speed and performance of computational schemes to mitigate time-critical issues in electric power systems. The first approach in this testbed validation is taken by running a set of closed-loop load shedding algorithms to compare and contrast two paradigms of arresting cascading failure propagation. Two algorithms involve solving DC and AC power flow model-based optimization problems to compute load shedding at different buses, while a model-based stochastic search using parallel computing provides a viable alternative. The algorithms are implemented in the DRTS-HPC testbed for the IEEE 14-bus benchmark transmission system. As a proof of the concept, simulation results are presented for implementation of closed-loop load-shedding algorithms for cascading failures in the DRTS-HPC testbed.

Most demand response programs focus primarily on commercial and industrial loads. The sheer number of decision-influencing variables combined with a lack of a comprehensive understanding of human behavior make it difficult to develop response programs that encourage greater residential consumer participation. This paper presents a multi-criteria decision making approach using actual survey data for identifying user preferences. The novelty of the work is in considering functionality, cost, and carbon emissions for a given set of home appliances toward energy management. The paper explains the design methodologies used to prioritize the preferences from 1023 survey participants. The results are expected to inform the design of Home Energy Management Systems (HEMS) for participating in demand response programs.

We use a deterministic cascade propagation model based on thermal overloading limits on the transmission lines to achieve a load shedding protection mechanism to mitigate cascading failures. Departing from the linearized DC-power flow based models, we adopt a more faithful AC-power flow model to compute the line flows. For AC power flow, we adopt the branch flow model, taking inspiration from the recent optimal power flow formulations, and use tools from convex optimization to solve the optimal load shedding problem. The results are demonstrated using a IEEE 14-bus test system in a Digital Real-Time Simulator (DRTS) environment.

The 21st century electric power grid is transforming with an unprecedented increase in demand and increase in new technologies. In the United States Energy Independence and Security Act of 2007, Title XIII sets the tenets for modernizing the electricity grid through what is known as the ?Smart Grid Initiative.? This initiative calls for increased design, deployment, and integration of distributed energy resources, smart technologies and appliances, and advanced storage devices. The deployment of these new technologies requires rethinking and re-engineering the traditional boundaries between different electric power system domains (Figure 1).

One challenge of transitioning to renewable energy is that household electricity use and renewable generation are often misaligned. Smart home energy management systems hold promise for shifting usage to match generation, but these systems need to be designed with the occupants’ preferences in mind. The purpose of the present research is to compare two approaches for collecting and modeling consumers’ load management preferences, both of which are amenable to use in a home energy management system. Specifically, we examine the performance of Simple Multi-Attribute Rating Technique Exploiting Ranks (SMARTER) and Analytic Hierarchy Process (AHP) in quantifying consumers’ preferences regarding air temperature (air conditioning and heating), water heating, dishwashing, clothes washing and drying, monetary costs, environmental impacts, and comfort/convenience. Two studies are presented: Study 1 examines the SMARTER approach, and Study 2 focuses on the AHP approach. In both studies, online surveys (NSMARTER = 956 and NAHP = 1023) were conducted to elicit preferences from participants across the United States. The preferences modeled by both approaches were validated based on (a) their ability to predict participants’ choices in a Discrete Choice Experiment and (b) their convergence with previous research on load-shifting behavior. The validation procedure suggests that the SMARTER approach is superior in modeling consumers’ preferences for load management. This research lays the groundwork for designing a smart home interface capable of collecting occupants’ preferences and using those preferences to deliver improved occupant comfort, lower operating costs, reduced environmental impact, and more significant demand response than exists today.

Optimal scheduling of hydrogen production in dynamic pricing power market can maximize the profit of hydrogen producer; however, it highly depends on the accurate forecast of hydrogen consumption. In this paper, we propose a deep leaning based forecasting approach for predicting hydrogen consumption of fuel cell vehicles in future taxi industry. The cost of hydrogen production is minimized by utilizing the proposed forecasting tool to reduce the hydrogen produced during high cost on-peak hours and guide hydrogen producer to store sufficient hydrogen during low cost off-peak hours.

Future smart homes are expected to satisfy homeowners by acting on their behalf through personalized adaptation to their preferences. It is important to understand how individuals' preferences vary, what occupants consider "ideal" and how they value tradeoffs between costs and benefits of home services. In this poster, we present three models of human preference capable of evaluating the utility of a home outcome and generating a unique, personalized score. Using these, a home energy management system (HEMS) can identify the outcome evaluated as best by homeowners. We discuss an online survey method, results, and comparison between three methods in terms of their preference prediction accuracy, time to complete, and participant usability.

Performing co-simulation studies on transmission and distribution systems software environments requires linking the transmission system elements in the former with highly detailed distribution network models and probabilistic load models in the latter. This paper presents a two-step algorithm for: (i) creating a scalable distribution system in a bottom-up approach to match the connected load at the transmission system bus; and (ii) populating the distribution system with active power loads possessing time varying load profiles. We demonstrate an application of the algorithm by creating a detailed distribution topology populated with residential loads expanded from a selected transmission node in a standard test system. All codes are written using the Python coding environment.