The rapid development of renewable energy sources (RESs) has led to their increased integration into microgrids (MGs), emphasizing the need for safe and efficient energy management in MG operations. We investigate the methods of MG energy management, primarily categorized into model-based and model-free approaches. Due to a lack of incremental knowledge, model-based methods need to be reengineered for new scenarios during the optimization process, leading to reduced computational efficiency. In contrast, model-free methods can obtain incremental knowledge via trial-and-error in the training phase, and output energy management scheme rapidly. However, ensuring the safety of the scheme during the training phases poses significant challenges. To address these challenges, we propose a safe reinforcement learning (SRL) framework. The proposed SRL framework initially includes a safety assessment optimization model (SAOM) to evaluate scheme constraints and refine unsafe schemes for ensuring MG safety. Subsequently, based on SAOM, the MG energy management issue is formulated as an assess-based constrained Markov decision process (A-CMDP), enabling the SRL can be adopted in this issue. After that, we adopt a Lyapunov-based safety policy optimization for agent policy learning to ensure that policy updates are confined within a safe boundary, theoretically ensuring the safety of the MG throughout the learning process. Numerical studies highlight the superior performance of our proposed method. Specifically, the SRL framework effectively learns energy management policy, ensures MG safety, and demonstrates outstanding outcomes in the economic operation of MG.

The future power systems with renewable energy sources (RESs) and electric vehicles (EVs) are usually subject to strong uncertainties in system parameters, communication network, and disturbances, which may severely degrade the frequency control performance. This article proposes a performance enhancing load frequency control (LFC) scheme for future power systems with strong uncertainties based on the Kalman-filter (KF) control compensation method. First, the LFC of a multiarea interconnected power system (MAIPS) with RESs and EVs is conceptualized as a linear stochastic and discrete model. Then, KF is introduced to evaluate the unmeasurable states of the LFC, so as to design an additional KF-based state feedback control law. The KF-based control loop serves as compensation for the original controller of the LFC system. Next, an optimal compensation control gain of the KF-based control loop is derived based on a differential optimal calculation method to enhance the control performance of the LFC of MAIPS under strong uncertainties. Finally, simulation results are conducted on a typical three-area LFC systems that integrate RESs and EVs, which demonstrate that the proposed control strategy can provide better control performance and robustness than the original LFC strategy when the systems are subject to strong uncertainties.

Recently, the development of the Internet of Things (IoT) enables more comprehensive and intelligent analysis and defense for cascading failures in power grids. This article summarizes power grids as temporal weighted networks (TWNs) which are different from conventional temporal networks. For TWN, the topological structure is fixed but weight distribution is time varying. Then it is noted that for different operating states represented by different weight (power flow) distribution at different time sections, the risks of cascading failures would be completely different. Inspired by the analysis of intersubnetworks power shifts in cascading failures, inverse-community (IC) structure is proposed in TWN to intuitively identify the risk of cascading failures. IC describes a structure in weighted networks with several communities in which the weighted interaction between communities is significantly stronger than that within the same community. Furthermore, the conventional modularity is upgraded as inverse-modularity (IM) to quantify the characteristic of IC structure in power networks. Subsequently, considering the risk of cascading failures represented by IM and the cost of power network operation, a security/economic dispatch (SED) method is designed to handle the optimal power dispatch issues. Simulation results prove a positive correlation between IM of power flow distribution and risk of cascading failures. Furthermore, the results on the IEEE 118-bus system demonstrate the effectiveness of the proposed SED method in mitigating cascading failure risks.

In the case of introducing the double integral state into the augmented vector, the time-varying delay square terms in the derivative of the Lyapunov-Krasovskii functional (LKF) usually needs to be treated with some negative-determination lemmas in the existing literatures, which are only sufficient conditions and are conservative to some extent. In this work, by introducing some new augmented variables, some zero-valued equations are proposed to avoid the appearance of these time-varying delay square terms, which have great potential to reduce the conservatism. Then, a stability result in terms of linear matrix inequalities is derived via the presented zero-valued equations. Finally, a representative example is provided to testify the usefulness and meliority of the raised stability criterion.

This article presents a novel reconstructed model for the delayed load frequency control (LFC) schemes considering wind power, which aims to improve the computational efficiency for PID controllers while retaining their dynamic performance. Via fully exploiting system states influenced by time delays directly, this novel reconstructed method is proposed with a controller isolated. Hence, when the PID controllers are unknown, the stability criterion based on this model can resolve controller gains with less time consumed. For given PID gains, this model can be employed to establish criteria for stability analysis, which can realize the tradeoff between the calculation accuracy and efficiency. The case study is first based on a two-area traditional LFC system to validate the merits of a novel reconstructed model, including accurately estimating the influence of time delay on system frequency stability with increased computational capability. Then, under traditional and deregulated environments, case studies are carried out on the two-area and three-area schemes, respectively. Through the novel reconstructed model, the efficiency of obtaining controller parameters is highly improved while their robustness against the random wind power, tie-line power changes, inertial reductions, and time delays remains almost unchanged.

This article proposesa control performance standard (CPS)-based fuzzy event-triggered scheme for load frequency control (LFC) of power systems with a limited communication bandwidth. First, a CPS-based fuzzy LFC system is established to reduce the wear and tear of the generating unit equipment. Then, based on the Lyapunov stability theory, a stability criterion of the LFC system is proposed to ensure the stable operation of the LFC system, which considers the threshold parameter of the event-triggered condition and the fuzzy gain in the fuzzy LFC system. Next, based on the stability criterion and the Gaussian-type curve-fitting method, a functional expression between the fuzzy gain and the threshold parameter is obtained. According to the expression, the threshold parameter is updated in real time with the change of fuzzy gain, so as to further save usage of the communication network bandwidth. Case studies based on a one-area power system and an IEEE 39-bus benchmark test system are undertaken. Simulation results show that the proposed scheme achieves three objectives: 1) to comply with CPS1 and CPS2 in the North American Electric Reliability Council; 2) to reduce wear and tear of the generating unit equipment; and 3) tosave more communication network resources.

In this article, the approach of an adjustable uncertainty set is proposed to deal with the uncertainty of renewable energy (RE) in unit commitment (UC). Demand response (DR) is co-optimized to reduce the operation risk of load shedding and RE curtailment when the RE falls out of the adjustable uncertainty set. In comparison with existing approaches with an adjustable uncertainty set, the proposed approach further incorporates DR requires no predefined parameters to constrain the deviation from the forecast RE. It divides the maximum RE set into subintervals, and bounds of the adjustable uncertainty set are determined among these subintervals with the consideration of DR in reducing the operation risk. The original mixed-integer nonlinear problem of UC scheduling is transformed to be a mixed-integer linear problem to be effectively solved. The performance of the proposed approach is verified on the IEEE 6-bus, 30-bus, and 300-bus systems. Through the comparison with existing methods, the effectiveness of the proposed approach in reducing the conservativeness is verified. The effectiveness of the proposed approach in the reduction of the operation risk of load shedding and RE curtailment is verified through the comparison between situations with and without DR.

Uncertain transmission delays, sampling periods, parameters uncertainties regarding the power system, load fluctuations, and the intermittent generation of renewable energy sources (RESs) will significantly influence a power system's frequency. This article designs a robust delay-dependent PI-based load frequency control (LFC) scheme for a power system based on sampled-data control. First, a sampled-data-based delay-dependent LFC model of power system is constructed. Then, by applying the Lyapunov theory, and the linear matrix inequality technique, a novel stability criterion is developed for the LFC of the power system by considering the sampling period, and transmission delay of the communication network, which ensures that the proposed scheme operates in large sampling periods, and under transmission delays. Next, an exponential decay rate (EDR) is introduced to guide the design of a robust PI-based LFC scheme. The LFC scheme with robustness is designed by setting a small EDR. The values of EDR are adjusted by the given robust performance evaluation conditions of parameter uncertainties, and$H_\infty$performance. Finally, case studies are carried out based on a one-area power system, and a three-area power system with RESs. Simulation results show that the proposed LFC scheme performs strong robustness against parameter uncertainties regarding the power system, and communication network, load fluctuations, and the intermittent generation of RESs.

Solar forecasting is one of the most promising approaches to address the intermittent photovoltaic (PV) power generation by providing predictions before upcoming ramp events. In this article, a novel multistep forecasting (MSF) scheme is proposed for PV power ramp-rate control (PRRC). This method utilizes an ensemble of deep ConvNets without additional time series models (e.g., recurrent neural network (RNN) or long short-term memory) and exogenous variables, thus more suitable for industrial applications. The MSF strategy can make multiple predictions in comparison with a single forecasting point produced by a conventional method while maintaining the same high temporal resolution. Besides, stacked sky images that integrate temporal-spatial information of cloud motions are used to further improve the forecasting performance. The results demonstrate a favorable forecasting accuracy in comparison to the existing forecasting models with the highest skill score of 17.7%. In the PRRC application, the MSF-based PRRC can detect more ramp-rates violations with a higher control rate of 98.9% compared with the conventional forecasting-based control. Thus, the PV generation can be effectively smoothed with less energy curtailment on both clear and cloudy days using the proposed approach.

This paper is concerned with the delay-dependent stability analysis of linear systems with a time-varying delay. Two types of improved Lyapunov-Krasovskii functionals (LKFs) are developed to derive less conservative stability criteria. First, a new delay-product-type LKF, including single integral terms with time-varying delays as coefficients is developed, and two stability criteria with less conservatism due to more delay information included are established for different allowable delay sets. Second, the delay-product-type LKF is further improved by introducing several negative definite quadratic terms based on the idea of matrix-refined-function-based LKF, and two stability criteria with more cross-term information and less conservatism for different allowable delay sets are also obtained. Finally, a numerical example is utilized to verify the effectiveness of the proposed methods.

With the increasing application of railway transportation, energy consumption of railway transportation rises dramatically, which in turn undermines its sustainability. Optimization on train speed profile and use of regenerative energy is becoming a feasible and applicable approach to achieve an energy-efficient operation without changing existing infrastructures. Considering both dwelling at stations and running in the inter-station sections, the paper proposes an integrated optimization model for reducing net energy consumption from the viewpoint of energy interaction among train, substation and on-board energy storage device (ESD), based on which the optimal train speed profile is also found. The discharge/charge strategy of on-board ESD is explored and comparative case studies are given, under the assumption of higher efficiency from the on-board ESD, less net energy consumption can be achieved via energy interaction with substation. Through charging from substation the net energy consumption can be reduced, i.e. 0.2%, in the comparison.

In this paper, the distributionally robust optimization approach (DROA) is proposed to schedule the energy consumption of the heating, ventilation and air conditioning (HVAC) system with consideration of the weather forecast error. The maximum interval of the outdoor temperature is partitioned into subintervals, and the proposed DROA constructs the ambiguity set of the probability distribution of the outdoor temperature based on the probabilistic information of these subintervals of historical weather data. The actual energy consumption will be adjusted according to the forecast error and the scheduled consumption in real time. The energy consumption scheduling of HVAC through the proposed DROA is formulated as a nonlinear problem with distributionally robust chance constraints. These constraints are reformulated to be linear and then the problem is solved via linear programming. Compared with the method that takes into account the weather forecast error based on the mean and the variance of historical data, simulation results demonstrate that the proposed DROA effectively reduces the electricity cost with less computation time, and the electricity cost is reduced compared with the traditional robust method.

This paper proposes distributionally robust energy-reserve-storage co-dispatch model and method to facilitate the integration of variable and uncertain renewable energy. The uncertainties of renewable generation forecasting errors are characterized through an ambiguity set, which is a set of probability distributions consistent with observed historical data. The proposed model minimizes the expected operation costs corresponding to the worst case distribution in the ambiguity set. Distributionally robust chance constraints are employed to guarantee reserve and transmission adequacy. The more historical data are available, the smaller the ambiguity set is and the less conservative the solution is. The formulation is finally cast into a mixed integer linear programming whose scale remains unchanged as the number of historical data increases. Inactive constraint identification and convex relaxation techniques are introduced to reduce the computational burden. Numerical results and Monte Carlo simulations on IEEE 118-bus systems demonstrate the effectiveness and efficiency of the proposed method.

As a promising mobility tool in future transportation systems, Electric Vehicle (EV) has environment-friendly benefits compared with traditional internal-combustion-engine vehicle. However, uncoordinated charging of mass EVs bring huge burden to power grids. To tackle this problem, a coordinated charging strategy of EVs is necessary. EV aggregator could play as a coordinator between EV owner and power grids, both meeting owner driving requirements and power grids operation requirements. However, a owner-aggregator economic inconsistency issue appears, that is EV owner get a higher charging cost in aggregator scheduling than self scheduling. In order to mediate owner-aggregator economic inconsistency issue, this paper designed a centralized two-stage EVs charging/discharging scheduling strategy in a residential community within 24 hours from the viewpoint of two stakeholders: EV owners (to minimize each EV owner charging cost) and aggregator (to maximize aggregator revenue). In the first-stage, EVs operation are scheduled from EV owners viewpoint, to obtain the minimal charging cost for each EV owner. Then, in the second-stage, the scheduling results in the first-stage are involved as constraints. The objective in the second-stage is to maximize aggregator revenue, without sacrificing each EV owner's economic benefit (no charging cost increment). A rebate factor is introduced in this model, which is the pay back for each EV owner provided by aggregator. Case study shows the effectiveness of the proposed scheduling strategy: the aggregator revenue is maximized without sacrificing each EV owner's economic benefit so that owner-aggregator economic inconsistency issue is mediated. The impact parameter of rebate factor in aggregator revenue in analyzed.

Battery energy storage (BES) and distributed generation (DG) play an important role in active distribution networks. However, harmonic problems could be caused by the inverters in BES and DG, resulting in a poor power quality (PQ). In addition, ensuring acceptable voltage unbalance level is also another crucial PQ issue in distribution networks. Therefore, in this paper, an optimal distribution network operational model (ODNOM) is proposed to consider PQ problems caused by BES and DG. In this model, additional network power losses due to harmonics are considered into the objective function, and the harmonic constraints and voltage unbalance constraints are also taken into account. The particle swarm optimization is used to solve the proposed ODNOM. The simulations on the IEEE 13-bus, IEEE 37-bus, and IEEE 123-bus systems show that a satisfactory PQ can be achieved by the proposed approach while optimizing the total branch active power losses. This paper aims to obtain a satisfactory PQ level while minimizing the total branch active power losses in the day-ahead dispatch of active distribution networks, since inverters-based BES and DG can inject harmonic pollution into distribution networks and voltage unbalance level is also an important issue for supplying good quality electricity to end users. To achieve such an objective, PQ constraints such as the total voltage harmonic distortion (THD) constraints, the individual voltage distortion constraints, and the voltage unbalance factor constraints are considered into the day-ahead scheduling. In addition, the network active power losses of the concerned harmonic frequencies are also considered into the objective function. The simulation results of the unbalanced IEEE 13-bus, IEEE 37-bus, and IEEE 123-bus systems show that the proposed approach can give a satisfactory day-ahead schedule of good quality power for active distribution networks. Also, the case studies indicate that total network power losses and the satisfactory PQ are the two contradictory objectives. The proposed approach can be applied to ensure a satisfactory PQ when making a dispatch plan in distribution networks with inverter-based BES and DG.

This paper is concerned with the stability analysis of discrete-time neural networks with a time-varying delay. Assessment of the effect of time delays on system stability requires suitable delay-dependent stability criteria. This paper aims to develop new stability criteria for reduction of conservatism without much increase of computational burden. An extended reciprocally convex matrix inequality is developed to replace the popular reciprocally convex combination lemma (RCCL). It has potential to reduce the conservatism of the RCCL-based criteria without introducing any extra decision variable due to its advantage of reduced estimation gap using the same decision variables. Moreover, a delay-product-type term is introduced for the first time into the Lyapunov function candidate such that a delay-variation-dependent stability criterion with the bounds of delay change rate is established. Finally, the advantages of the proposed criteria are demonstrated through two numerical examples.

This paper introduces an isolated high voltage gain DC-DC converter, which can achieve high output voltage and large power level with hybrid connection of modular active-clamping current-fed push-pull(CFPP) converters. Thanks to the modularity, the voltage stress of semiconductor devices and the power rating of magnetic components are low. In addition, zero-voltage-switching operation is achieved for all switches and thus low EMI noise can be obtained. Furthermore, the secondary current of different CFPP converters in the same column is auto-balanced, contributing to reduced control complexity. With redundant rows and columns, the converter can remain normal operation under fault state. In the paper, the operation and analysis of the converter are illustrated in detail and the simulation results are demonstrated to verify its advantages.

This paper investigates delay-dependent stability for continuous neural networks with a time-varying delay. This paper aims at deriving a new stability criterion, considering tradeoff between conservativeness and calculation complexity. A new Lyapunov-Krasovskii functional with simple augmented terms and delay-dependent terms is constructed, and its derivative is estimated by several techniques, including free-weighting matrix and inequality estimation methods. Then, the influence of the techniques used on the conservativeness and the complexity is analyzed one by one. Moreover, useful guidelines for improving criterion and future work are briefly discussed. Finally, the advantages of the proposed criterion compared with the existing ones are verified based on three numerical examples.

A direct grid-current control method is proposed to attenuate low-order current harmonics in three-phase LCL-filtered grid-connected inverters. This method allows the use of a distorted reference current, thus the reference can be generated directly from grid voltage or by a simple phase-locked loop (PLL). It is found that the direct control is necessary for effectively attenuating the current harmonics caused by the distortion in the grid voltage. Active damping is achieved with an inner inverter-current feedback loop, which is found to be superior to the widely used capacitor-current feedback damping in improving system stability. A systematic controller design procedure is proposed to optimize system performance. Experimental results confirm the improved harmonic attenuation ability of the proposed method in comparison to that of conventional control methods.

This paper investigates the delay-dependent stability for generalized continuous neural networks with time-varying delays. A novel Lyapunov-Krasovskii functional (LKF) that considers more information on activation functions of delayed neural networks and delay upper bounds is developed. Simultaneously, most commonly used techniques for treating the derivative of the LKF are reviewed and compared with each other. With the way of introducing slack matrices, those techniques are classified into two categories, including free-weighting matrix (FWM)-based techniques and reciprocally convex combination-based techniques. It is found that the introduced slack matrices play an important role in conservatism reducing and those four types of FWM-based methods lead to same results and are equivalent. Moreover, the obtained criteria are extended to the system with a single time-varying delay. Two numerical examples are given to verify the effectiveness of the proposed method.