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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]

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]

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]

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]

This paper develops a convex quadratic programming (QP) framework for the charge pattern optimization of plug-in hybrid electric vehicles (PHEVs) under time-varying electricity price signals. The work is motivated by the need for a computationally efficient PHEV charging model in the bidirectional vehicle-to-grid (V2G) integration studies, accounting for the hybrid powertrain dynamics and battery energy losses of the PHEVs. We adopt a previously developed PHEV power management system and construct a simplified model for the convex optimization problem. We use an equivalent circuit battery model to compute battery energy losses during grid charging and discharging. We then derive the total fuel and electricity cost of the PHEV as a quadratic function of battery state of charge and use a standard QP solver to minimize it for a few sample trips obtained from the National Household Travel Survey data set. Using a quad-core computer, the daily PHEV charging trajectory with 5-min time resolution can be optimized in less than tenth of a second. Through several examples, we show the application of the proposed method in various V2G-related problems, such as obtaining the aggregate load patterns of PHEVs, analyzing the potential impacts of large-scale bidirectional V2G integration, benchmarking the fuel economy of PHEVs, and determining the sensitivity of V2G load to abrupt price variations. [ABSTRACT FROM PUBLISHER]

In this paper, the mathematical model of an electricity generator with four static magnets and four dynamic magnets is suggested for the electricity generation. The introduced electricity generator is based on the electricity generator with two static magnets and two dynamic magnets, but it has three main advantages: it has more interactions between the static and dynamic magnets, it generates more electricity, and it generates smoother signals. Electricity generators are compared for the electricity generation. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of clearing sequential electricity markets under uncertainty. We consider the European approach, where reserves are traded separately from energy to meet exogenous reserve requirements. Recently, proposed stochastic dispatch models that co-optimize these services provide the most efficient solution in terms of expected operating costs by computing reserve needs endogenously. However, these models are incompatible with existing market designs. This paper proposes a new method to compute reserve requirements that bring the outcome of sequential markets closer to the stochastic energy and reserves co-optimization in terms of cost efficiency. Our method is based on a stochastic bilevel program that implicitly improves the inter-temporal coordination of energy and reserve markets, but remains compatible with the European market design. We use two standard IEEE reliability test cases to illustrate the benefit of intelligently setting operating reserves in single and multiple reserve control zones. [ABSTRACT FROM AUTHOR]

Battery energy storage is becoming an important part of modern power systems. As such, its operation model needs to be integrated in the state-of-the-art market clearing, system operation, and investment models. However, models that commonly represent operation of a large-scale battery energy storage are inaccurate. A major issue is that they ignore the dependence of the charging power on the battery state of energy. Consequently, market players might suffer great monetary losses for not being able to follow their day-ahead schedule and/or deliver the scheduled reserves. In order to bridge the gap between very detailed low-level battery charging constraints and high-level battery operation models used in the literature, this paper examines a dependence of battery charging ability on its state of energy. It proposes a laboratory procedure, which can be used for any battery type and technology, to obtain this dependence. It also formulates an accurate linear battery charging model, which closely approximates the real-life battery charging constraints. The proposed battery charging model is compared against the models commonly used in the literature. Battery operation schedules obtained by all the models are compared against experimentally obtained results in order to assess the value of the proposed model in real life. [ABSTRACT FROM AUTHOR]

An efficient energy management requires an exchange of information among different energy grids, such as electricity, gas, and heating. This paper analyzes the open metering system (OMS), a set of protocols used to provide the communication in a multiutility grid, with special focus on its capability of distributing the time information over the infrastructure. Currently, synchronization accuracy on the order of seconds is more than enough for the traditional metering applications, while more advanced power quality and monitoring systems may require synchronization on the order of hundreds of microsecond or less (as stated by the national and international normative for dynamically controlling loads or renewable power generator on the local microgrid). In this paper, advantages and limits of the current standard, when wireless M-bus (wM-bus) is used as a physical layer, are highlighted by means of the analysis of the synchronization uncertainty sources, and by the experimental evaluation. Next, a custom synchronization method is applied for the first time, since the current OMS standard does not propose any solutions. The analysis and the experimental characterization indicate that the synchronization limit on the last-mile segment of wM-bus, using off-the-shelf components, is on the order of 1.8 $\mu \text{s}$. The proposed methodology can easily be extended to other communication protocols adopting similar architectures. [ABSTRACT FROM AUTHOR]

This paper proposes a novel causality analysis approach called the causal Markov Elman network (CMEN) to characterize the interdependence among heterogeneous time series in multinetwork systems. The CMEN performance, which comprises inputs filtered by Markov property, successfully characterizes various multivariate dependencies in an urban environment. This paper also proposes a novel hypothesis of characterizing joint information between interconnected systems such as electricity and transportation networks. The proposed methodology and the hypotheses are then validated by information theory distance-based metrics. For cross validation, the CMEN is applied to the electricity load forecasting problem using actual data from Tallahassee, Florida. [ABSTRACT FROM AUTHOR]

This paper presents an experimental study of the temporal behavior of a bistable vertical cavity surface emitting laser (VCSEL) under the effect of optical feedback coming from vibrating diffusely reflecting surface. We demonstrate that a VCSEL operating in the regime of polarization switchings caused by self-mixing effects can greatly enhance a responsivity of the detection of microvibrations. For small amplitudes of microvibrations (less than $\lambda $ /2, where $\lambda $ is the operating wavelength of the laser), which cause only harmonic (or close to harmonic) oscillations in the laser output outside of the bistability region, the use of polarization switching can increase the responsivity up to 45 times. For the amplitudes larger than $\lambda $ /2, the response of a bistable VCSEL well reproduces a fine temporal structure of the self-mixing signal. A procedure of the data processing in the switching regime for the correct determination of the direction of the displacement and for the reconstruction of the waveform of surface vibrations with a basic resolution of $\lambda $ /2 is also shown. [ABSTRACT FROM AUTHOR]

This paper investigates the numerical accuracy of currently employed methods for electromagnetic transient simulation with power electronics. Existing tools often employ the second-order accurate trapezoidal method for numerical integration. However, solution techniques employed to facilitate ideal power-electronic switches do not always preserve this second-order accuracy at switch events. As a result, the step size required to achieve the desired level of accuracy is often smaller than expected, affecting simulation speed. Therefore, this paper aims to assess and improve the tradeoff between step size and accuracy. First, currently employed solution techniques are reviewed by deriving asymptotic estimates of the rates at which approximation errors decrease as the step size reduces. Afterward, a benchmark method is proposed that preserves second-order accuracy at switch events. The tools EMTP-RV and PSCAD are compared with this benchmark method for elementary circuit examples. The results demonstrate the potential to improve accuracy, by several orders of magnitude in some cases, without increasing the number of linear systems to be solved. [ABSTRACT FROM AUTHOR]

Convergence bidding (CB), a.k.a., virtual bidding (VB), is a market mechanism facilitated by Independent System Operators (ISOs) in wholesale electricity markets to help lower the gap between the prices in the day-ahead market (DAM) and prices in the real-time market (RTM). In this paper, we seek to answer two questions: 1) how can a strategic market participant maximize its profit when submitting CBs? 2) how can such strategically placed CBs affect the price gaps? Answering these questions is not straightforward but the results are insightful. The bidding problem in this context is a bi-level optimization, where the upper-level is about maximizing the profit for the convergence bidder and the lower-level is the economic dispatch problem. By solving the formulated bidding problem, we investigate the impact of strategic CBs on the DAM and RTM locational marginal prices (LMPs) under various practical scenarios. We demonstrate the scenarios under which a strategic CB, whether on its own, or when it is submitted jointly with a physical demand or physical supply bid, can or cannot work as intended, and result in decreasing or increasing the price gap in nodal electricity markets. We also examine how the performance of strategic CBs can be affected by uncertainty in demand or generation bids as well as physical contingencies in the power system, such as transmission line tripping. Special cases, such as net-zero convergence bidding are studied. The long-term performance of strategic convergence bidding is investigated. The above and other market implications are discussed in multiple case studies. [ABSTRACT FROM AUTHOR]

The acceleration of distributed energy resources and carbon pricing policies have compelled utilities to act and to prioritize carbon-constrained infrastructure augmentation in their capital programs. To implement various carbon emission reduction policies, power system transmission planning has become more challenging. The existing energy system will face massive retirement of coal-fired power plants (CFPPs), large scale integration of renewable energy and network expansion. In this paper, an electricity network planning and transformation roadmap, which has two milestones, is put forward. In the first stage, a mathematical model is proposed based on the average cost of carbon emission reduction to realize the cooperation of CFPPs retirement and renewable energy investment. It can help the network carry out the transition from a fossil-fuel dominated system to a low-carbon oriented system. Because of the promising prospect of power-to-gas (P2G) technology, in the second milestone, a method based on carbon emission flow (CEF) is employed to help the power-to-gas stations (P2GSes) to select the construction site and capacity. The gas network constraints are modeled to guarantee that P2GSes can work smoothly without energy flow congestion in both electricity and gas networks. According to the simulation results in case studies, our method can reach the emission reduction target more economically and effectively, and the P2GSes can produce and absorb clean energy. [ABSTRACT FROM AUTHOR]

Hybrid electrical energy storage (HEES) systems have the potential to result in considerable cost savings by reducing the electric bills of home users. This paper first presents grid-connected dual-bank HEES system design and management to maximize the electric bill savings for residential users, and subsequently provides a comprehensive sensitivity analysis of the economic feasibility of residential HEES systems. Specifically, the paper describes a daily management policy based on energy buffering strategy with one bank as the main storage bank and the other as the energy buffering bank, and then derive the global design of HEES specifications based on the daily management results. Simulation results prove the effectiveness of energy buffering strategy and show the proposed HEES system is capable of bringing in profits under current input variables. Finally, a detailed analysis is conducted to show how each input variable affects the final design of the proposed residential HEES system and the maximum annual profits it achieves. Together with the design and control mechanism, the proposed analysis provides potential customers with the comprehensive knowledge of how HEES systems can be deployed to achieve savings in their electric bills. [ABSTRACT FROM PUBLISHER]

This paper proposes a real-time price (RTP)-based demand-response (DR) algorithm for achieving optimal load control of devices in a facility by forming a virtual electricity-trading process, where the energy management center of the facility is the virtual retailer (leader) offering virtual retail prices, from which devices (followers) are supposed to purchase energy. A one-leader, N -follower Stackelberg game is formulated to capture the interactions between them, and optimization problems are formed for each player to help in selecting the optimal strategy. The existence of a unique Stackelberg equilibrium that provides optimal energy demands for each device was demonstrated. The simulation analysis showed that the Stackelberg game-based DR algorithm is effective for achieving the optimal load control of devices in response to RTP changes with a trivial computation burden. [ABSTRACT FROM PUBLISHER]

Smart meters have been deployed in many countries across the world since early 2000s. The smart meter as a key element for the smart grid is expected to provide economic, social, and environmental benefits for multiple stakeholders. There has been much debate over the real values of smart meters. One of the key factors that will determine the success of smart meters is smart meter data analytics, which deals with data acquisition, transmission, processing, and interpretation that bring benefits to all stakeholders. This paper presents a comprehensive survey of smart electricity meters and their utilization focusing on key aspects of the metering process, different stakeholder interests, and the technologies used to satisfy stakeholder interests. Furthermore, the paper highlights challenges as well as opportunities arising due to the advent of big data and the increasing popularity of cloud environments. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a robust approach for scheduling of smart appliances and electrical energy storages (EESs) in active apartments with the aim of reducing both the electricity bill and the \CO2 emissions. The proposed robust formulation takes the user behavior uncertainty into account so that the optimal appliances schedule is less sensitive to unpredictable changes in user preferences. The user behavior uncertainty is modeled as uncertainty in the cost function coefficients. In order to reduce the level of conservativeness of the robust solution, we introduce a parameter allowing to achieve a trade-off between the price of robustness and the protection against uncertainty. Mathematically, the robust scheduling problem is posed as a multi-objective Mixed Integer Linear Programming (MILP), which is solved by using standard algorithms. The numerical results show effectiveness of the proposed approach to increase both the electricity bill and \CO2 emissions savings, in the presence of user behavior uncertainties. Mathematical insights into the robust formulation are illustrated and the sensitivity of the optimum cost in the presence of uncertainties is investigated. Although home appliances and EESs are considered in this work, we point out that the proposed scheduling framework is generally applicable to many use cases, e.g., charging and discharging of electrical vehicles in an effective way. In addition, it is applicable to various scenarios considering different uncertainty sources, different storage technologies and generic programmable electrical loads, as well as different optimization criteria. [ABSTRACT FROM AUTHOR]

This paper proposes a novel comprehensive operation and self-healing strategy for a distribution system with both dispatchable and nondispatchable distributed generators (DGs). In the normal operation mode, the control objective of the system is to minimize the operation costs and maximize the revenues. A rolling-horizon optimization method is used to schedule the outputs of dispatchable DGs based on forecasts. In the self-healing mode, the on-outage portion of the distribution system will be optimally sectionalized into networked self-supplied microgrids (MGs) so as to provide reliable power supply to the maximum loads continuously. The outputs of the dispatchable DGs will be rescheduled accordingly too. In order to take into account the uncertainties of DG outputs and load consumptions, we formulate the problems as a stochastic program. A scenario reduction method is applied to achieve a tradeoff between the accuracy of the solution and the computational burden. A modified IEEE 123-node distribution system is used as a test system. The results of case studies demonstrate the effectiveness of the proposed methodology. [ABSTRACT FROM PUBLISHER]

Game theoretic methods and simulations based on reinforcement learning (RL) are often used to analyze electricity market equilibrium. However, the former is limited to a simple market environment with complete information, and difficult to visually reflect the tacit collusion; while the conventional RL algorithm is limited to low-dimensional discrete state and action spaces, and the convergence is unstable. To address the aforementioned problems, this paper adopts deep deterministic policy gradient (DDPG) algorithm to model the bidding strategies of generation companies (GenCos). Simulation experiments, including different settings of GenCo, load and network, demonstrate that the proposed method is more accurate than conventional RL algorithm, and can converge to the Nash equilibrium of complete information even in the incomplete information environment. Moreover, the proposed method can intuitively reflect the different tacit collusion level by quantitatively adjusting GenCos’ patience parameter, which can be an effective means to analyze market strategies. [ABSTRACT FROM AUTHOR]