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During the past two decades, a number of two-terminal switching devices have been demonstrated in the literature. They typically exhibit hysteric behavior in the current-to-voltage characteristics. These devices have often been also referred to as memristive devices. Their capacity to switch and exhibit electrical hysteresis has made them well-suited for applications such as data storage, in-memory computing, and in-sensor computing or in-memory sensing. The aim of this perspective paper is to is twofold. Firstly, it seeks to provide a comprehensive examination of the existing research findings in the field and engage in a critical discussion regarding the potential for the development of new non-Von-Neumann computing machines that can seamlessly integrate sensing and computing within memory units. Secondly, this paper aims to demonstrate the practical application of such an innovative approach in the realm of cancer medicine. Specifically, it explores the modern concept of employing multiple cancer markers simultaneously to enhance the efficiency of diagnostic processes in cancer medicine.

Following Part I of this paper that reveals two distinct interpretations of demand curves and the corresponding different forms of the market clearing formulation, Part II of this paper analyzes the sequential and the iterative market clearing algorithms currently implemented in some markets. It is shown that these algorithms lead to socially sub-optimal solutions. The root cause of the problem is the lack of a clear representation of the demand benefit. The social surplus optimization based on the proper interpretation of demand curves is then proposed. A small numerical example is used to demonstrate the significant optimality gap between the socially optimal solution and the sequential/iterative solution. The potentially large impacts of the gap on the societal benefit and the individual resource revenues entail a close examination of these algorithms. [ABSTRACT FROM AUTHOR]

The presence of non-convexities in electricity markets has been an active research area for about two decades. The — inevitable under current marginal cost pricing — problem of guaranteeing that no market participant incurs losses in the day-ahead market is addressed in current practice through make-whole payments a.k.a. uplift. Alternative pricing rules have been studied to deal with this problem. Among them, Convex Hull (CH) prices associated with minimum uplift have attracted significant attention. Several US Independent System Operators (ISOs) have considered CH prices but resorted to approximations, mainly because determining exact CH prices is computationally challenging, while providing little intuition about the price formation rationale. In this paper, we describe the CH price estimation problem by relying on Dantzig-Wolfe decomposition and Column Generation, as a tractable, highly paralellizable, and exact method — i.e., yielding exact, not approximate, CH prices — with guaranteed finite convergence. Moreover, the approach provides intuition on the underlying price formation rationale. A test bed of stylized examples provide an exposition of the intuition in the CH price formation. In addition, a realistic ISO dataset is used to support scalability and validate the proof-of-concept. [ABSTRACT FROM AUTHOR]

In this paper, we investigate whether and under which conditions jointly owning a variable renewable source of electricity (VRES) and an electricity storage generates economies of scope in competitive electricity markets. Using a simple stochastic optimization model that assumes frictionless markets, we analytically show that no economic benefit arises from combining the two assets. This finding is in contradiction to large parts of the literature, which claim that it is in the economic interest of owners of VRES to additionally own electricity storage. We also identify circumstances where our argument does not hold and the combination of storage and VRES could theoretically make economic sense on the level of individual agents. In the last part of the paper, we demonstrate in a numerical case study of the German market that even in cases where our theoretical results do not hold, tying together storage and VRES may produce suboptimal results. [ABSTRACT FROM AUTHOR]

In the absence of demand-side bids for certain reliability products in the wholesale electricity markets, Independent System Operators (ISOs) traditionally use fixed demand requirements with penalty factors to clear the market. This approach does not allow proper tradeoffs between cost and reliability due to the inelasticity of the fixed requirements. Therefore, ISOs have been replacing the fixed requirements with sloped demand curves. However, the economic interpretation of demand curves, especially for multiple coupled products, is largely missing in both theory and practice. This could lead to the misrepresentation of the demand benefit in the market clearing objective. Using a two-product market model, Part I of this paper reveals two distinct interpretations of demand curves, each associated with a specific form of the market clearing problem. This implies that the construction of demand curves and the use of them in market clearing must be consistent with their economic interpretation. In Part II of this paper, we analyze the sequential and iterative market clearing processes currently implemented in some markets to demonstrate their solution sub-optimality. The root cause of the problem is the lack of a clear representation of the demand benefit. The social surplus optimization based on the proper interpretation of demand curves is proposed. [ABSTRACT FROM AUTHOR]

Advanced power systems for electromagnetic propulsion depend on capacitor banks as the first stage of pulsed power. Significant electrical energy is required to support a number of anticipated launch applications. In support of successful integration of these technologies, TPL has developed an improved dielectric material for high energy density capacitors. Recent developments have led to a novel dielectric film that can be fabricated and incorporated into innovative, high-voltage designs using conventional manufacturing processes. Full-scale (100 kJ) capacitors incorporating TPL's established film technology is projected to provide an energy density between 3.5 and 4.0 J/cm3. [ABSTRACT FROM AUTHOR]

Preventive diagnosis and maintenance of transformers have become more and more popular in recent times in order to improve the reliability of electric power systems. Dielectric testing techniques such as return voltage measurement (RVM) and polarization-depolarization current (PDC) measurement are being investigated as potential tools for condition assessment of transformer insulation. A better understanding and analysis of the dielectric test results are only possible with a clear understanding of the physical behavior of the insulation system in response to moisture and aging. A circuit model, which describes the dielectric behavior of the transformer's main insulation system, has been parameterized in this paper. The values of the parameters of the model have been identified from the dielectric tests. A correlation has been developed between the physical condition of the insulation and the equivalent model parameters that enable a clear and transparent interpretation of the dielectric test results. [ABSTRACT FROM AUTHOR]

Seismic events can cause devastating impacts on both overground and underground energy system infrastructure. This paper proposes a methodology to evaluate the impact of seismic events on the security of integrated electricity and gas system, mainly focusing on pipelines leakage and connection loss of electricity transmission lines. A stochastic model is used to formulate the damage level based on earthquake severity. The seismic impact on the integrated system is classified according to the levels of pipe leak and electricity line failure. Load curtailment due to limited generation capacity and overloaded transmission lines is thereafter quantified. Seismic intensity is generated randomly based on Monte Carlo simulation so that a certain seismic intensity can be related to relevant load curtailment. An integrated energy system with a 30-busbar electricity system and a 6-node natural gas network is used to demonstrate the effectiveness of the proposed method. The results clearly illustrate damage consequences under seismic events in terms of both probability and severity levels. This work can inform resilience enhancement scheme design based on the vulnerability performance and impact of both systems. [ABSTRACT FROM AUTHOR]

Due to the diversity of power consumption patterns, the false positive rate (FPR) of data-driven electricity theft detection (ETD) methods is too high to meet practical needs, which severely restricts the engineering application of data-based methods. To reduce FPR of ETD methods based on advanced metering infrastructure (AMI), a deep neural network with low FPR (LFPR-DNN) is proposed in this paper. First, a deep model is constructed based on one-dimensional convolution and residual network, which can automatically extract features from consumption data. Then, a two-stage training scheme is used to train the network. In the first stage, the conventional gradient descent algorithm is used to update the network weights. To minimize the impact of data imbalance on detection performance, focal loss is used. Besides, grid search is used to optimize the hyper-parameters of the model. In the second stage, with FPR as the optimization objective, the particle swarm optimization (PSO) algorithm is used to train the network. Finally, the proposed LFPR-DNN is verified by using the open Irish data set. Compared to other state-of-the-art classifiers, LFPR-DNN has the lowest FPR with 0.29% and the highest AUC with 99.42%. The FPR is reduced by an order of magnitude, which verifies the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

After the energy bill was announced in 2005, a transition of the national energy system from hydrocarbon to hydrogen had been envisioned. However, safe and convenient refueling and widespread availability of hydrogen from various energy sources are the prerequisites for the development of hydrogen economy. This paper focuses on the design of a hydrogen filling station with on-site hydrogen production by electrolysis of water. The proposed virtual wind farm and on-site installed photovoltaic supply the main and renewable energy for electrolyzers in the station. The utility electricity is served as an auxiliary energy resource to compensate the intermittence of wind and solar. Since the market clearing price (MCP) can change dramatically in the deregulated power market, the station can participate in the power market with a hydrogen-storage tank. Based on the forecasting of wind power with the artificial neural network model and MCP with the statistical model, this paper proposes an optimization algorithm for the hydrogen production schedule and the strategy of power trading to optimize the production costs of the station. [ABSTRACT FROM AUTHOR]

This paper presents a sensitivity analysis of similar days (SD) parameters to increase the accuracy of artificial neural network (ANN) and SD-based short-term price forecasting. Work presented in this paper is an extended version of previous works done by the authors to integrate ANN and SD method for predicting electricity price. The focus here is on sensitivity analysis of SD parameters while keeping the parameters same for ANN to forecast hourly electricity prices in the Pennsylvania--New Jersey--Maryland (PJM) (regional transmission organization in northeast America) electricity market. Several cases are simulated by choosing (a) two, (b) three, (c) four, and (d) five SD parameters to calculate the norm. In addition, sensitivity analysis has been carried out by changing the time framework of SD (d = 15,30, 45, 60) and the number of selected similar price days (N = 5, 10). From sensitivity analysis, it is identified that the optimized mean absolute percentage error (MAPE) is obtained using case-c with d = 30 and N = 10. MAPE of reasonably small value, along with forecast mean square error and mean absolute error of around 2$/MWh and 1$/MWh, is obtained for the PJM data, which has correlation coefficient of determination (R²) of 0.7758 between load and electricity price. Numerical results show that forecasts generated by the developed ANN model based on the optimized case are accurate and efficient. [ABSTRACT FROM AUTHOR]

With the deepening of electricity market reform in China, the competition in the electricity retail market becomes increasingly intense. Electricity retailers (ERs) need to explore new business models to enhance their competitiveness in the retail market. Meanwhile, with the improvement of industrial production and people's living standards, more and more nonlinear electrical equipment have been put into use, leading to severe harmonic pollution problems. Harmonic pollution causes loss of electricity, resulting in the economic loss of customers, especially for large industrial customers. In the above contexts, this paper proposes a novel business model that incorporates harmonic control as a value-added service into electricity retail contracts for utility-owned ERs. Both utility-owned ERs and customers can benefit from the designed business model. For customers, it helps them to improve the power quality while saving the electricity cost. For ERs, it helps them to cultivate the customer loyalty and improve the customer satisfaction. A case study is performed to demonstrate the effectiveness of the proposed business model. [ABSTRACT FROM AUTHOR]

This paper describes the design and implementation of an 80-kb logic-embedded non-volatile multi-time programmable memory (MTPM) with no added process complexity. Charge trap transistors (CTTs) that exploit charge trapping and de-trapping behavior in high-K dielectric of 32-/22-nm Logic FETs are used as storage elements with logic-compatible programming voltages. A high-gain slew-sense amplifier (SA) is used to efficiently detect the threshold voltage difference ( \Delta V\textrm {DIF} ) between the true and complement FETs in the twin cell. Design-assist techniques including multi-step programming with over-write protection and block write algorithm are used to enhance the programming efficiency without causing a dielectric breakdown. High-temperature stress results show a projected data retention of 10 years at 125 °C with a signal loss of <30% that is margined in while programming, by employing a sense margining logic in the SA. Scalability of CTT has been established by the first demonstration of CTT-based MTPM in 14-nm bulk FinFET technology with read cycle time of 40 ns at 0.7-V VDD. [ABSTRACT FROM AUTHOR]

A self-assembly and electrostatic (SAE) carrier technology is developed for high-precision and high-throughput chip-to-wafer 3-D integration. In this paper, water surface tension-driven chip assembly is combined with electrostatic adhesion to keep high alignment accuracies obtained by the capillary self-assembly process. The self-assembled chips can be firmly fixed on an SAE carrier wafer by electrostatic adhesion, and then, the chips can be readily detached from the carrier by discharging and transferred to another carrier with a temporary adhesive. This paper describes the impact of chip clamping forces and electrical reliability of the SAE carrier on chips to be 3-D stacked in chip-to-wafer configuration. Through-Si via formation is demonstrated by using a via-last 3-D integration process based on the SAE carrier. The demonstration shows that the SAE carrier maintains higher chip alignment accuracies than does conventional carrier without electrostatic adhesion. [ABSTRACT FROM AUTHOR]

Complex-cavity resonator with mode conversion is a promising way of improving mode selection properties of a terahertz gyrotron. In this paper, enhancement of the frequency tunability of the second-harmonic 0.4-THz gyrotron by using the complex-cavity resonator is considered. Results of detailed numerical study of the cold-cavity electromagnetic parameters are presented. Using the results of cold-cavity simulations, start-oscillation currents for different modes are calculated. [ABSTRACT FROM AUTHOR]

Hybrid energy systems with small modular reactors (SMRs)—a fast-emerging nuclear power plant technology—and renewables hold significant promise for the development of clean energy systems. This paper proposes a simulation model of SMR-based hybrid energy system for electricity and district heating (DH) with a detailed dynamic model of the reactor and a quasi-static model of the DH system in Siemens PTI PSS/E and PSS/Sincal. A multi-timescale approach, separating the load following and frequency regulation operation, is proposed to assess the flexible operation in the presence of highly intermittent renewable energy sources (RESs). A portion of a modified IEEE 30-bus system network is used as a test system for an isolated community to simulate the proposed hybrid energy system, and comparative results demonstrate the potential benefits of the DH system, thermal energy storage, and electrical energy storage to the SMR’s flexible operation. [ABSTRACT FROM AUTHOR]

As wind power penetration increases and fossil plants are retired, it is feared that there will be insufficient kinetic energy (KE) from the plants to support the system frequency. This paper shows the fear is groundless because the high inertias (H ≅ 4 seconds) of wind turbine-generators (WTGs) can be integrated to provide frequency support during generation outage. [ABSTRACT FROM AUTHOR]

In order to effectively make up for the defect of service life of re-tired power battery echelon used in microgrids (MGs) and improve the reliability of MGs system, an energy management method based on the energy hub of the electricity heat hydrogen multi-energy storage system (EHH-MESS) is proposed the in the paper. Firstly, considering the uncertain factors such as energy output, load fluctuation, retired power battery life defects and multi energy flow impact, MGs supply-demand balance model is built. Secondly, taking the economy of energy supply as the goal, a control method based on energy hub is proposed, which considers the weight of multi-objective optimization to control MGs. Finally, the effectiveness of the proposed energy management method is verified by simulation analysis, which improves the power supply reliability and economy of the system. [ABSTRACT FROM AUTHOR]

This paper provides an overview of hyperloop technologies, including a brief discussion of key components of a hyperloop system that determine the electric power requirements as a function of time. At-scale hyperloop systems do not yet exist, so a model was used to generate a set of load profiles for conceptual hyperloop realizations at four locations in the United States: two systems in California and one each in Colorado and Ohio. In all four cases, the modeled hyperloop load profiles included pulses in both active and reactive power. Grid modeling was performed to estimate the grid impacts and for discussing grid integration challenges. The paper discusses how energy storage systems might be used to eliminate the pulsating load characteristics or significantly reduce it to accommodate current grid planning guidelines. [ABSTRACT FROM AUTHOR]

Conventionally, power flow equations are used for distribution systems (DS) analysis to find the nodal voltages. For the particular form of the DS reconfiguration problem, however, a direct formulation in terms of branch flows allows a substantial increase in solution efficiency from an optimization point of view. In this paper, a set of linear current flow (LCF) equations are derived for DS. This formulation is then used within the network reconfiguration problem for loss minimization. A mixed-integer quadratically constrained programming (MIQCP) formulation, together with a mixed-integer linear programming (MILP) formulation, are proposed in this paper and assessed through simulations. In these comparisons, the MILP formulation shows computational advantages over the MIQCP version and the preceding literature. The proposed methods are evaluated on several test systems. [ABSTRACT FROM PUBLISHER]

This paper develops an analytical model for a residential microgrid (RMG) under a collaborative environment. The model assumes that the RMG community is under a social agreement referred to as Collaborative Consumption. The model includes a framework for RMGs using a unique method of demand response based on the particular characteristics of residential loads. Residential loads are categorized into three types based on their necessity and reschedulable ability. Consumers assign priority to their appliances. Then, the microgrid informs consumers about their real-time consumption and economic benefits associated with their participation in collaborative consumption. Accordingly, consumers can evaluate suggested alternatives prior to using appliances and consequentially make better decisions. Finally, the effects of model parameters on profit maximization are studied. The results of this paper estimate the economic benefits within a collaborative environment. [ABSTRACT FROM PUBLISHER]

So-called smart technologies are at the forefront of modern energy research. Many existing works focus on the effects of smart technologies, with scales ranging from a single household to an entire city. One area that is less studied is the adoption of these technologies. In this paper, we extend our prior work to develop a more robust framework for exploring the diffusion of basic smart grid technologies using a social-network-based model to study demand response adoption. This network is based around considering an end-user as a node, and any relationship where mutual trust and communication exists as an edge. In addition, we have incorporated mathematical representations of user personality traits in the decision-making progress to better simulate real-world actions. We observe greater usage of demand response when conventional electricity is high, when conscientiousness is high, and when the network is densely connected; all of these are reasonable results given logical behavior of the individual agents. Our model includes many tunable parameters in the update stages, of which the effects of only a few are included in this paper. This quantity of potential parameters, as well as the broad nature of the model and algorithm, makes our model a candidate for future improvement and development based on including different parameters. [ABSTRACT FROM PUBLISHER]

A building energy management system (BEMS), which forms an integral part of a smart grid, enables building operators to monitor, manage, and control the energy utilized in their buildings, thus reducing the demand and consumption of energy. Building operators are responsible for the day-to-day maintenance and operation of their buildings’ heating, cooling, mechanical, and electrical systems. This paper proposes an intelligent preemptive demand response management (DRM) using the BEMS to ensure contracted capacity or demand limit (CC/DL) is not exceeded and at the same time reduce energy consumption in buildings. In this paper, dynamic electric vehicle (EV) charge scheduling, speed control of air conditioning (AC) system’s variable speed drive (VSD), and priority-based load shedding are considered in the DRM program. The performance of the proposed DRM program to keep the building power demand within the CC/DL and reduce the energy consumption is tested and analyzed using the BEMS. [ABSTRACT FROM PUBLISHER]

As climate concerns, low natural gas prices, and renewable technologies increase the electric power sector's dependence on natural gas-fired power plants, operational and investment models for gas and electric power systems will need to incorporate the interdependencies between these two systems to accurately capture the impacts of one on the other. Currently, few hybrid gas–electricity models exist. This paper reviews the state of the art for hybrid gas–electricity models and presents a new model and case study to illustrate a few potential coupling effects between gas and electric power systems. Specifically, the proposed model analyzes the optimal operation of gas-fired power plants in a competitive electricity market taking into consideration gas purchases, gas capacity contracting, and residual demand uncertainty for the generation company due to renewable energy sources. [ABSTRACT FROM PUBLISHER]