The power supply from urban power network (UPN) ensures the proper operation of the urban metro network (MN). However, due to the close mutual connections, the negative impact of a power outage can propagate to the MN through a traction power network (TPN). Therefore, it is pressing to assess the resilience of such coupled networks. This paper initially proposes a multi-layer power-metro interdependent network (PMIN) model by integrating physical characteristics of UPN, TPN and MN into their topology networks. In the PMIN, the interdependence of UPN, TPN and MN is established in a hierarchical manner, where the community-based TPN model is practically elaborated to bridge fault propagation from the disabled UPN the to MN. Next, the traditional MN is equivalently converted into a mission-oriented network to simulate passenger rerouting processes, enabling the exploration of rerouting-incurred cascading failure with three proposed failed load redistribution disciplines. Finally, the resilience curves and assessment metrics are proposed to evaluate the resilience of the PMIN, with consideration of coupling effects among the UPN, TPN, and MN. The proposed method is applied to a quasi-authentic PMIN to reveal the fault propagation mechanisms and vulnerability characteristics of critical components within the PMIN, ultimately contributing to resilience enhancement.

The vigorous development of electric vehicles (EVs) is an important means of reducing carbon emissions and mitigating environmental problems such as the greenhouse effect. Battery swapping stations (BSSs) can both provide battery swapping services for large-scale EVs and charge batteries centrally. As the supply of fully charged batteries in the BSS shrinks, it becomes necessary to schedule the charging of the depleted batteries rapidly that users have swapped for fully-charged ones. The charging schedule for depleted batteries must be made without knowledge of future battery arrivals. In this context, this paper develops a mathematical model for online charging scheduling of BSSs, formulates the charging strategy as a two-dimensional rectangle packing problem, and quickly calculates the scheduling arrangement of batteries by partitioning the remaining available capacity of a BSS. Since there are limited battery types within the BSS which can provide battery replacement services, this paper supplements the proposed model with known battery types, which improves the utilization of the available capacity of BSSs. Finally, numerical results verify the effectiveness of the proposed model.Note to Practitioners—Electric vehicles (EVs) are becoming an alternative way to reduce carbon emissions in transportation systems. Herein, the optimal battery charging problem is the core problem when it comes to dispatching a huge number of EVs. Up to now, battery-swapping is widely used for EVs due to its simple, convenient way. Furthermore, a business model for the battery swapping stations (BSSs) is brought up, where EV users send their depleted batteries to the BSS and the BSS provides the users with a fully charged replacement battery from its warehouse, which only takes a few minutes. Since the maximum charging power of the BSS is limited by the capacity of the transformer connecting the BSS to the power grid, the BSS will adopt an optimal charging schedule that maximizes the charging benefit for large quantities of depleted batteries in the warehouse. However, the challenge is that the charging schedule for depleted batteries must be made without knowledge of future battery arrivals because the EV behaviors are difficult to predict. To address this problem, this paper developed an online charging scheduling algorithm, which formulates the charging strategy as a two-dimensional rectangle packing problem. The proposed method can provide battery replacement services in real-time and solve quickly without any information about incoming depleted EV batteries. The proposed model and method have been tested on the system with different numbers of batteries to show the effectiveness. Besides, the online two-dimensional rectangle packing problem can provide an online decision for BSSs.

Due to the spatially and temporally transferable workloads, data centers (DCs) become increasingly important in demand response (DR) implementations. By making full use of their owned DCs, data center operators (DCOs) are significant DR resource providers. To fully exploit the DR capability of the DCO on different time scales, we present a two-stage scheduling model for DCOs and the system operator (SO). In the DR scheduling, the SO formulates DR compensation prices first, and then each DCO decides its best-response power demands accordingly. Considering the profit-hunting property of the SO and DCOs, a two-stage DR Stackelberg game model is proposed. Furthermore, the existence and uniqueness of the Stackelberg equilibrium are proved. Finally, to protect the data privacy of DCOs, we design a Kriging-metamodel-based algorithm which avoids the DCO privacy exposure to the SO in the optimization process. Simulation results prove the accuracy and the calculation efficiency of the proposed Kriging-metamodel-based algorithm.Note to Practitioners—DCs play an increasingly important role in the power balance of grids in recent years. Existing studies focusing DCs’ DR participation generally consider the single-stage DR participation of DCs which ignores the role of DCOs in unified scheduling and management of their owned DCs. Considering the impacts of service request submitting time on the DR capabilities of DCs, this work proposes a novel two-stage DR scheduling model in the account of the unified scheduling role of DCs. Then, a Stackelberg game model is further proposed to characterize the profit-hunting property of the SO and DCOs. Finally, a privacy-protected algorithm is designed to address the optimization problem without explosions of DCO’s sensitive data. Simulation results show the effectiveness of the proposed model and algorithm in encouraging DCOs to adjust their energy consumption plans according to the SO’s request. Moreover, it is proven that the proposed algorithm can achieve global optimization with effective data privacy protection and low computation cost. Simulation results suggest that the proposed method can obtain a high-quality solution for a Stackelberg game model with privacy protection.

Energy storage (ES), with its flexible characteristics, has been gaining attention in recent years. The ES planning problem is highly significant to establishing better utilization of ES in power systems, but different market regulations impact the ES planning strategy. Thus, this paper proposes a novel ES capacity planning model under the joint capacity and energy markets, which aims to minimize the total cost for power consumers. The great challenge is that the ES planning model has a large number of time periods, which significantly increases the problem dimensionality. In order to alleviate the computational burden, a fully parallel algorithm is proposed to temporally decompose the original problem into a series of small sub-problems, which can be solved in parallel. Moreover, we find that the corresponding analytical solutions to the sub-problems remarkably accelerate the calculation speed while ensuring accurate results. Finally, numerical results verify the effectiveness of the proposed model. Note to Practitioners—Energy storage (ES) has become more and more essential to guaranteeing power balance in power and energy systems by shifting peak loads to valley loads. However, investors may face challenges to ES capacity planning due to the lack of business models. To address this challenge, price tariffs should be carefully investigated. In the practical power system, the market price should consider both the energy price and capacity price for industries and big companies. In the energy market, investors can gain a profit by selling energy at the peak load (high price) and buying energy in the valley (low price). It should be noted that the energy market cannot recover the ES investment cost, but investors can, in fact, reduce their capacity cost since ES can reduce the peak load. Consequently, we have designed a new business model for ES planning under joint capacity and energy markets to analyze the profits via the two market regulations. The computational burden is another challenge for the proposed multi-period convex optimization model. The model must consider a long-term simulation, potentially containing thousands of time periods, which can be difficult to solve. In order to alleviate the computational burden resulting from the long-term market simulation, we further propose a fully parallel algorithm to solve the proposed business model for ES quickly. To sum up, the proposed model and method have been tested on a practical company with a practical price tariff in China to show their effectiveness.

In Part I of this two-part article series, we proposed the locational marginal hydrogen price (LMHP) decomposition theorem and derived analytical expressions for LMHP components. Notably, the application of the LMHP decomposition theorem is based on the premise of obtaining optimal solution for the hydrogen market. Due to the existence of the Weymouth equation, which is used to characterize pipeline hydrogen flow, the hydrogen market clearing model is strongly nonconvex and difficult to solve directly. Although the literature has investigated solution algorithms for the Weymouth equation, improving the calculation efficiency while ensuring the accuracy of the solution remains a challenge. To address this knowledge gap, Part II of this two-part article series proposes an improved second-order cone programming (SOCP) algorithm with umbrella constraint identification to solve the hydrogen market. The hydrogen market clearing model is transformed into a mixed-integer SOCP problem, which can be solved iteratively. Before iteration, redundant constraints are removed to minimize the representation of the hydrogen market clearing model, thereby improving computational efficiency. Case studies based on the Belgium-20 node system and a 90-node system verify the effectiveness of the proposed LMHP decomposition theorem and solution algorithm for the hydrogen market.

High penetration of converter-interfaced renewable energy resources will significantly change the swing dynamics between synchronous generators (SGs) in future power systems. This article examines the impact of high converter penetration on wave-like disturbance propagation arising from sudden generator and load losses in radial (1-D) and meshed (2-D) power systems. To keep the uniformity assumption as converters are introduced, the rating of each SG is decreased with a converter resource making up for the reduction. Numerical simulations demonstrate that as the penetration level of constant-power grid-following (GFL) converters increases, the speed of disturbance propagation increases due to the reduced system inertia. Naturally, converters with the capabilities to positively respond to disturbances would in turn reduce the propagation speed. Analytical studies based on continuum models are presented for the 2-D system with SGs and constant-power GFL converters in order to visualize the disturbance propagation and validate numerical simulations based on differential-algebraic equations. In addition, fast active power control of converters can slow down the electromechanical wave (EMW) propagation and even contain it. These concepts are illustrated on the idealized radial and meshed systems and a reduced model of the U.S. eastern interconnection.

Traditionally, the electric distribution system operates with uniform energy prices across all system nodes. However, as the adoption of distributed energy resources (DERs) propels a shift from passive to active distribution network (ADN) operation, a distribution-level electricity market has been proposed to manage new complexities efficiently. In addition, distribution locational marginal price (DLMP) has been established in the literature as the primary pricing mechanism. The DLMP inherits the LMP concept in the transmission-level wholesale market but incorporates characteristics of the distribution system, such as high$R/X$ratios and power losses, system imbalance, and voltage regulation needs. The DLMP provides a solution that can be essential for competitive market operation in future distribution systems. This article first provides an overview of the current distribution-level market architectures and their early implementations. Next, the general clearing model, model relaxations, and DLMP formulation are comprehensively reviewed. The state-of-the-art solution methods for distribution market clearing are summarized and categorized into centralized, distributed, and decentralized methods. Then, DLMP applications for the operation and planning of DERs and distribution system operators (DSOs) are discussed in detail. Finally, visions of future research directions and possible barriers and challenges are presented.

Load aggregators (LAs) play a key role in fully tapping the demand response (DR) resources of small and medium-sized end-users to enable a more flexible power grid. In the ancillary service market, the LA can provide DR to the system by aggregating the resources of its users. In response to the issued DR program, end-users offer to provide DR resources. To help optimize the user bidding strategy, an evolutionary game model is presented here in view of the bounded rationality of bidders. A combined Q-learning and compound differential evolution (CDE) algorithm is proposed to deal with the problems of incomplete information and uncertainties in the opponents’ decision-making, and prevent the evolutionary stable strategy (ESS) from falling into a local optimum. Moreover, a cloud-computing-based framework is designed and agent servers are introduced to protect data privacy. Numerical results show that by adopting the proposed algorithm, the user's bidding price keeps slightly lower than the opponents’ price which guarantees its revenue remains on a high level. This indicates that the proposed algorithm has good adaptability for addressing incomplete information and uncertainties in opponents’ decision-making.

Customer baseline load (CBL) reconstruction is a critical problem in residential demand response. The difficulty of residential CBL lies in the variability of both irregular consumption and on-site distributed energy resources. Targeting the CBL reconstruction of residential prosumers, a regression-based estimation scheme is proposed using stacked autoencoders (SAEs) under the federated learning (FL) framework. In the FL framework, each residential unit (RU) stores and trains data locally without sharing them with neighboring RUs or the independent third party (ITP) responsible for CBL reconstruction. Local updates containing no load information are exchanged with the ITP (server) for the training improvement. The FL framework can, thus, protect the privacy of customers. Experimental results show that the proposed FL-based cascaded SAE outperforms the baseline on all tests and achieves up to 62.5% improvement in reducing reconstruction error. Moreover, it has enhanced privacy-preserving knowledge-sharing ability, higher efficiency, and better stability.

The progression of modern computing technologies assists the development of cyber-physical systems, which are transforming the legacy electrical power systems into smarter ones. The informationalization of the grid poses potential vulnerabilities concerning cyberattacks. With dynamic variations over time, cyberattacks can cause significant impacts on the secondary frequency control with various attack scenarios. In this article, by divulging the characteristics of dynamic attacks, the stability and dynamic responses of secondary frequency control systems are analyzed. The complete attack models considering dynamic load altering attack and dynamic false data injection attack are both derived first. Then the system stability is evaluated with different attack models through mathematical analysis. Eventually, the simulation studies against two benchmark power system models validate the evaluation results.

Self-healing capability is a critical factor for a resilient distribution system, which requires intelligent agents to automatically perform service restoration online, including network reconfiguration and reactive power dispatch. The article proposes the imitation learning framework for training such an agent, where the agent will interact with an expert built based on the mixed-integer program to learn its optimal policy, and therefore significantly improve the training efficiency compared with exploration-dominant reinforcement learning (RL) methods. This significantly improved training efficiency makes the training problem under$N-k$scenarios tractable. A hybrid policy network is proposed to handle tie-line operations and reactive power dispatch simultaneously to further improve the restoration performance. The 33-bus and 119-bus systems with$N-k$disturbances are employed to conduct the training. The results indicate that the proposed method outperforms traditional RL algorithms such as the deep-Q network.

Unexpected large power surges will cause instantaneous grid shock and, thus, emergency control plans must be implemented to prevent the system from collapsing. In this article, with the aid of reinforcement learning, novel model-free control (MFC)-based emergency control schemes are presented. First, multi-Q-learning-based emergency plans are designed for limited emergency scenarios by using offline-training-online-approximation methods. To solve the more general multiscenario emergency control problem, a deep deterministic policy gradient (DDPG) algorithm is adopted to learn near-optimal solutions. With the aid of deep Q network, DDPG-based strategies have better generalization abilities for unknown and untrained emergency scenarios, and thus are suitable for multiscenario learning. Through simulations using benchmark systems, the proposed schemes are proven to achieve satisfactory performances.

The rapid evolution of sensor technologies and communication networks is tightly coupling the cyber and physical layers of power systems. Because a few power grids have fallen victim to cyber intrusions causing unexpected device failure and large-scale power outages, enhancing power system cybersecurity is the utmost focus of power grid development today. Financially, the deregulation of the electricity market opens the gate to profit-oriented cyberattacks. Real-time market auctions rely heavily on the accuracy of state estimation, which is susceptible to cyberattacks. Extensive reviews have been conducted on modern power system cybersecurity. However, the lack of a comprehensive and in-depth review of electricity market cyberattacks prevents independent system operators from systematically analyzing the financial consequences of cyberattacks and limits public awareness of the significant monetary loss. This article briefly summarizes previous review works and analyzes the two-settlement market design from a cybersecurity perspective. Then the current achievements of electricity market cyberattacks are discussed, and state-of-the-art works are analyzed based on their contributions. Additionally, a few possible improvements and future directions are presented.

Industrial and commercial electricity customers have significant potential in providing flexibility for power systems through diverse demand response (DR) programs. However, the industrial and commercial potential of DR is not yet completely understood, especially regarding the emerging and advanced technologies associated with the smart grid. Advances in smart meter technology that allow monitoring and controlling responsive loads in real time will also be key enablers of DR potential. It can be more complex to implement DR for industrial loads if compared to residential loads mainly due to the reliability management that is more vital for industrial plants. Hence, this paper aims at providing a comprehensive review of the most recent advances on industrial and commercial DR. On this basis, this survey first presents the potential and technologies of DR in industrial and commercial sectors. Then, the existing models of DR in the mentioned sectors are presented. The presence of industrial and commercial DR in electricity markets is also investigated. Finally, the main positive and beneficial aspects, as well as challenges and barriers of industrial and commercial DR, are investigated.

The eleven papers in this special section focus on the industrial and commercial potential of demand response (DR). Customers from this non-residential market base have great potential in providing flexibility for power systems through diverse demand response (DR) programs. Intelligent energy management can be carried out with DR in industrial and commercial facilities, especially if onsite control, information, and communication technologies are available, enabling also the inherent automation capabilities of heating, ventilation, and air conditioning systems. In the dawn of the Smart Grid era, with increasing distributed generation and the conversion of traditionally passive consumers to newly active energy players in the market, DR is being effectively considered for outage management and network reinforcement deferral.

Power grid expansion, load demand increase, and renewable energy integration all present severe challenges to the grid operations. As transmission networks become increasingly stressed, research on voltage stability analysis (VSA) has attracted widespread attentions. Voltage instability usually occurs within a short period, which makes it difficult to be detected. To assess the stability margin, P-V curve method has been widely used for years. However, this approach is only effective when the load power factor or reactive power load does not change. To overcome this weakness, P-Q curve method can be applied to analyze the voltage stability problems. In this paper, the performance of both P-V and P-Q methods is tested on the IEEE 14 bus system. Simulation results show that P-Q curve method is more intuitive and convenient than P-V curve method on assessing the load margins.

The aim of this Special Section is to attract and report the latest advances toward the trend of applying advanced informatics techniques resolving complex problems facing power system operation and planning in the new era of smart grids. Special interests are given to the new methods that can handle various tasks of risk hedging and decision making appeared in eleven system operation and planning, though the scope has been slightly expanded to other topical issues in smart grids as well. The accepted eleven high-quality papers represent how the newadvances and solutions toward resolving complex problems facing power system operation and planning can be brought forward by continuously leveraging emerging techniques in the field of data analytics and informatics. It should be highlighted that with the increased penetration of various emerging technologies such as renewables and electric vehicles (EVs), secure and economic system operation and planning deserve continuous research efforts in producing the most up-to-date methods and solutions dealing with issues of diversified natures and complexities in future power grids. Specifically, the covered topics in this Special Section are topical and broad, concerning mainly power system security analysis and electricity market planning and operation under risks and uncertainties, which are briefly summarized.

Blackouts in our daily life can be disastrous with enormous economic loss. Blackouts usually occur when appropriate corrective actions are not effectively taken for an initial contingency, resulting in a cascade failure. Therefore, it is critical to complete those tasks that are running power grid computing algorithms in the energy management system (EMS) in a timely manner to avoid blackouts. This problem can be formulated as guaranteeing end-to-end deadlines in a distributed real-time embedded (DRE) system. However, existing work in power grid computing runs those tasks in an open-loop manner, which leads to poor guarantees on timeliness thus a high probability of blackouts. Furthermore, existing feedback scheduling algorithms in DRE systems cannot be directly adopted to handle with significantly different timescales of power grid computing tasks. In this paper, we propose a hierarchical control solution to guarantee the deadlines of those tasks in EMS by grouping them based on their characteristics. Our solution is based on well-established control theory for guaranteed control accuracy and system stability. Simulation results based on a realistic workload configuration demonstrate that our solution can guarantee timeliness for power grid computing and hence help to avoid blackouts.