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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]

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]

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]

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 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]

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]

In light of the increasing coupling between electricity and gas networks, this paper introduces two novel iterative methods for efficiently solving the multiperiod optimal electricity and gas flow (MOEGF) problem. The first is an iterative MILP-based method and the second is an iterative LP-based method with an elaborate procedure for ensuring an integral solution. The convergence of the two approaches is founded on two key features. The first is a penalty term with a single, automatically tuned, parameter for controlling the step size of the gas network iterates. The second is a sequence of supporting hyperplanes and halfspaces for controlling the convergence of the electricity network iterates. Moreover, the two proposed algorithms use as a warm start the solution from a novel polyhedral relaxation of the MOEGF problem, for a noticeable improvement in computation time as compared to a cold start. Unlike the first method, which invokes a branch-and-bound algorithm to find an integral solution, the second method implements an elaborate steering procedure that guides the continuous variables to take integral values at the solution. Numerical evaluation demonstrates that the two proposed methods can converge to high-quality feasible solutions in computation times at least two orders of magnitude faster than both a state-of-the-art nonlinear branch-and-bound (NLBB) MINLP solver and a mixed-integer convex programming (MICP) relaxation of the MOEGF problem. The experimental setup consists of five test cases, three of which involve the real electricity and gas transmission networks of the state of Victoria with actual linepack and demand profiles. [ABSTRACT FROM AUTHOR]

Cogeneration coupling of small modular reactor (SMR)-based nuclear power plants (NPPs) with district heating (DH) systems enhances an SMR’s ability to provide flexible operation. Coordinated reactor control and DH steam extraction can fulfill the load following requirements, while a battery energy storage system (BESS) and steam bypass system can absorb short-term disturbances, allowing the system to host renewable energy sources (RESs) such as wind and photovoltaics (PV). This paper develops an optimal operation framework for a SMR-RES hybrid energy system for electricity and DH and demonstrates the proposed scheme using a portion of the IEEE-30 bus system. Simulation results from a set of studies are provided to demonstrate load following (LF) and frequency regulation (FR) operation for a month-long period. The operational results for different RES hosting levels are analyzed, evaluating the optimum size of wind and PV plants for the proposed system. The results show the proposed hybrid energy system is feasible as a stand-alone system and can optimally host as high as 50% renewables. [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]

Over the past decades, the increasing penetration of distributed energy resources (DERs) has dramatically changed the power load composition in the distribution networks. The traditional static and dynamic load models can hardly capture the dynamic behavior of modern loads especially for fault-induced delayed voltage recovery (FIDVR) events. Thus, a more comprehensive composite load model with combination of static load, different types of induction motors, single-phase A/C motor, electronic load and DERs has been proposed by Western Electricity Coordinating Council (WECC). However, due to the large number of parameters and model complexity, the WECC composite load model (WECC CMLD) raises new challenges to power system studies. To overcome these challenges, in this paper, a cutting-edge parameter reduction (PR) approach for WECC CMLD based on active subspace method (ASM) is proposed. Firstly, the WECC CMLD is parameterized in a discrete-time manner for the application of the proposed method. Then, parameter sensitivities are calculated by discovering the active subspace, which is a lower-dimensional linear subspace of the parameter space of WECC CMLD in which the dynamic response is most sensitive. The interdependency among parameters can be taken into consideration by our approach. Finally, the numerical experiments validate the effectiveness and advantages of the proposed approach for WECC CMLD model. [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]

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]

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]

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 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]

This paper presents a framework to determine the strategic bidding of the wireless charging aggregators that operate wireless charging stations to provide in-motion charging services for the electric vehicles. The proposed framework captures the interactions between the electricity and transportation networks through competition among the wireless charging aggregators. Wireless charging aggregators participate in the wholesale electricity market to minimize the cost of gained energy from the bulk electricity network. In the retail wireless charging market, wireless charging aggregators compete to maximize their revenue by offering energy to the electric vehicles that are traveling on the transportation links. The strategic behavior of the wireless charging aggregators highlights the interdependence between the electricity and transportation infrastructure systems. In each system, the strategic behavior of the entities is determined by formulating a bi-level optimization problem which is further transformed into a mathematical problem with equilibrium constraints. The upper-level problem maximizes the profit of the entities while the lower level problem ensures the balance between the demand and supply in each infrastructure system. The demand and supply balance is guaranteed by the optimal power flow in the electricity network and the user equilibrium traffic assignment in the transportation network. [ABSTRACT FROM AUTHOR]

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]

In order to achieve sustainable development goals, modern power systems need to achieve a low-carbon transition. Retirement and replacement of aging coal-fired power plants (CFPP) is the major part of electricity network transition. However, the sudden retirement of large aging CFPP could lead to power supply or reserve shortages and generally, it takes several years to complete the replacement process. This paper proposes a low-carbon electricity network transition model, helping to plan aging CFPP retirement and renewable power plants installation as well as network augmentation. The trade-off decisions are found among three conflicting objectives including cost, risk and carbon emission. Moreover, the carbon emission flow (CEF) model is applied in the planning process to calculate carbon emissions from the demand side, in order to assess the efficacy of the low-carbon transition. A relatively new multi-objective natural aggregation algorithm (MONAA) is introduced and applied to find the optimal solution. The proposed model is verified on a modified IEEE 24-bus RTS system and a modified IEEE 118-bus system. According to the numerical results, the proposed model can achieve carbon mitigation, cost and risk benefits and provide a roadmap to guide the energy network transformation towards a low-carbon economy. [ABSTRACT FROM AUTHOR]

Inverse equilibrium modeling fits parameters of an equilibrium model to observations. This allows investigation of whether market structures fit observed outcomes and it has predictive power. We introduce a methodology that leverages relaxed stationarity conditions from Karush-Kuhn-Tucker conditions to set up inverse equilibrium problems. This facilitates reframing of existing equilibrium approaches on power systems into inverse equilibrium programs. We illustrate the methodology on network-constrained and unconstrained Nash-Cournot games between price-making power generators. The inverse equilibrium problems in this paper reformulate into linear programming problems that are flexible and interpretable. Still, inverse equilibrium modeling provides generally inconsistent estimation and econometric approaches are better for this purpose. [ABSTRACT FROM AUTHOR]

A basic principle of electricity market design is that cost allocation should follow cost causation as closely as possible. As renewable resources play a larger role in electricity generation, system operators have become increasingly concerned about costs related to variability in net load. While variable generation resources have attracted a great deal of attention along these lines, load is a larger source of variability in most systems. This paper introduces a Shapley value framework to assess the total system cost attributable to electricity market participants that either exacerbate or alleviate net load variability. Furthermore, this paper investigates the extent to which these costs are adequately reflected in wholesale market prices. Numerical tests indicate that the cost of variability on the hourly time scale is in most cases internalized by market-integrated generators and buyers, mitigating concerns that variability imposes a socialized cost from ramping or cycling of thermal generators. [ABSTRACT FROM PUBLISHER]

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]

Carbon financing policies such as emission trading have been used to assist in emission mitigation worldwide. As energy end-users/consumers are the underlying driver of emissions, it would be difficult to effectively mitigate carbon emissions by creating an emission trading market without active end-users’ involvement. In electricity markets, demand side management (DSM) in the smart grid can manage demands in response to power supply conditions and influence end-users to contribute to improving both network efficiency and economic efficiency. However, it is a relatively new topic to study the environmental benefits of DSM. This paper proposes a two-stage scheduling model to comprehensively investigate the environmental benefits of consumers participating in both electricity and carbon emission trading markets through active DSM. A developed zero sum gains-data envelopment analysis (ZSG-DEA) model based multi-criteria allocation scheme for emission allocation is employed. Meanwhile, the carbon emission flow model (CEF) is applied to track the “virtual” carbon flow accompanying power flow. According to case studies on the IEEE 24-bus system and IEEE 118-bus system, the proposed model can effectively achieve carbon emission mitigation and provide consumers extra environmental benefits in some scenarios. This model can be an important guide for governments to establish emission trading schemes. [ABSTRACT FROM AUTHOR]

Synchronous generators are operating for less time than before or being decommissioned in the National Electricity Market (NEM) of Australia, due to the proliferation of asynchronous wind and solar generation. Sub-networks of the NEM will face inertia shortages in the near future. This paper develops a formulation of zonal inertia constrained generator dispatch for power systems with a diversified generator portfolio including synchronous generators, synchronous condensers, inverter-interfaced generators and energy storages. Zonal inertia constraints are formulated in unit commitment and optimal power flow to limit the rate of change of frequency (RoCoF) in the event of network separation. Load frequency relief is also considered to reduce the ramp rate requirement of primary reserve. The proposed formulation can reduce the average cost of primary reserve and maintain zonal inertia adequacy to constrain RoCoF in case of the trip of the interconnector(s). [ABSTRACT FROM AUTHOR]

Constraint relaxation is widely used in electricity market clearing to obtain an acceptable solution when the market model is infeasible facing conflicting operating requirements. However, the penalty factors added in the objective function cause extremely high price spikes. Such a problem is currently handled by ad-hoc manners in power industries. After summarizing the current industrial practice and relative problems, we present a unified pricing model under constraint violations, which considers the prices as decision variables. Theoretical proof shows that the proposed method is capable of representing LMP with price spikes and current industrial practice. Hence, the pricing model has the ability of achieving more desired prices under the same pricing requirements. Furthermore, this paper presents a practical implementation of this unified model. The minimization of uplift payments, which is caused by the split of pricing and scheduling, is used as the objective function. This model can be transformed to a linear formulation without accuracy loss. Case studies show that the proposed pricing method brings distinct advantages compared with current industrial practices. [ABSTRACT FROM AUTHOR]

With ever increasing people's awareness of low carbon and environmental protection, electric vehicles are gradually gaining wide popularity. However, the driving endurance of the electric vehicle is the biggest shortage that hinders the fully acceptance of this new vehicle technology. To deal with this shortage, this paper proposed a vehicle-to-vehicle (V2V) electricity trading scheme based on Bayesian game pricing in blockchain-enabled Internet of vehicles (BIoV). Specifically, the Bayesian game is adopted for pricing in the distributed BIoV with incomplete information sharing. The optimal pricing under the linear strategic equilibrium has been obtained which maximizes the utilities of both sides of electricity transaction. The transaction volume is determined from the formulated convex problem that maximizes the social welfare. Then, the pricing game is implemented by the dedicated smart contract. Blockchain guarantees its trustworthiness, security, and reliability. Finally, the experimental results show that referring to the benchmark of static game with complete information, the proposed Bayesian game with incomplete information can achieve approximate satisfaction of users. The degree of approximation can reach to 98% when the pricing ranges of buyers and sellers are close. Moreover, the proposed scheme has great advantages over the static game with complete information in terms of communication overhead and timeliness in the decentralized IoVs. [ABSTRACT FROM AUTHOR]

Coordinated operations of electricity and district heating networks offer a potential for mitigating inherent variability of renewable energy sources (RES) in the ongoing transition to smart grids. This paper proposes a two-stage distributionally robust optimization (DRO) approach to determine the optimal day-ahead unit commitment in coordinated electricity and district heating networks with variable RES power output. The proposed formulation is to minimize the worst-case expected total cost over an ambiguity set comprising a family of probability distributions with given support and moments of RES power output. As such, the proposed DRO approach can overcome the limitations of stochastic programming in its inherent dependence of exact probability distributions along with a huge computational burden, but also becomes less conservative than classical robust optimization. The pertinent DRO model is eventually reformulated as a tractable mixed-integer second-order cone (SOC) programming after employing linear decision rules and the SOC duality. Simplified affine policies are utilized to further improve computational tractability and performance. Finally, case studies are conducted based on Barry Island electricity and district heating networks. The numerical results demonstrate the decision-making superiority of the proposed method as compared with deterministic, stochastic programming, and robust optimization approaches. They also validate the computational improvement of the proposed approach by employing simplified affine policies. [ABSTRACT FROM AUTHOR]

An electricity bill of a data center (DC) is determined not only by how much energy the DC consumes, but especially by how the consumed energy is spread over time during the billing cycle. More specifically, these electricity costs are essentially made up of two major charges: the Energy Charge, a cost based on the amount of consumed energy (in kWh), and a Peak Charge, a cost based on the maximum power (in kW) requested during the billing cycle. The latter charge component is forced to encourage DCs to balance and regulate their power demands over the billing cycle, allowing the utility company to manage congestion without increasing supply. This billing model has thus called for the development of peak power shaving approaches that reduce costs by smoothing peak power demands over the billing cycle to minimize the Peak Charge component. In this paper, we investigate peak power shaving approaches, and begin by using Google data traces to quantify and provide a real sense of how much electricity cost reduction can peak power demand shaving achieve on a Google DC cluster. We then discuss why peak power shaving is well-suited for reducing electricity costs of DCs, and describe two commonly used peak shaving approaches, namely energy storage and workload modulation. We finally identify and describe key research problems that remain unsolved and require further investigation. [ABSTRACT FROM AUTHOR]

To decarbonize the heating sector, residential consumers may install heat pumps. Coupled with heating loads with high thermal inertia, these thermostatically controlled loads may provide a significant source of demand side flexibility. Since the capacity of residential consumers is typically insufficient to take part in the day-ahead electricity market (DAM), aggregators act as mediators that monetize the flexibility of these loads through demand response (DR). In this paper, we study the strategic interactions between an aggregator, its consumers and the DAM using a bilevel optimization framework. The aggregator-consumer interaction is captured either as a Stackelberg or a Nash Bargaining Game, leveraging chance-constrained programming to model limited controllability of residential DR loads. The aggregator takes strategic positions in the DAM, considering the uncertainty on the market outcome, represented as a stochastic Stackelberg Game. Results show that the DR provider-aggregator cooperation may yield significant monetary benefits. The aggregator cost-effectively manages the uncertainty on the DAM outcome and the limited controllability of its consumers. The presented methodology may be used to assess the value of DR in a deregulated power system or may be directly integrated in the daily routine of DR aggregators. [ABSTRACT FROM AUTHOR]

Non-technical losses (NTL) in electricity utilities are responsible for major revenue losses. In this paper, we propose a novel end-to-end solution to self-learn the features for detecting anomalies and frauds in smart meters using a hybrid deep neural network. The network is fed with simple raw data, removing the need of handcrafted feature engineering. The proposed architecture consists of a long short-term memory network and a multi-layer perceptrons network. The first network analyses the raw daily energy consumption history whilst the second one integrates non-sequential data such as its contracted power or geographical information. The results show that the hybrid neural network significantly outperforms state-of-the-art classifiers as well as previous deep learning models used in NTL detection. The model has been trained and tested with real smart meter data of Endesa, the largest electricity utility in Spain. [ABSTRACT FROM AUTHOR]

Nonconvexity in electricity markets creates difficulties in the pricing of electricity today. Equilibrium prices often do not exist, and some forms of side payments are required to provide revenue adequacy for cleared offers and bids in markets. To improve market efficiency and transparency, academia and industry have proposed different pricing schemes, each of that emphasizes only a certain aspect of the desired price. In this paper, we summarize the existing pricing objectives and propose a unified model for pricing under nonconvexity. The tradeoff among competing objectives can be modeled. A specific example of the unified nonconvexity pricing model is presented, which can be regarded as a general representation of the convex hull pricing method and the commonly adopted locational marginal pricing scheme. The primal formulation of this model is derived. The economic insight of the convex hull pricing method is illustrated based on this model. An extreme-point-based solution method is presented. The proposed pricing model establishes a framework for analyzing the tradeoff among different objectives for pricing under nonconvexity and provides an opportunity to construct more economically justifiable prices through a modification of the objective function and constraints of the proposed pricing model. [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]

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]

A major restructuring of electricity markets takes place worldwide, pursuing maximum economic efficiency. In most modern electricity markets, including the widely adapted Locational Marginal Price (LMP) market, efficiency is only guaranteed under the assumption of perfect competition. Moreover, market design is heavily focused on deterministic conventional generation. Electricity markets, though, are vulnerable to strategic behaviors and challenged by the increased penetration of renewable energy generation. In this paper, we cope with the aforementioned bottlenecks by investigating the application of Vickrey–Clarke–Groves (VCG) auction in a two-stage stochastic electricity market. The VCG mechanism achieves incentive-compatibility by rewarding market participants for their contribution towards market efficiency, being attractive from both market operation and participants perspectives. Both traditional and VCG market-clearing approaches are explored and compared, investigating as well the impact of increasing wind power penetration. The main shortcoming of VCG, i.e., not ensuring revenue adequacy, is quantified in terms of market budget imbalance for various levels of wind power penetration. To this end, a novel ex-post budget redistribution scheme is proposed, which achieves to partially recover budget deficit. [ABSTRACT FROM AUTHOR]

Notable benefit can be brought by combined operation of a coupled electricity and heat system (CEHS), and be enhanced by introducing thermal energy storage (TES). Existing literature researching on this topic either neglects the heat network, or requires TES system being located and operated together with a combined heat and power unit. This paper aims at removing such requirement whilst taking heat network into account. With prevalent constant-flow variable temperature control strategy of heat network, the optimal dispatch model of CEHS considering independent TES (ITES) system with networked electric and heat energy distribution systems is presented. The non-convex parts in the heat network, mainly caused by the MFR reversal when ITES changes its operation state, are exactly reformulated to mixed-integer linear (MIL) constraints, which leads to a mixed-integer second-order programming formulation of the problem. Case studies demonstrate that introducing ITES not only brings reduction in the total operation cost, but also ameliorates the system reliability and its capability in accommodating renewables. [ABSTRACT FROM AUTHOR]

In electricity markets with a dual-pricing scheme for balancing energy, controllable production units typically participate in the balancing market as “active” actors by offering regulating energy to the system, while renewable stochastic units are treated as “passive” participants that create imbalances and are subject to less competitive prices. Against this background, we propose an innovative market framework whereby the participant in the balancing market is allowed to act as an active agent (i.e., a provider of regulating energy) in some trading intervals and as a passive agent (i.e., a user of regulating energy) in some others. To illustrate and evaluate the proposed market framework, we consider the case of a virtual power plant (VPP) that trades in a two-settlement electricity market composed of a day-ahead and a dual-price balancing market. We formulate the optimal market offering problem of the VPP as a three-stage stochastic program, where uncertainty is in the day-ahead electricity prices, balancing prices and the power output from the renewable units. Computational experiments show that the VPP expected revenues can increase substantially compared to an active-only or passive-only participation, and in the paper, we discuss how the variability of the stochastic sources affects the balancing market participation choice. [ABSTRACT FROM AUTHOR]

This paper proposes a novel model for the day-ahead self-scheduling problem of a virtual power plant trading in both energy and reserve electricity markets. The virtual power plant comprises a conventional power plant, an energy storage facility, a wind power unit, and a flexible demand. This multi-component system participates in energy and reserve electricity markets as a single entity in order to optimize the use of energy resources. As a salient feature, the proposed model considers the uncertainty associated with the virtual power plant being called upon by the system operator to deploy reserves. In addition, uncertainty in available wind power generation and requests for reserve deployment is modeled using confidence bounds and intervals, respectively, while uncertainty in market prices is modeled using scenarios. The resulting model is thus cast as a stochastic adaptive robust optimization problem, which is solved using a column-and-constraint generation algorithm. Results from a case study illustrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper presents a day-ahead coordinated scheduling method of hydro and wind power generation systems (HWPGSs) with consideration of uncertainties. In this method, we first formulate the coordinated scheduling model with two-part price and power flow constraints, which manifests the fixed and variable economic benefits as well as the secure operation of HWPGS, respectively. Then, the interval analysis is used to study the effect of uncertain wind power, water inflow, and electricity load on the coordinated scheduling model. Finally, we use a real test system in a western area of China to conduct simulation studies, which verify the effectiveness of the interval analysis and the effect of price mechanism on the day-ahead coordinated scheduling of HWPGS. [ABSTRACT FROM AUTHOR]

In developing countries, reliable electricity access is often undermined by the absence of supply from the national power grid and/or load shedding. To alleviate this problem, smart micro-grid (SMG) networks that are small scale distributed electricity provision networks composed of individual electricity providers and consumers, are being increasingly deployed. To ensure the reliable operation of SMGs, monitoring is necessary for data collection and state estimation processes. However, highly calibrated and trustworthy smart meters that are ideally suited to perform such monitoring tasks are often costly and non-ideally suited to SMGs which operate under unreliable communication network infrastructures. As a result, SMGs are an easy target to an adversary who can very easily gain access to private information by monitoring transmission between nodes in the SMG network, and launch inference-based privacy attacks. These attacks lead to electricity theft and grid instability problems in the SMG. The widely popular differential privacy (DP) technique (a rigorous technique in the family of privacy-preserving data publishing (PPDP) techniques to mathematically guarantee the preservation of data privacy) does not address multi-attribute correlations, that are inherently exploited by an adversary in inference attacks. In this paper, we propose HIDE, an oblivious computationally efficient, and rigorous information-theoretic privacy engineering framework for datasets/databases arising in the SMG environments that robustly accounts for multi-attribute correlations while preserving data privacy in a provably optimal fashion. A salient and powerful advantage of HIDE is its ability to generate optimal utility-privacy tradeoffs (computationally efficiently) when the privacy preserving entity in the worst case might have no prior statistical information that links a user's private data with his public data. [ABSTRACT FROM AUTHOR]

This paper deals with the protection of critical loads from voltage-related power quality issues using a dynamic voltage restorer (DVR). A generalized control algorithm based on instantaneous space phasor and dual $P$ - $Q$ theory has been proposed to generate the instantaneous reference voltages to compensate the load voltages with direct power flow control. The proposed algorithm adapts energy-optimized series voltage compensation, which results in a reduction of energy storage requirement. The proposed DVR control scheme can support the load from voltage-related power quality issues irrespective of the load current profile. Each leg of the three-phase three-leg split capacitor inverter is used to inject series compensation voltage in respective phases of the system. Model-based computer simulation studies and real-time experimental results validate the effectiveness of the proposed control algorithm. [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]

In the current competition framework governing the electricity sector, complex dependencies exist between electrical and market data, which complicates the decision-making procedure of energy actors. These must indeed operate within a complex, uncertain environment, and consequently need to rely on accurate multivariate, multi-step ahead probabilistic predictions. This paper aims to take advantage of recent breakthroughs in deep learning, while exploiting the structure of the problem to design prediction tools with tailored architectural alterations that improve their performance. The method can provide prediction intervals and densities, but is here extended with the objective to generate predictive scenarios. It is achieved by sampling the predicted multivariate distribution with a copula-based strategy so as to embody both temporal information and cross-variable dependencies. The effectiveness of the proposed methodology is emphasized and compared with several other architectures in terms of both statistical performance and impact on the quality of decisions optimized within a dedicated stochastic optimization tool of an electricity retailer participating in short-term electricity markets. [ABSTRACT FROM AUTHOR]

Smart grids provide great opportunities for design and implementation of sustainable and efficient electricity supply chains that are more resilient to disruptions. The problem with electricity supply chain network design (ESCND) using smart grid has not been broadly explored by researchers. This paper aims to approach this problem using a multiobjective robust optimization method. The three objectives are economic (profit maximization), environmental (greenhouse gas emissions minimization), and resilience (network resilience maximization). Efficiency maximization—power loss minimization—has also been taken into account using its corresponding cost element in the economic objective function. The proposed approach accounts for different unique smart grid components such as demand side management programs, microgrid structure, two-way distribution lines, and supplier–consumer nodes, while incorporating different interrelated decisions including facility location, capacity expansion, load allocation, and pricing. To solve the resultant model, a hybrid multiobjective robust optimization technique is proposed based on cutting plane and AUGMECON2 algorithms. The proposed model and solution approach are then applied to an actual case study and the produced results are thoroughly analyzed. We find that while economic and environmental objectives can be strictly conflicting, the implementation of smart grids can result in concurrent boost in both environmental performance and network resilience. [ABSTRACT FROM AUTHOR]

Privacy has become a big concern for consumers in electricity consumption activities, as privacy disclosure may cause losses to individuals. Since the information exchange and update in distributed energy management (DEM) of smart grids leaves eavesdroppers an opportunity to obtain the private information, it is worth studying privacy disclosure of DEM and design effective privacy-preserving schemes. In this paper, we investigate the privacy concern of a consensus-based DEM algorithm, where both generation units and responsive consumers cooperatively maximize the social welfare. First, we reveal that the private information of consumers including the electricity consumption and the sensitivity to the electricity price can be disclosed under traditional consensus-based DEM. Then, we propose a secret-function-based privacy-preserving algorithm to preserve the private information, where each node adds zero-sum and exponentially decaying noises to the original data for communications. It is assumed that local secret function can only be known by neighboring nodes. To relax this assumption, we propose a privacy-preserving algorithm, where each node utilizes real information for the state update and broadcasts the one with noise. We show that both of two proposed algorithms can preserve the privacy and the privacy degree is analyzed through $(\epsilon, \delta)$ -data-privacy. At the same time, the convergence and optimality of final solution are maintained. Extensive simulations demonstrate the effectiveness of proposed algorithms. [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]

In this paper, low-profile broadband planar folded transmitarray antennas (FTAs) are proposed and studied based on a novel Fabry–Perot polarizer. A novel element is proposed to carry out high-efficiency transmission, the phase compensation and the 90° polarization rotation at the same time. The element consists of two identical dielectric slabs, sandwiched by three metal layers, and has a thickness of only about $0.1\lambda $. To verify this element, a transmitarray antenna is first designed and its highly efficient radiation performance is demonstrated by both simulations and the experiments. Low-profile FTAs are then designed and studied. The results show that the advantages of broad bandwidth, high gain, lower profile, planar geometry, and ease of integration can be achieved. The experiments of the FTA antenna demonstrate a 3 dB gain bandwidths of 18% and a peak gain of 25.2 dBi. [ABSTRACT FROM AUTHOR]

This paper analyzes the influence of stator windings turn-to-turn fault on inverter-fed permanent magnet (PM) machines. By adding a secondary neutral point (NP) in stator windings, the turn fault detection and tolerant capability can all improve considering a small turn-to-turn short at an early stage. From the perspective on fault detection, the proposed windings with two NPs increase the equipment impedance per NP. Once a turn fault occurs, the fault-induced voltage harmonic magnitude increases. It results in a better signal-to-noise ratio for the fault detection. From the perspective on tolerant drive, the fault-induced circulating currents can flow across different phase coils in the windings with two NPs. Reduced effect on circulating currents in a faulty machine is achieved due to the current diversion. Turn fault induced additional losses are then decreased compared to conventional windings with only one NP. A 1-kW PM machine with two different windings is tested to verify the fault detection and tolerant performance. [ABSTRACT FROM AUTHOR]

Using solar photovoltaics (PV) to help a microgrid (MG) operator for cost reduction may not be a straightforward problem due to the intermittent nature of PV power generation and unpredictable load demands. One potential way to address this challenge is to use batteries that can store the surplus PV energy whenever possible and supply the energy back to the MG when needed. In this context, this paper proposes a battery energy management system (BEMS) for an MG, in which PVs and diesel generators (DGs) are the primary sources of electricity. The novelty of the proposed BEMS lies within the energy management of multiple types of batteries’ characteristics and the reduction of DGs’ operating hours simultaneously. Furthermore, the proposed BEMS also takes into account different characteristics of the batteries when controlling the charging and discharging decision to extend the battery lifetime. Real-world data of MG load and PV power generations are used to verify the effectiveness of the proposed BEMS. Through numerical simulation case studies, it is demonstrated that the proposed BEMS is capable of achieving the following: reduction in DGs’ operating hours, reduction in PV power fluctuations, and concurrent management of multiple batteries of different characteristics and extension of battery lifetime by controlling battery charge and discharge rate. [ABSTRACT FROM PUBLISHER]