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In future power systems, various generation units are expected to contribute to frequency stability. Depending on their characteristics, these units will support the frequency stability in diverse ways. Traditionally, power systems have relied on synchronous generators (SGs), ensuring sufficient inertia and providing ancillary services for frequency stability. However, with the integration of multiple wind power plants (WPPs), which have substantially different characteristics, many countries are restructuring their ancillary service markets to accommodate WPPs.This study proposes a novel frequency stability contribution rate index to analyze and compare the contribution of SGs and WPPs to frequency stability. To do so, this paper first analyzes the factors from SGs and WPPs, such as inertia, power reserves, resource conditions, and available frequency responses, and compares their roles in supporting frequency stability. The proposed index is then calculated using a multiple linear regression model that incorporates these factors. Therefore, this index provides a comprehensive evaluation of SGs and WPPs in terms of their contribution to frequency stability, offering valuable insights into their roles in supporting three key frequency stability indicators, the rate of change of frequency, frequency nadir, and settling frequency. The effectiveness of the proposed index is verified through case studies on the IEEE 39-bus system. In all cases, the coefficient of determination exceeds 95%, indicating a highly predictive regression model. Furthermore, as the penetration level of WPPs rises from 20% to 60%, the contribution weight of factors related to WPPs increases while that of SGs decreases.

The rail transit system plays a crucial role in modern transportation. With the increasing demand for clean and green energy in the transport sector, its energy system is expected to achieve low-carbon and highly efficient energy utilization in rail transit. However, the gradual development of the rail transport energy system has led to an increase in its complexity, and the rising difficulty of system assessment has faced the limitations of traditional assessment methods. Hence, it is essential to develop effective assessment methods. This paper begins by providing a systematic review of the development status of Reliability, Availability, Maintainability and Safety (RAMS) assessment and analyzing the shortcomings of traditional RAMS assessment technology in the context of rail transit energy systems. Subsequently, based on the four fundamental properties of RAMS, it summarizes the current state of key assessment technologies in the field of rail transit. Moreover, the paper delves into the challenges and potential solutions concerning the implementation of RAMS assessment technology for rail transit energy systems. Finally, the paper offers an outlook on the future development of RAMS assessment for rail transport energy systems. By comprehensively analyzing these aspects, the paper aims to contribute valuable insights into optimizing the rail transit energy system, promoting its sustainable and efficient operation in the context of clean and green energy utilization.

The three-dimensional (3D) path planning problem of an Unmanned Aerial Vehicle (UAV) considering the effect of environmental wind in a dense city is investigated in this paper. The mission of the UAV is to fly from its initial position to its destination while ensuring safe flight. The dense obstacle avoidance and the energy consumption in 3D space need to be considered during the mission, which are often ignored in common studies. To solve these problems, an improved Deep Reinforcement Learning (DRL) path planning algorithm based on Double Deep Q-Network (DDQN) is proposed in this paper. Among the algorithms, the random obstacle training method is first proposed to make the algorithm consider various flight scenarios more globally and comprehensively and improve the algorithm’s robustness and adaptability. Then, the linear soft update strategy is employed to realize the smooth neural network parameter update, which enhances the stability and convergence of the training. In addition, the wind disturbances are integrated into the energy consumption model and reward function, which can effectively describe the wind disturbances during the UAV mission to achieve the minimum drag flight. To prevent the neural network from interfering with training failures, the meritocracy mechanism is proposed to enhance the algorithm’s stability. The effectiveness and applicability of the proposed method are verified through simulation analysis and comparative studies. The UAV based on this algorithm has good autonomy and adaptability, which provides a new way to solve the UAV path planning problem in dense urban scenes.

Quickly recognizing the real-time operating states will be helpful to identify the instantaneous and permanent power loss of the renewable energy station, so as to realize the continuous operation under the influence of the instantaneous disturbances caused by faults. This paper proposes a state recognition method for renewable energy units based on sparse stacked auto-encoder (SSAE) feature extraction and improved k-nearest neighbor (KNN) algorithm. The characteristics of this method is that the electrical parameters of the unit port are collected directly without relying on the unit’s supervisory control and data acquisition (SCADA) system, whose acquisition speed is too slow to meet the recognition accuracy requirement, and that the unit operation states can be recognized quickly and accurately. Firstly, operation states of renewable energy unit are divided, and the framework for the unit’s state recognition is proposed. Moreover, improved strategies for state recognition of renewable energy unit are proposed. Finally, the power system analysis software package (PSASP) is used to obtain the electrical parameters of renewable energy units and the improved KNN algorithm is used to recognize operation states after extracting features based on SSAE. By comparing the method proposed with the traditional KNN algorithm, the effect of the proposed method for states recognition is shown to be the best, with an accuracy of 98.16% and computing time of 50ms. The results show the validity of the proposed method.

In this work, a robust subspace clustering algorithm is developed to exploit the inherent union-of-subspaces structure in the data for reconstructing missing measurements and detecting anomalies. Our focus is on processing an incessant stream of large-scale data such as synchronized phasor measurements in the power grid, which is challenging due to computational complexity, memory requirement, and missing and corrupt observations. In order to mitigate these issues, a low-rank representation (LRR) model-based subspace clustering problem is formulated that can handle missing measurements and sparse outliers in the data. Then, an efficient online algorithm is derived based on stochastic approximation. The convergence property of the algorithm is established. Strategies to maintain a representative yet compact dictionary for capturing the subspace structure are also proposed. The developed method is tested on both simulated and real phasor measurement unit (PMU) data to verify the effectiveness and is shown to significantly outperform existing algorithms based on simple low-rank structure of data.

The gas turbine is a crucial piece of equipment in the energy and power industry. The exhaust gas has a sufficiently high temperature to be recovered for energy cascade use. The supercritical carbon dioxide (S-CO2) Brayton cycle is an advanced power system that offers benefits in terms of efficiency, volume, and flexibility. It may be utilized for waste heat recovery (WHR) in gas turbines. This study involved the design of a 5 MW S-CO2 recompression cycle specifically for the purpose of operational control. The dynamic models for the printed circuit heat exchangers, compressors, and turbines were developed. The stability and dynamic behavior of the components were validated. The suggested control strategies entail utilizing the cooling water controller to maintain the compressor inlet temperature above the critical temperature of CO2 (304.13 K). Additionally, the circulating mass flow rate is regulated to modify the output power, while the exhaust gas flow rate is controlled to ensure that the turbine inlet temperature remains within safe limits. The simulations compare the performance of PI controllers tuned using the SIMC rule and ADRC controllers tuned using the bandwidth method. The findings demonstrated that both controllers are capable of adjusting operating conditions and effectively suppressing fluctuations in the exhaust gas. The ADRC controllers exhibit a superior control performance, resulting in a 55% reduction in settling time under the load-tracking scenario.

The growing distributed energy resource (DER) penetration into distribution networks, such as through residential and commercial photovoltaics (PV), has emerged through a transition from passive to active networks, which takes the complexity of planning and operations to the next level. Optimal PV allocation (sizing and location) is challenging because it involves mixed-integer non-linear programming with three-phase non-linear unbalanced power flow equations. Meta-heuristic algorithms have proven their effectiveness in many complex engineering problems. Thus, in this study, we propose to achieve optimal PV allocation by using several basic evolutionary algorithms (EAs), particle swarm optimization (PSO), artificial bee colony (ABC), differential evolution (DE), and their variants, all of which are applied for a study of their performance levels. Two modified unbalanced IEEE test feeders (13 and 37 bus) are developed to evaluate these performance levels, with two objectives: one is to maximize PV penetration, and the other is to minimize the voltage deviation from 1.0 p.u. To handle the computational burden of the sequential power flow and unbalanced network, we adopt an efficient iterative load flow algorithm instead of the commonly used and yet highly simplified forward–backward sweep method. A comparative study of these basic EAs shows their general success in finding a near-optimal solution, except in the case of the DE, which is known for solving continuous optimization problems efficiently. From experiments run 30 times, it is observed that PSO-related algorithms are more efficient and robust in the maximum PV penetration case, while ABC-related algorithms are more efficient and robust in the minimum voltage deviation case.

Recently, the environmental problem has become a global issue. The air to fuel ratio (AFR) in the combustion of thermal power plants directly influences pollutants and thermal efficiency. A research result was published showing that the AFR control performance of thermal power plants can be improved through supplementary control using dynamic matrix control (DMC). However, online optimization of DMC needs an extra computer server in implementation. This paper proposes a practical AFR control with one-step ahead control which does not use online optimization and can be implemented directly in existing distributed control system (DCS) of thermal power plants. Closed-loop transfer function models at three operating points are independently developed offline. Then, an online transfer function using interpolation of offline models is applied at each sampling step. A simple one-step ahead control with online transfer function is applied as a supplementary control of AFR. Simulations with two different type power plants, a 600 MW oil-fired drum-type power plant and a 1000 MW ultra supercritical (USC) coal-fired once-through type power plant, are performed to show the effectiveness of the proposed control structure. Simulation results show that the proposed supplementary control can effectively improve the conventional AFR control performance of power plants.

Vehicle-to-grid (V2G) technology has received a lot of attention as a smart interconnection solution between electric vehicles and the grid. This paper analyzes the relevant research progress and hotpots of V2G by using CiteSpace 6.1.R6 software to construct a visualization graph, which includes keyword co-occurrence, clustering, and burstiness, and further systematically summarizes the main trends and key results of V2G research. First, the connection between electric vehicles and the grid is outlined and the potential advantages of V2G technology are emphasized, such as energy management, load balancing, and environmental sustainability. The important topics of V2G, including renewable energy consumption, power dispatch, regulation and optimization of the grid, and the smart grid, are discussed. This paper also emphasizes the positive impacts of V2G technologies on the grid, including reduced carbon emissions, improved grid reliability, and the support for renewable energy integration. Current and future challenges for V2G research, such as standardization, policy support, and business models, are also considered. This review provides a comprehensive perspective for scholars and practitioners in V2G research and contributes to a better understanding of the current status and future trends of V2G technology.

Since doubly-fed induction machine pumped storage hydro (DFIM-PSH) unit can adjust active power flexibly through adjustable-speed operation, it has frequency regulation capability in both generating and pumping modes. In order to explore the frequency regulation capability of DFIM-PSH unit under different working conditions, this paper develops a frequency control module for DFIM-PSH unit in pumping mode, which is quite different from that in generating mode, and then optimizes the frequency control parameters aimed at minimizing the frequency deviation in multiple operating conditions. Based on the dynamic model of the DFIM-PSH unit, the system frequency response model is built to analyze the influence of parameters on frequency dynamic characteristics. An optimization method of frequency control parameters is developed based on improved particle swarm optimization algorithm to maximize its frequency regulation capability under different operating conditions while ensuring safe and stable operation. Finally, a four-machine two-zone power system model with a DFIM-PSH unit is simulated, and the simulation results show that the proposed strategy can make the DFIM-PSH unit have great frequency regulation performance in a wide range of operating conditions.

The intermittence of renewable energy sources increases the importance of the effective load-tracking ability of power plants. Coordinated control between boiler and turbine systems is the uppermost layer of a thermal power plant control to follow the load demand. In this paper, a supplementary controller is proposed based on the One-Step Ahead strategy for coordinated control of thermal power plants. After a plant model is developed offline from a step response test, the optimized control of the One-Step Ahead strategy is applied to the boiler feed-forward (BFF) signal to control the electric power output and the main steam pressure simultaneously. Simulation with a 1000 MW ultra-supercritical (USC) once-through type power plant is performed. The results show that the error of Mega-Watt Output (MWO) was reduced to 78~95%, and settling time was reduced to 64~79% from conventional coordinated control by adding the proposed supplementary controller.

The frequency stability of interconnected power systems becomes quite challenging when incorporating renewable energy sources (mostly wind power). Distributed model predictive control (DMPC) is an effective method to maintain stable grid frequency and realize power system load frequency control (LFC). This paper proposes a two-layer robust DMPC for the LFC of an interconnected power system. In the scheme, the wind power penetrating the power grid is largely affected by the environment condition, and it is taken as an uncertain disturbance to the power system. The two-layer robust DMPC consists of a nominal DMPC controller and an ancillary DMPC controller. The nominal DMPCs coordinate with each other in achieving the systemwide LFC objective, where the systemwide objective is a strict convex combination of the local LFC objectives. The nominal optimization problems are solved supposing the wind power fluctuation is zero. The ancillary DMPC generates the actual control signal for each generation unit based on signals which are transmitted from the nominal DMPC controller. The simulation on a four-area interconnected power system demonstrates the effectiveness of the proposed algorithm in alleviating the frequency deviation caused by varying the load and uncertain wind power fluctuation.

This paper applies the elitist genetic algorithm to the electric vehicle routing problem with time window. In initialization, the paper proposes an improved neighbor routing initialization method for adaptive elitist genetic algorithm. The improved neighbor routing method is used to select the nearest EV customer as the next route to be scheduled and make the route start from the suitable first customer in the initialization of the elitist GA. It makes the scheduled route begins with a neighboring directionality, which can be inherited in selection, crossover, and mutation operations. For effective convergence, new adaptive crossover probability and mutation probability are provided to make the algorithm converge faster. Experimental studies on randomly distributed customers and Solomon benchmark cases show the effective performance of the algorithm. The algorithm is demonstrated in the simulation of a U.S. Postal Service system.

It is economic and secure to determine the optimal siting and sizing of the offshore wind farms (OWFs) integrated into the AC system through voltage-source converter high-voltage direct current (VSC-HVDC) links. In this paper, an integrated planning model for the VSC-HVDC-link-based OWFs and the capacitors is proposed, where a decomposition technique is presented to solve the proposed mixed-integer nonlinear programming (MINLP) problem and obtain the optimal solution. This model can optimize the siting and sizing of the OWFs to improve the voltage profile and reduce the adverse influence of the reactive power of the OWFs. With the proposed planning model, the total investment costs, operation costs and maintenance costs of the OWFs, VSC-HVDC links, and the capacitors can be minimized. Simulations on the modified IEEE 118-bus system show that the proposed integrated planning model can provide more economic scheme than the independent planning scheme, in which the capacitors are planned after the OWFs. Besides, a series of sensitivity analysis on certain equipment costs are studied to obtain the regular pattern for sizing VSC stations.

The depletion of existing resources and environmental problems led to an increase in distributed energy resources (DERs) and the accordingly emergence of the prosumer. This change has created an issue of power system operations and the coordinating entities of distribution networks. Besides, environmental concerns and the global policy of supporting renewable energy resources has given some electricity consumers a better perception and a willingness to pay (WTP) for green energy at a higher cost. This article presents a bidding-based peer-to-peer (P2P) energy transaction optimization model that considers these green energy preferences based on prosumer and consumer’s intention. The objective of the model, which deals with energy transactions between prosumers and consumers within the virtual energy community, is to minimize community operating costs taking into account the depreciation costs of energy storage units while maximizing the social welfare for encouraging the energy trading among consumers and prosumers. The weighted social-welfare terms in the objective function provides diverse community operations reflecting the goal of the energy trading operator (community operator). Case studies were conducted according to the presence and absence of energy transactions and energy storages and the green energy preference. Results of the case study showed that the benefits of prosumers and community have occurred in energy transactions and CO2 emission is reduced according to green energy preference.

There are many uncertain factors, which are critical for the efficient operation of steam power systems (SPSs). Due to the modelling inaccuracy and the changing environment, it is challenging to directly formulate the model for SPS operation. In this paper, a method is proposed to alleviate the influence of small-scale fluctuation by discretizing the uncertainty working condition on the SPS. Moreover, the exergy analysis method is proposed to evaluate the quality of different energy and develop a mixed-integer nonlinear model of SPS considering the economic and exergy objectives. Finally, the $\varepsilon $ -constraint method is used to obtain the Pareto front of the multi-objective optimization problem, and the fuzzy decision is used to determine the optimal strategy of the system. By introducing the multi-objective optimization of the SPS model with demand side uncertainty, a flexible scheduling scheme with good economic and exergy objectives is obtained. Comparing with the optimal scheduling without the demand-side uncertainty, numerical results indicate the feasibility and effectiveness of the model.

In the face of the pressing environmental issues, the past decade witnessed the booming development of the distributed energy systems (DESs). A notable problem of DESs is the inevitable uncertainty that may make DESs deviate significantly from the deterministically obtained expectations, in both aspects of optimal design and economic operation. It thus necessitates the sensitivity analysis to quantify the impacts of the massive parametric uncertainties. This paper aims to give a comprehensive quantification, and carries out a multi-stage sensitivity analysis on DESs from the perspectives of evaluation criteria, optimal design and economic operation. First, a mathematical model of a DES is developed to present the solutions to the three stages of the DES. Second, the Monte-Carlo simulation is carried out subject to the probabilistic distributions of the energy, technical and economic parameters. Based on the simulation results, the variance-based Sobol method is applied to calculate the individual importance, interactional importance and total importance of various parameters. The comparison of the multi-stage results shows that only a few parameters play critical roles while the uncertainty of most of the massive parameters has little impact on the system performance. In addition, the influence of parameter interactions in the optimal design stage are much stronger than that in the evaluation criteria and operation strategy stages.

Modern power systems consist of power electronics devices, which are used in renewable energy (RE) conversion. However, these devices, associated controllers, and uncertainty in RE output could bring new challenges to power system stability, especially oscillatory stability. Hence, the integration of battery energy storage systems (BESSs) is being developed to minimise the uncertainty and variability in renewables. Furthermore, to tackle the complex dynamics and inertia‐less characteristics of wind and PV plants additional controllers such as power oscillation damping (POD) control and virtual inertia scheme are sought. However, the primary challenges associated with the wide‐area oscillation damping controller are signal transmission delay, loss of communication signal, data drops, and others. This paper proposes a bat algorithm (BA) based resilient wide‐area multi‐mode controller (MMC) for enhancing oscillatory stability margin with high penetration of renewable power generations (RPGs) and BESSs. The Java 500 kV Indonesian grid is used to evaluate the performance of the resilient wide‐area MMC. From the results, it is found that the proposed controller effectively damp the critical mode of oscillation in the system even under communication failure as well as certain damping controller failures.

Nowadays, in researches on electric vehicle routing problems, in order to improve the delivery efficiency and reduce the routing cost, many important elements are broad discussed such as the customer time window, the routing algorithm, the electric energy consumption, etc. In these, the routing algorithm is the key element to achieve a good solution. Based on this background, the paper investigates the routing algorithm, then adopts the elitist genetic algorithm and proposes an improved neighbor routing initialization method for solving the electric vehicle routing problem. In our method, the electric vehicle energy consumption is used as the main component of the routing system. The neighbor routing initialization enables the routing system to choose the close route from a suitable first customer in the initialization, which makes the routing search faster and find the global optimal route easily. The simulations on the Solomon benchmark data and the Hiland Dairy milk delivery example in Dallas, Texas, USA verifies the good performance of the method.

Abstract This study aims to determine the improvement effect on the delay margin if fractional-order proportional integral (PI) controller is used in the control of a single-area delayed load frequency control (LFC) system. The delay margin of the system with fractional-order PI control has been obtained for various fractional integral orders and the effect of them has been shown on the delay margin as a third controller parameter. Furthermore, the stability of the system that is either under or over the delay margin is examined by generalized modified Mikhailov criterion. The stability results obtained have been confirmed numerically in time domain. It is demonstrated that the proposed controller for delayed LFC system provides more flexibility on delay margin according to integer-order PI controller.

The growth in renewable energy integration over the past few years, primarily fueled by the drop in capital cost, has revealed the requirement for more sustainable methods of integration. This paper presents a collocated hybrid plant consisting of solar photovoltaic (PV) and Ternary pumped-storage hydro (TPSH) and designs controls that integrate the PV plant such that the behavior and the controllability of the hybrid plant are similar to those of a conventional plant within operational constraints. The PV array control and hybrid plant control implement a neural–network-based framework to coordinate the response, de-loading, and curtailment of multiple arrays with the response of the TPSH. With the help of the designed controls, a symbiotic relationship is developed between the two energy resources, where the PV compensates for the TPSH nonlinearities and provides required speed of response, while the TPSH firms the PV system and allows the PV to be integrated using its existing infrastructure. Simulations demonstrate that the designed controls enable the PV system to track references, while the TPSH’s firming and shifting transforms the PV system into a base load plant for most of the day and extends its hours of operation.

In the parking lots of public commercial areas, such as shopping malls, hospitals, and scenic spots, the parking spaces with electric vehicle (EV) charging facilities are often occupied by ordinary cars. How to regulate the parking order in the parking lot is a key issue in the operation and management of the parking facilities. In this paper, a method of assessing parking fees for vehicles parked at the charging facilities is proposed based on an economic penalty strategy, including fixed-penalty and dynamic-penalty strategies. First, a traffic flow model of the parking lot in public area is established. Then, a price and consumption model of parking fees and parking lot utilization is established, along with different penalty strategies. Finally, taking the parking lot of a shopping mall as an example, the penalty strategies are optimized through particle swarm optimization (PSO) algorithm. The simulation results show that the method proposed can help to improve the utilization of EV charging facilities in parking lots and guide the orderly parking and charging of EVs at the same time.

With increasing renewable penetration and projected increase in natural disasters, the reliability and resiliency of a power system become crucial issues. As network inertia drops with increasing penetration of renewables, operators search for flexible resources that can help cope with a disruptive event or manage renewable intermittency. Energy storage is a solution, but the type of storage solution needs to be profitable to exist in the current and upcoming power markets. Advanced pumped-storage hydropower (PSH) is one solution that can help cope with such requirements, which will in turn help to increase the renewable penetration in the system. This paper qualitatively compares the revenue earning potential of PSH configurations, including, adjustable-speed PSH (AS-PSH) and ternary PSH (T-PSH) in comparison to conventional PSH (C-PSH) from the arbitrage and regulation markets, with and without the presence of wind penetration. In addition, a framework for quantitative analysis of any energy storage system has been proposed. A 24-bus RTS system is studied with summer and winter variations in load and wind power. Through revenue and operational mode analysis, this paper reveals that T-PSH has the highest revenue earning potential, which is mainly due to its ability to operate with a hydraulic short circuit.

This paper proposes a maximum power point tracking (MPPT) and voltage regulation method based on model predictive control (MPC) for the two-stage grid-tied photovoltaic (PV) system, which can achieve MPPT and output voltage regulation of a PV system simultaneously. The MPPT algorithm based on MPC is implemented in a DC-DC boost converter. The reference voltage at maximum power point is obtained by dual step Incremental Conductance (I&C) algorithm under the rapidly varying illumination intensity, and the MPPT controller only needs to minimize one cost function of PV current, without pulse width modulation (PWM) module. To inject the generated PV power into the grid with high quality, this paper designs voltage regulation controller based on MPC to maintain the output voltage of the PV system at the desired value. The MPC controller outputs the optimal duty signal with the input and state constraints in the inner loop, and the PI controller in the outer loop is designed to improve the dynamic performance. The proposed method based on MPC was demonstrated using the SimPower systems tool in MATLAB/Simulink. Analysis and simulation results for the PV system show possible improvements on the closed-loop performance such as fast response and low overshoot.

This paper proposes a supplementary control for tighter control of the air−fuel ratio (AFR), which directly affects the environmental emissions of thermal power plants. Dynamic matrix control (DMC) is applied to the supplementary control of the existing combustion control loops and the conventional double cross limiting algorithm for combustion safety is formulated as constraints in the proposed DMC. The proposed supplementary control is simulated for a 600-MW drum-type power plant and 1000 MW ultra-supercritical once-through boiler power plant. The results show the tight control of the AFR in both types of thermal power plants to reduce environmental emissions.

The ultra-supercritical (USC) coal-fired boiler-turbine unit has been widely used in modern power plants due to its high efficiency and low emissions. Since it is a typical multivariable system with large inertia, severe nonlinearity, and strong coupling, building an accurate model of the system using traditional identification methods are almost impossible. In this paper, a deep neural network framework using stacked auto-encoders (SAEs) is presented as an effective way to model the USC unit. In the training process of SAE, maximum correntropy is chosen as the loss function, since it can effectively alleviate the influence of the outliers existing in USC unit data. The SAE model is trained and validated using the real-time measurement data generated in the USC unit, and then compared with the traditional multilayer perceptron network. The results show that SAE has superiority both in forecasting the dynamic behavior as well as eliminating the influence of outliers. Therefore, it can be applicable for the simulation analysis of a 1000 MW USC unit.

With decreasing costs of renewable energy harvesting devices, penetration of solar panels and wind turbines have increased manifold. Under such high levels of penetration, coping with increased intermittency and unpredictability and maintaining power system resiliency under reduced inertia conditions has become a critical issue. Pumped storage hydro (PSH) is the most matured and economic form of storage that can serve the purpose of capacity for over 4 to 8 h. However, to increase network inertia and add required flexibility to low inertia power systems, significant paradigm shifting modifications have been engineered to result in the development of Ternary PSH (TPSH). In this paper a test system to consider governor interaction is constructed. The dynamic models of conventional PSH (CPSH) and TPSH are constructed and integrated to the test system to examine the effect of CPSH and TPSH in the hydraulic short circuit (TPSH-HSC). The ability and the effect of mode change (from pump to turbine) without the loss synchronism of TPSH has also been examined. Results display the superior capability and effect of TPSH with its HSC capability to contribute to frequency regulation during pumping mode and the effect of rapid mode change, as compared to its primitive alternative, CPSH.

The distributed energy system is an energy supply method built around the end users, which can achieve energy sustainability and reduce emissions compared to traditional centralized energy systems. The micro gas turbine (MGT)-based combined cooling and power (CCP) system has received renewed attention as an important distributed energy system technology due to its substantial energy savings and reduced emission levels. The task of the MGT-CCP system is to quickly adapt to changes in various renewable energy sources to maintain the balance in energy supply and demand in a distributed energy system. Therefore, it is imperative to improve the load tracking capability of the MGT-CCP system with advanced control technologies toward achieving this goal. However, the difficulty of controlling the MGT-CCP system is that the MGT responds very fast while CCP responds very slowly. To this end, the dynamic characteristics and nonlinear distribution of the MGT and CCP processes are analyzed, and a coordinated predictive control strategy is proposed by utilizing the generalized predictive control for the MGT system and the Hammerstein generalized predictive control for the CCP system. The coordinated predictive control of generalized predictive control and Hammerstein generalized predictive control was implemented in an 80 kW MGT-CCP simulator to verify the effectiveness of the proposed method. The simulation results show that compared with PID and MPC, the proposed control method not only can greatly improve simultaneous cooling and power load-following capability, but also has the best control effect when accessing with renewable energy.

The transportation industry contributes a significant amount of carbon emissions and pollutants to the environment globally. The adoption of electric vehicles (EVs) has a significant potential to not only reduce carbon emissions, but also to provide needed energy storage to contribute to the adoption of distributed renewable generation. This paper focuses on a design model and methodology for increasing EV adoption through automated swapping of battery packs at battery sharing stations (BShS) as a part of a battery sharing network (BShN), which would become integral to the smart grid. Current battery swapping methodologies are reviewed and a new practical approach is proposed considering both the technical and socio-economic impacts. The proposed BShS/BShN provides novel solutions to some of the most preeminent challenges that EV adoption faces today such as range anxiety, grid reliability, and cost. Challenges and advancements specific to this solution are also discussed.

Multi-energy carriers system (MECS), in which diverse energy carriers and different energy systems interact together, has drawn the interest of many researchers in recent years. However, the optimal economic operational model of the MECS is a nonlinear, multi-variable, and multi-period problem, of which it is difficult to find the solution because several different energy flows are integrated in the system. To this end, three interest bodies in the MECS were investigated, which included the energy provider, the energy facilitator, and the energy consumer, and a hierarchical optimal economic operation strategy was then presented. A hybrid optimization strategy combining the swarm intelligence algorithm and interior point method was developed taking advantage of the merits of each method. Case studies were conducted to verify the effectiveness of the proposed hierarchical optimal economic operation strategy, whereby demonstrating that the proposed strategy can achieve rational energy allocation and decrease the energy cost in the MECS compared with traditional energy systems.

A prospect of increasing penetration of uncoordinated electric vehicles (EVs) together with intermittent renewable energy generation in microgrid systems has motivated us to explore an effective strategy for safe and economic operation of such distributed generation systems. This paper presents a robust economic dispatch strategy for grid-connected microgrids. Uncertainty from wind power and EV charging loads is modeled as an uncertain set of interval predictions. Considering the worst case scenario, the proposed strategy can help to regulate the EV charging behaviors, and distributed generation in order to reduce operation cost under practical constraints. To address the issue of over-conservatism of robust optimization, a dispatch interval coefficient is introduced to adjust the level of robustness with probabilistic bounds on constraints, which gradually improves the system's economic efficiency. In addition, in order to facilitate the decision-making strategies from an economic perspective, this paper explores the relationship between the volatility of uncertain parameters and the economy based on the theory of interval forecast. Numerical case studies demonstrate the feasibility and robustness of the proposed dispatch strategy.

With electric vehicles (EVs) pouring into infrastructure systems, coupled traffic and energy transportation networks (CTETNs) can be applied to capture the interactions between the power grids and transportation networks. However, most research has focused solely on the impacts of EV penetration on power grids or transportation networks. Therefore, a simulation model was required for the interactions between the two critical infrastructures, as one had yet to be developed. In this paper, we build a framework with four domains and propose a new method to simulate the interactions and the feedback effects among CTETNs. Considered more accurately reflecting a realistic situation, an origin-destination (OD) pair strategy, a charging strategy, and an attack strategy are modeled based on the vehicle flow and power flow. The model is able to analyze the spatial and temporal distribution characteristics of EVs, and measure the impacts on power grids and transportation networks with road-block. The IEEE 33-bus system with geographic information was used as a test system to verify the effectiveness of the model.

In this paper, a robust estimation method for estimating the power system harmonics is proposed by using the optimal finite impulse response (FIR) filter. The optimal FIR filter is applied to the state space representation of the noisy current or voltage signal and estimates the magnitude and phase-angle of the harmonic components. Due to the FIR structure, the FIR filter is more robust against model uncertainty than the Kalman filter. Hence, the FIR filter-based method will give a more robust solution for the power system harmonic estimation than the previous Kalman filter-based approaches. The performance and robustness of the proposed method are verified through simulation. Moreover, the proposed method is employed in the power conditioning system to estimate the harmonic components and total harmonic distortions.

The monoethanolamine (MEA)-based post-combustion CO2 capture plant must operate flexibly under the variation of the power plant load and the desired CO2 capture rate. However, in the presence of process nonlinearity, conventional linear control strategy cannot achieve the best performance under a wide operation range. Considering this problem, this paper systematically studies the multi-model modeling of the MEA-based CO2 capture process for the purpose of (1) implementing well-developed linear control techniques to the design of an advanced controller and (2) achieving a wide-range flexible operation of the CO2 capture process. The local linear models of the CO2 capture process are firstly established at given operating points using the method of subspace identification. Then the nonlinearity distribution at different loads of an upstream power plant and different CO2 capture rates is investigated via the gap metric. Finally, based on the nonlinearity investigation results, the suitable linear models are selected and combined together to form the multi-model system. The proposed model is validated using the measurement data, which is generated from a post-combustion CO2 capture model developed in the go-carbon capture and storage (gCCS) simulation platform. As the proposed multi-linear model has a simple mathematical expression and high prediction accuracy, it can be directly employed as the control model of a practical advanced control strategy to achieve a wide operating range control of the CO2 capture process.

An optimal load-tracking operation strategy for a grid-connected tubular solid oxide fuel cell (SOFC) is studied based on the steady-state analysis of the system thermodynamics and electrochemistry. Control of the SOFC is achieved by a two-level hierarchical control system. In the upper level, optimal setpoints of output voltage and the current corresponding to unit load demand is obtained through a nonlinear optimization by minimizing the SOFC’s internal power waste. In the lower level, a combined L1-MPC control strategy is designed to achieve fast set point tracking under system nonlinearities, while maintaining a constant fuel utilization factor. To prevent fuel starvation during the transient state resulting from the output power surging, a fuel flow constraint is imposed on the MPC with direct electron balance calculation. The proposed control schemes are testified on the grid-connected SOFC model.

The deterministic methods generally used to solve DC optimal power flow (OPF) do not fully capture the uncertainty information in wind power, and thus their solutions could be suboptimal. However, the stochastic dynamic AC OPF problem can be used to find an optimal solution by fully capturing the uncertainty information of wind power. That uncertainty information of future wind power can be well represented by the short-term future wind power scenarios that are forecasted using the generalized dynamic factor model (GDFM)—a novel multivariate statistical wind power forecasting model. Furthermore, the GDFM can accurately represent the spatial and temporal correlations among wind farms through the multivariate stochastic process. Fully capturing the uncertainty information in the spatially and temporally correlated GDFM scenarios can lead to a better AC OPF solution under a high penetration level of wind power. Since the GDFM is a factor analysis based model, the computational time can also be reduced. In order to further reduce the computational time, a modified artificial bee colony (ABC) algorithm is used to solve the AC OPF problem based on the GDFM forecasting scenarios. Using the modified ABC algorithm based on the GDFM forecasting scenarios has resulted in better AC OPF’ solutions on an IEEE 118-bus system at every hour for 24 h.

This paper focuses on the optimal intraday scheduling of a distribution system that includes renewable energy (RE) generation, energy storage systems (ESSs), and thermostatically controlled loads (TCLs). This system also provides time-of-use pricing to customers. Unlike previous studies, this study attempts to examine how to optimize the allocation of electric energy and to improve the equilibrium of the load curve. Accordingly, we propose a concept of load equilibrium entropy to quantify the overall equilibrium of the load curve and reflect the allocation optimization of electric energy. Based on this entropy, we built a novel multi-objective optimal dispatching model to minimize the operational cost and maximize the load curve equilibrium. To aggregate TCLs into the optimization objective, we introduced the concept of a virtual power plant (VPP) and proposed a calculation method for VPP operating characteristics based on the equivalent thermal parameter model and the state-queue control method. The Particle Swarm Optimization algorithm was employed to solve the optimization problems. The simulation results illustrated that the proposed dispatching model can achieve cost reductions of system operations, peak load curtailment, and efficiency improvements, and also verified that the load equilibrium entropy can be used as a novel index of load characteristics.

As a result of extensive penetration of wind farms into electricity grids, power systems face enormous challenges in daily operation because of the intermittent characteristics of wind energy. In particular, the load peak-valley gap has been dramatically widened in wind energy-integrated power systems. How to quickly and efficiently meet the peak-load demand has become an issue to practitioners. Previous literature has illustrated that the demand response (DR) is an important mechanism to direct customer usage behaviors and reduce the peak load at critical times. This paper introduces air-conditioning loads (ACLs) as a load shedding measure in the DR project. On the basis of the equivalent thermal parameter model for ACLs and the state-queue control method, a compensation cost calculation method for the ACL to shift peak load is proposed. As a result of the fluctuation and uncertainty of wind energy, a two-stage stochastic unit commitment (UC) model is developed to analyze the ACL users’ response in the wind-integrated power system. A simulation study on residential and commercial ACLs has been performed on a 10-generator test system. The results illustrate the feasibility of the proposed stochastic programming strategy and that the system peak load can be effectively reduced through the participation of ACL users in DR projects.

This paper presents linear programming (LP) formulations for short-term energy time-shift operational scheduling with energy storage systems (ESSs) in power grids. In particular, it is shown that the conventional nonlinear formulations for electric bill minimization, peak shaving, and load leveling can be formulated in the LP framework. New variables for the peak and off-peak values are introduced in peak shaving and load leveling model, and the historical peak value for demand charge are considered in the electric bill minimization model. The LP formulations simplify computation while maintaining the accuracy for including linear technical constraints of ESSs, such as the state-of-charge, charging/discharging efficiency, output power range, and energy limit considering the life cycle of ESS. Proposed LP formulations have been implemented and verified in practical power systems and a large-scale industrial customer using historical data.