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• Distinct DG Output Modes Validation: The simulation validates distinct DG output modes, impacting prediction. • Mode-Based State Estimation: The paper proposes an innovative mode-based state estimation using IPSO-DBSCAN clustering, overcoming k-means limitations. • Enhanced Data Recovery using DBSCAN: Enhanced data recovery employs DBSCAN with improved secondary clustering, guided by similarity and homogeneity, outperforming traditional methods. • BiGRU-PF State Estimation: BiGRU-PF enhances state estimation accuracy and robustness in distribution networks by detecting DG output modes, addressing k-means limitations, and improving data recovery. This paper proposes a new forecasting-aided state estimation (FASE) method for distribution systems that mitigates issues with uncertain distributed generation (DG) and lost measurements. We utilize an Improved Particle Swarm Optimization (IPSO)-optimized Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for examination of historical DG output data. Based on identified DG output modes, it facilitates precise state prediction and data reconstruction,. The proposed method employs a Bidirectional Gated Recurrent Unit (BiGRU) neural network for state prediction and a particle filter (PF) for final state filtering. The method verification is provided through Python simulations of the Distribution Transformer Unit (DTU)7k distribution network system, demonstrating improved accuracy and robustness against sudden load change and bad data in measurements. [ABSTRACT FROM AUTHOR]

• A two-stage risk dispatching model for integrated transmission and distribution networks is proposed. The day-ahead phase focuses on the economics of the transmission and distribution network, while the intra-day phase focuses on operational safety. • The four dimensions of regulable resources, such as power side, grid side, load side and energy storage side, are coordinated to optimally dispatch the transmission and distribution network. • An improved particle swarm (PSO) algorithm solution model is proposed. In this paper, the dynamic adjustment of the inertia weight factor and learning factor can improve the ability to globally search for optimal values and increase the convergence speed of the algorithm. With the incorporation of more renewable energy sources, distribution networks are shifting from passive to active. The interconnection of the transmission and distribution networks will increase and the tidal currents will become bidirectional, affecting the stable operation of the transmission and distribution networks. This paper proposes a multi-timescale risk dispatch study for transmission and distribution networks that have a high proportion of distributed energy access to meet the rising demand for coupling between the two types of networks. First, a day-ahead scheduling model that takes into account various operational risks is developed with the goal of minimizing total operating costs, and it optimizes the source network load and storage of multiple adjustable resources of the transmission network and distribution network jointly under the risk limit constraint. Second, an intraday scheduling model is built with the goal of minimizing the risk cost of the transmission network and distribution network, and the day-ahead scheduling plan is modified by considering the day-ahead forecast error and the different operational risks of the transmission network and distribution network. Finally, the effectiveness of the proposed multi-time-scale risk dispatch model is verified through the analysis of arithmetic cases, and it is proved that the model can enable the interaction and coordination between transmission and distribution networks as well as the synergistic optimization of operating costs and operating risks. [ABSTRACT FROM AUTHOR]

• The problem of potential changes in propagation paths generated by different prevention strategies is addressed. • A multi-stage coordination model is proposed for reducing cascading failure impacts. • A double-layer optimization framework is proposed for solving the multi-stage coordination dispatch model. Blackout events caused by cascading failures have induced enormous losses in power systems. Good defense strategies are helpful for stopping cascading failures spread, which not only reduces system losses, but also improves the system's ability to cope with the cascading failures. However, the current studies mostly focus on single-stage defense strategy and lack coordination of preventive, emergency and restoration dispatch (CPERD) against cascading failures. This paper proposes a CPERD methodology to counteract cascading failures for resilience enhancement. We first analyze the propagation mechanism and the prediction method for the propagation paths of cascading failures. Second, we explore the necessity and feasibility of whole-process coordinated dispatch under cascading failures and design a CPERD model framework. Based on this framework, we further analyze the impact of whole-process coordinated dispatch on the propagation path of cascading failures. Third, preventive dispatch, emergency dispatch and restoration dispatch sub-models are separately modeled to minimize the dispatch cost. Since the model has the characteristics of multi-stage and multi-objective, a double-layer optimization framework and an improved particle swarm optimization algorithm are introduced to solve the CPERD problem. Finally, the IEEE 39-bus and IEEE 118-bus test systems are taken as examples for simulation analysis. The results show that whole-process coordinated dispatch can effectively reduce the final load loss and overall dispatch cost, and enhance power system resilience against cascading failures. [ABSTRACT FROM AUTHOR]

• Developed cost model for railway decarbonization upgrade options, including operating, rolling stock upgrade, infrastructure construction, and maintenance support costs. • An adaptive particle swarm optimization is used to find the minimum cost of three decarbonization options. • The experiment determined the traffic density range in which hydrogen fuel cell trains, battery trains, and electrified trains have cost advantages over various distances. • Rule-based state machine energy management strategies are used to find the appropriate power allocation scheme for hydrogen fuel cell trains, considering the energy conversion efficiency of hydrogen fuel cells and the state of charge of the batteries. • The main defects of hydrogen fuel cell trains and battery trains were analyzed, providing insights for guiding their future commercial development. The UK government plans to phase out pure diesel trains by 2040 and fully decarbonize railways by 2050. Hydrogen fuel cell (HFC) trains, electrified trains using pantographs (Electrified Trains), and battery electric multiple unit (BEMU) trains are considered the main solutions for decarbonizing railways. However, the range of these decarbonization options' line upgrade cost advantages is unclear. This paper analyzes the upgrade costs of three types of trains on different lines by constructing a cost model and using particle swarm optimization (PSO), including operating costs and fixed investment costs. For the case of decarbonization of the London St. Pancras to Leicester line, the electrified train option is more cost-effective than the other two options under the condition that the service period is 30 years. Then the traffic density range in which three new energy trains have cost advantages on different line lengths is calculated. For route distances under 100 km and with a traffic density of less than 52 trips/day, BEMU trains have the lowest average cost, while electrified trains are the most cost-effective in other ranges. For route distances over 100 km, the average cost of HFC trains is lower than that of electrified trains at traffic densities below about 45 trips/day. In addition, if hydrogen prices fall by 26 %, the cost advantage range of HFC trains will increase to 70 trips per day. For route distances under 100 km, BEMU trains still maintain their advantages in terms of lower traffic density. [ABSTRACT FROM AUTHOR]

• A pricing method of Time-of-Use transmission price is proposed based on ATC curve. • A market allocation model of power generation is built based on Prospect Theory. • A bi-level model is solved by combining heuristic algorithms with Cplex tool. • The proposed transmission price promotes the utilization for certain project. The electricity delivered by inter-regional line projects is impacted by the operation of spot market, the previously approved fixed transmission pricing method based on planned electricity may not be fully applicable. To solve the above problem, the Time-of-Use transmission pricing method for inter-regional line with spot market at both ends is proposed in this paper. Firstly, it analyses the impact of transmission price on the electricity distribution of the power producers in the spot market at both ends, and the clearing price and total power generation cost in the spot market at the receiving-end. Then, the calculation method of daily available transmission capacity is introduced. Further, based on the incentive of transmission price, a Bi-Level optimization model is established by considering spot market clearing at both ends and the cost recovery of inter-regional line. On the transmission side, the optimal model of transmission price to maximize the benefits of the inter-regional line is built. On the spot market side, consider transmission prices, the power distribution model in the sending-end market and a unit commitment optimization model in the receiving-end market are established, and the relationship between transmission price and transmission electricity is found for the inter-regional line. The two models are interacted and restricted by each other, so a Bi-Level model of the cooperation between the inter-regional line and the spot market is formed. Finally, an example is simulated based on the Particle swarm optimization algorithm and Cplex optimization tool, and the effectiveness of the method is verified. © 2017 Elsevier Inc. All rights reserved. [ABSTRACT FROM AUTHOR]

• A two-step scheme is proposed coordinating the distribution grid, aggregator, and EV. • Temporal-spatial features are exploited to achieve EV charging orderly. • Modified multi-objective particle swarm optimization algorithm is proposed. • Load variance and aggregator revenue are improved when meeting charging demands. • A higher proportion of private charging facilities enhance comprehensive benefits. Burdens of increasing penetration of electric vehicles (EVs) on distribution systems have attracted wide attention to the research of EV charging coordination. Nevertheless, existing coordination lacks the joint consideration of the temporal-spatial flexibility of EVs, which could reduce load fluctuations when meeting the EV charging demands. Moreover, a thorough investigation is required for the coordination of the benefits to the distribution system operator (DSO), EV aggregator (EVA), and EVs. In this paper, a novel EV charging scheme considering the temporal-spatial features of EVs is proposed, achieving the coordination of DSO, EVA, and EVs in two steps. First, a mixed-integer second-order cone programming (MISOCP)-based EV charging schedule is developed for DSO. Second, the temporal-spatial features of EVs are exploited by EVA to track the charging schedule. Specifically, a dynamic charging price mechanism based on active power margin is proposed for public charging facilities and a specific load regulation is designed for private charging facilities. On this basis, the modified adaptive multi-objective particle swarm optimization (AMOPSO) algorithm is proposed, including adaptive flight parameter adjustment and termination mechanisms. Case studies demonstrate the proposed strategy can attenuate load variance and raise EVA revenue. Further, the impact analysis of penalty price and the price elasticity of electricity demand can provide references for stable distribution network operation, higher EVA revenue, and charging cost reduction. [ABSTRACT FROM AUTHOR]

• A combination model for controlling APD-RPD of TGS was designed based on controlling the generator frequency and its terminal voltage simultaneously. • Mathematical model derivation proposed for tracking the RPD and preserving the APD of TGS at the infinite bus. • Creation of control gain parameters (G P 1 , G P 2 , G P 3 and G P 4 ) to meet the APD and RPD based mathematical model. • Performance assessment of proposed system compared with the relevant previous works. This paper presents a new control strategy for tracking the reactive power demand (RPD) from the turbo generator system at the infinite bus, while maintaining constant active power demand (APD). The positive and negative disturbances of RPD are applied by ±10 % of 0.6 pu while APD is applied at 0.8 pu. The tracking of RPD and APD depends on a new mathematical model to control simultaneously both of the governor valve position and the exciter field voltage by four gain parameters (G P 1 , G P 2 , G P 3 and G P 4 ). These parameters will be found applying the bus admittance matrix model. To improve the system dynamics, 10th parameters of two fractional order PID (FOPID) controllers are optimized (OFOPID) by particle swarm optimization (PSO). The simulations revealed that the settling time, rise time, overshoot and undershoot could be reduced to a smaller value than the recently published researches. The voltage level has been regulated at the generator terminals by ±1.3 % while its settling time is 0.15 s and preserving the generator frequency at the nominal value with an overshoot of ±0.0046 Hz. So, the proposed method is significantly improved and highly effective as compared to previous related works. [ABSTRACT FROM AUTHOR]

• To propose sooty-tern optimization (STO) for designing PSS parameters of multi-machine power system. • The performance of STO is evaluated using eigenvalue analysis, simulation results and performance indices. • The results are matched with other powerful optimization techniques. • Robustness is verified on hidden operating cases also under critical disturbances. This paper reports a comparative study among four bio-inspired meta-heuristic techniques i.e. Sooty-Tern Optimization (STO), Grey Wolf Optimization (GWO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) to tune the robust Power System Stabilizer (PSS) parameters of the multi-machine power system. These approaches are successfully tested on two bench-mark systems: sixteen-machine, sixty-eight-bus New England Extended Power Grid (NEEPG) and three-machine, nine-bus Western System Coordinating Council (WSCC). The efficacy of planned PSS via STO and GWO is validated by extensive non-linear simulations, eigenvalue analysis, and performance indices for numerous operating conditions under decisive perturbations, and outcomes are matched with those of GA and PSO techniques. In addition, the robustness is also tested for these algorithms. The results indicate that the PSS design using STO and GWO improves the small-signal stability and damping performance for mitigating inter-area and local area modes of low-frequency oscillations compared to GA and PSO. [ABSTRACT FROM AUTHOR]

• A virtual damping stabilizer for BESS inertia capability enhancement is proposed. • Frequency nadir, zenith, and oscillations are mitigated, and damping is improved. • BESS size is reduced, and lifetime is maintained by smoothing the power profile. • Verified via real-time experiment, sensitivity analysis, and small signal stability. • The balance between frequency stability and BESS initial size and cost is achieved. The Inverter-based generator lacks natural inertia, which leads to instability problems in Microgrid frequency. Virtual inertia is a promising control concept that rapidly injects/absorbs active power to/from the power system to ensure the frequency stability of low-inertia systems. This paper proposes an innovative virtual damping stabilizer (VDS) integrated into battery energy storage system (BESS) based virtual inertia and damping control loops. The proposed VDS adds supplementary damping and stabilizing capability to the BESS by properly correcting and controlling its inertia pumped/drawn power. The VDS aims to enhance frequency stability, decrease the necessary BESS large capacity, and lower the initial expense. Furthermore, the proposed VDS helps operate the BESS efficiently by smoothing the output power profile during transient disturbances, consequently elongating the lifetime. The Particle Swarm Optimization (PSO) method is utilized to get the optimal parameters of the proposed VDS. A frequency control model of an isolated microgrid with the proposed controller is presented, considering various operating conditions. Time-domain simulations and experimental validation using OPAL-RT verify the efficacy of the proposed VDS scheme. Sensitivity and eigenvalues analyses assess the VDS's dynamic stability enhancement and robustness against inherent uncertainties in Microgrid parameters. The results demonstrate a superior frequency response and a reduction in the BESS size compared to PSO and model predictive controller (MPC) based virtual inertia and damping strategies. [ABSTRACT FROM AUTHOR]

• Growing Importance of Power Quality (PQ) Issues. • Simultaneous Allocation of Capacitors and Active Power Filters. • Synergistic Approach for Harmonic Pollution Reduction. • Application of a Modified Particle Swarm Optimization Algorithm. • Enhanced Power Quality and Harmonic Mitigation. • Optimization of Network Losses and Voltage Profile. The importance of Power Quality (PQ) issues in modern power systems has been growing in the last years. Capacitors are employed for optimizing network losses and increasing the voltage profile. However, in harmonic polluted networks, the placement of capacitors at their economic optimal locations may not be feasible due to harmonic constraints. Under such conditions, harmonic filters can mitigate harmonic pollution and enable capacitors to be optimally placed. This paper presents a novel approach for simultaneously optimizing the allocation of Active Power Filters (APFs) and capacitors, to improve the harmonic condition, network losses, and voltage profile of distribution networks. The method models APFs as harmonic sources in the Harmonic Power Flow (HPF) procedure, while capacitors are modelled as their corresponding harmonic admittance. Furthermore, a modified Particle Swarm Optimization (PSO) algorithm is presented as the optimization tool. Three case studies were conducted on the IEEE 18-bus test system. In the first study, optimal capacitor placement was performed with no regard to harmonic constraints, reducing network losses by 326 kW. The harmonic limits were then considered and satisfied by optimal APF placement. The total cost resulted in $241,983. The second study considered harmonic limits in the procedure of optimal capacitor placement, improving voltage profiles and network losses, but exceeding some harmonic constraints, which were subsequently satisfied by APF placement. The solution yielded the total cost of $264,942. The third study introduced simultaneous allocation of capacitors and APFs, proving to be the most cost-effective strategy ($225,417 total cost), promising enhanced network performance and efficiency, and adding valuable insights for future power system optimization. [ABSTRACT FROM AUTHOR]

Multi-region combined heat and power economic dispatch (MRCHPED) is a brand new optimization issue in power system operation. The MRCHPED uses the multiple regional structure to integrate power-only units, CHP units and heat-only units to achieve optimal economic dispatch while ensuring the supply of heat and power within each region. In this paper, an educational achievement guided group teaching optimization algorithm (EAGTOA) is developed and applied to solve the MRCHPED problem. In EAGTOA, aiming to improve the search efficiency, two historical archives are introduced to collect the valuable information of educational achievements from students and teachers, and are used in the update process of the EAGTOA. The EAGTOA algorithm is applied to solve three MRCHPED problems, including a small-scale MRCHPED with 2 regions and 9 units, a medium-scale MRCHPED with 3 regions and 17 units, and a large-scale MRCHPED with 3 regions and 96 units. The performance of EAGTOA is compared with several classical and recently-developed optimization algorithms. The simulation results show that the proposed EAGTOA can achieve better results in terms of solution accuracy and convergence speed for the MRCHPED problems. • Mathematical model of multi-region CHP economic dispatch (MRCHPED) is provided. • New group teaching optimization algorithm called EAGTOA is developed. • The EAGTOA is utilized to solve three MRCHPED with 9,17 and 96 units. • Simulation results show that EAGTOA achieves excellent results for the MRCHPED. [ABSTRACT FROM AUTHOR]

• Proposing a hierarchical battery sizing approach to improve dynamic performance of MG. • Optimal sizing of BESS based on stochastic models to minimize frequency fluctuations. • Secondary BESS optimal sizing and placement to improve static and dynamic stability. • Proposing a dynamic objective function based on system eigenvalues. Integration of energy storage systems (ESSs) to microgrids (MGs) due to the growing penetration of intermittent energy resources is a great motivation to develop studies in the context of optimal sizing and placement of these systems. In this paper, a multi-objective bi-level optimization problem is addressed based on the stochastic nature and dynamic model of power system elements. As a preliminary step, the optimal size and capacity of the battery energy storage system (BESS) are determined to minimize frequency fluctuations. This level has been performed based on three approaches considering the daily intra-hour stochastic data. In the secondary level, the dynamic behaviour of all MG elements has been considered and correspondingly a dynamic objective function based on system eigenvalues and voltage sensitivity index (VSI) has been developed. According to the proposed objective function, the secondary step for sizing and placement of the battery units has been performed by adaptive particularly tuneable fuzzy particle swarm optimization (APT-FPSO) algorithm to reduce power losses, improve the voltage profile/ stability, and mitigate low-frequency fluctuations. Finally, the effectiveness of the proposed approach is evaluated using time-domain simulations, voltage sensitivity studies, and eigenvalue analysis. [ABSTRACT FROM AUTHOR]

In this paper, a projection-based distributed algorithm is proposed to solve the economic dispatch problem of power system with communication delays under switching topologies. The algorithm is suitable for economic dispatch problem with strongly convex cost function, and ensures that the output power of each generator is always kept within its output limit. In addition, the topology is only required to be jointly connected. Firstly, the relationship between the equilibrium point and the optimal solution of the algorithm is analyzed, and then a common Lyapunov function is introduced for a set of switching topologies. According to the Lyapunov stability theory and Cauchy convergence criterion, LMIs-based sufficient conditions for the convergence of the algorithm are obtained. Finally, the effectiveness of the algorithm under different cases are verified by MATLAB simulation results. • The economic dispatch under switching topology with communication delay is studied. • A projection-based distributed algorithm with is proposed to solve the problem. • The communication topology is only required to be jointly connected. • The delay-dependent LMI conditions for the convergence of the algorithm are obtained. • The effects of parameters on the delay bound and convergence rate are analyzed. [ABSTRACT FROM AUTHOR]

• A method of period partitioning based on the silhouette coefficient is proposed. • A TOU price optimization model considering benefits and reliability is established. • A multi-objective simulated annealing particle swarm optimization algorithm is developed. • A bi-level TOU price optimization model for high reliability demand system is presented. The reliability of power system will vary with different demand response strategies, and it is significant to make a decision on demand response for satisfying differential reliability demand of power system. First, a peak-valley period partition model with an objective of maximizing silhouette coefficient was established based on the fuzzy clustering and step-wise iteration technology. Second, this paper proposed a multi-objective time of use electricity price optimization model considering the interests of both supply and demand sides, as well as reliability demand, and a multi-objective simulated annealing particle swarm optimization algorithm was developed to obtain solution. Third, the Pareto front curve of the dual-objective function was fitted by using a third-order Hermite interpolation algorithm, and the time of use prices corresponding to the reliability demand was obtained by using the back propagation neural network algorithm. Finally, the proposed model and algorithm were verified by the RBTS system, and the results indicate good rationality and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

• Taking the minimization of annualized cost as the objective function, as well as introducing environmental penalty cost, a bi-level optimal configuration model of integrated energy station is established to realize the collaborative optimization in the planning and operation stages. • Two different programming methods, the column-and-constraint generation (C&CG) and particle swarm optimization coordination with mixed-linear integer programming (PSO-MILP), are proposed to solve the model. The comparison and analysis of the programming results are illustrated in solving the two-stage robust planning problem. • Three operation modes of self-adaption, FEL and FTL are comprehensively considered to optimize the configuration of integrated energy station. On this basis, the sensitivity of heat-to-electric ratio (HPR) of CHP units and electric storage to the planning results are analyzed. • The planning results of integrated energy station are evaluated based on system dynamics (SD), which has certain guidance for the actual project. Operation modes of combined heat and power (CHP) units are closely related to the economic benefits of energy application in integrated energy station. In this paper, a novel bi-level optimal configuration method is proposed considering different operation modes of CHP, and a life-cycle assessment system on economic and environmental benefits is constructed using system dynamics (SD) method. Firstly, the basic structure and operation issues of a typical integrated energy station employing CHP are addressed. Secondly, the bi-level collaborative optimization model of the integrated energy station is constructed to minimize the annualized cost considering three operating modes, namely following the thermal load (FEL), following the electric load (FTL) and switching adaptively between FEL and FTL. Then, the model is solved by the column-and-constraint generation and particle swarm optimization coordination with mixed-linear integer programming respectively. Subsequently, the sensitivity of CHP units heat-to-electric ratio and electric storage to the planning results are analyzed in detail. Finally, case studies are carried out based on an actual integrated energy station. The optimization results show that, compared with FEL and FTL operating modes of the CHP units, the annualized cost of the system under adaptive operation mode is reduced by 26.5% and 42.8%, respectively. Besides, the assessment of the optimized system indicates that the total profit and carbon dioxide emission reduction of the station during its life-cycle are expected to reach 28.53 million CNY and 25.33 ten thousand tons respectively, which provides a reference for decision-making and a basis for flexible operation. [ABSTRACT FROM AUTHOR]