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In this work, we study simultaneous reconstructions of sensor fault and actuator fault, as well as fault-tolerant control (FTC) for nonlinear systems approximated by the T–S fuzzy model. By making the sensor fault as part of the state, a rectangular singular system is firstly given, based on which, a proportional-integral observer (PIO) is synthesized to realize the reconstructions of sensor and actuator faults simultaneously. With the support of these estimation information, an FTC scheme is well provided to maintain the stability of the closed-loop system even in the occurrence of faults. Furthermore, by resorting to linear matrix inequalities (LMIs), the stability criteria are formulated via the fuzzy Lyapunov function method. Finally, the performance of the theoretical result is verified through two real dynamics. [ABSTRACT FROM AUTHOR]

Shock initiation criteria are essential to the shock initiation process and applications in modern pyrotechnics. The most commonly used shock initiation criteria are Walker and James criterion, which can very well describe the threshold data of the impactor with sufficient size. However, the criteria were also found not to provide a good fit to the data of thin, curve, or small flyers. By comparing the wave structure of the shock process with the stable detonation wave structure, a shock initiation criterion is developed based on the concept of the chemical reactions during the impact, focusing more on the properties of the explosives. Furthermore, a desktop micro-flyer initiating system was designed for the initiation of the thin metal flyer. The obtained data and classical historical data were analyzed with the proposed criterion, producing an excellent fit, with R2 values greater than 0.96. Compared to the existing criteria, the proposed criterion can weaken the influence of the interfacial properties of the impact and collapse the threshold velocity data with different impactor types to a single curve. The shock sensitivities of various explosives are also discussed based on the criterion. A denser impactor or the incorporation of impurities may contribute to the generation of the hot spot during the impact, leading to an increase in the sensitivity. The proposed criterion provides insight into the development of the shock initiation criteria and may help to understand the mechanism of shock initiations. [ABSTRACT FROM AUTHOR]

The increasing penetration of renewable energy generation brings about variability and randomness, which poses challenges to the power systems due to a potential shortage of flexibility resources. Inverter heat pumps (IHPs) can be utilized to address this issue by providing flexible ramping capacity (FRC). However, unlike conventional generation that offers a fixed FRC, the FRC of individual IHPs and their aggregation are influenced by their operational constraints and the indoor temperature thresholds set by buildings. These thresholds, in turn, are affected by uncertainties in ambient temperature and solar irradiation. For the individual IHP, an IHP-FRC assessment model is established. This individual IHP-FRC assessment model is based on the building thermal dynamic model and user comfort model, incorporating uncertainties in ambient temperature and solar irradiation through affine representations. For the IHP aggregation within a virtual power plant (VPP), a two-stage FRC assessment method is proposed to assess the FRC of an IHP-VPP in both the day-ahead and intra-day periods. In the day-ahead period, the VPP-FRC is calculated using the Minkowski sum method to ensure resource adequacy for meeting ramping demands. In the intra-day period, the VPP-FRC is rolling adjusted according to the operating status of each IHP and sent to the dispatch centre of the power system. The max-min fairness (MMF) ramping demand allocation strategy is employed to ensure fairness in allocating ramping demands to individual IHPs within the VPP. The simulation with 10,000 households in a winter heating scenario is conducted. The results indicate that the proposed method provides sufficient ramping capacities during the day-ahead period, with an upward FRC maximum of 6.29 MW/10 min and a downward FRC of −3.65 MW/10 min. The IHP-VPP effectively responds to ramping demands during the intra-day period without affecting the fairness, indoor comfort, and considering uncertainties in solar irradiation and ambient temperature. • A framework for assessing the flexible ramping capacity (FRC) of the inverter heat pump - virtual power plant (IHP-VPP) is designed. • A two-stage affine assessment method is developed to interact FRC of IHP-VPP with dispatch center. • The ramping demand is allocated to each IHP by the max-min fairness (MMF) allocation method for the subsequent assessment cycle. [ABSTRACT FROM AUTHOR]

With the increasing distribution network accidents caused by typhoon disasters, risk management in typhoon scenarios is necessary. A risk management framework for power distribution networks (PDNs) undergoing a typhoon disaster is proposed. First, a risk prediction model of the PDN under typhoon conditions based on the Batts model is developed to predict the loss of load risk (LLR) index and economic loss risk (ELR) index of the PDN before the typhoon lands. Then, a vulnerability index of overhead lines, considering the failure frequency of specific overhead lines and their load importance, is introduced for use in a weak link identification model to identify weak links in the PDN. Finally, a two‐time‐step risk relief model based on typhoon forecast information and the identified weak links is developed to allocate PDN relief materials to reduce the LLR and ELR during a typhoon disaster. An IEEE RBTS BUS6 system is employed as a test system to verify the risk management strategy. The vulnerability index proposed by the strategy considers both the importance of load and line failure. The risk management strategy adopts a two‐time‐step model, which reduces the impact of meteorological forecast errors and enables more efficient use of materials. The results show that the proposed framework can take more effective use of relief materials to reduce the loss of PDNs compared with the traditional strategy. [ABSTRACT FROM AUTHOR]

Experimental research and numerical simulations of the structural response to shock waves with pulse durations of hundreds of milliseconds, or even seconds, are extremely challenging. This paper takes typical single-layer and sandwich cylindrical shells as the research objects. The response rules of cylindrical shells under long-duration blast loadings were studied. The results show that when the pulse duration is greater than or equal to 4~5 times the first-order period of the structure, the maximum response of the structure tends to be consistent, that is, the maximum response of the cylindrical shells with different vibration shapes shows a saturation effect as the pulse duration increases. This study established the relationship between the saturation loading time and the inherent characteristics of the structure. It was found that the saturation effect was applicable under the following conditions, including different load waveforms, elastic–plastic deformation of the structure, and the loading object being a sandwich shell. This will help transform the long-duration explosion wave problem into a finite pulse-duration shock wave problem that can be realized by both experiments and numerical simulations. [ABSTRACT FROM AUTHOR]

Collaboration between transmission and distribution system operators (TSOs and DSOs) is crucial to improve power supply reliability during a massive blackout. However, these two systems usually operate separately in reality. To coordinate them, a new fully parallel decentralized TSO + DSO service restoration (D-TDSR) scheme is proposed in this paper. Based on analytical target cascading (ATC) method, the proposed D-TDSR is able to formulate the local restoration model for each TSO and DSO by decomposing shared boundary information in an iterative process. By introducing the diagonal quadratic approximation in ATC, all local restoration models are solved in a fully parallel manner with no need for a central coordinator. This parallel implementation increases the computation efficiency of decentralized restoration procedure. Furthermore, to enhance the ability of TSO and DSOs to support each other, the potential benefits of flexible regulation in DSO, including soft open points and network reconfiguration, are fully exploited, thus improving critical load restoration speed, and reducing voltage deviations. The effectiveness of the proposed D-TDSR is validated using an IEEE standard test system. • A decentralized load restoration for transmission and distribution system is proposed. • SOP flexibility is fully exploited to improve load restoration level and speed. • The fully parallel restoration procedure is proposed to reduce the computation time of each iteration. • The warm start mode is developed, which further accelerates the convergence speed of the restoration model. [ABSTRACT FROM AUTHOR]

• A multi-level overload relief method for transmission network is proposed. • The proposed method can capitalize on the capability from the ADN for overload relief in transmission network. • An interactive mechanism between the transmission network and ADN is proposed. With the fast development of technologies of renewable generations and distributed energy resources, the distribution grids become more and more active, and the coordinated transmission and distribution management (CTDM) becomes more important. Under the CTDM framework, this paper proposes a multi-level overload relief method for transmission network using the capability from an active distribution network (ADN). When an overload event occurs at the transmission network, an appropriate operational scheme for ADN is selected by the proposed multi-level method. Then, the ADN takes the decided operational scheme and adjusts its operational status (i.e., reconfiguring its network topology, transferring loads to Microgrids and shedding the controllable loads) to contribute to load curtailment from the ADN side and further alleviate overload in the transmission network. Numerical case studies show that the proposed method can alleviate the overload of transmission network and maintain the normal operation of the ADN, which demonstrates a promising application for CTDM. [ABSTRACT FROM AUTHOR]

This study focuses on the proportional plus derivative state feedback (PDSF) control problem for continuous-time nonlinear descriptor systems described by Takagi–Sugeno fuzzy models with distinct fuzzy rules appearing in both sides of the considered system. Without using any additional transformations, some new conditions are developed by means of the free-weighting matrix technique and presented in terms of linear matrix inequalities to ensure that the resultant closed-loop system is normal and stable. Explicit expression of the desired PDSF controller is also given. Furthermore, the obtained result is extended to a class of uncertain nonlinear descriptor systems with norm-bounded perturbations. Finally, three examples are presented to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

• A data-driven RO control approach for a BES that integrates a VESS is proposed. • The control-oriented state space model of BES is established. • Conventional thermal comfort theory and adaptive thermal comfort theory are considered in VESS modelling. • SVM is adopted to correct the day-ahead forecast errors of outdoor temperature and solar irradiance. • The economy and load matching of BES are improved on the basis of ensuring thermal comfort of occupants. The virtual energy storage system (VESS) is an innovative and cost-effective technique for coupling building envelope thermal storage and release abilities with the electric and heat power conversion characteristics of an air conditioner; this system provides building energy systems (BESs) with adjustable potentials similar to those of conventional battery energy storage systems (BESSs). However, the VESS is a dynamic system, and uncertainties in the outdoor temperature and solar irradiance are difficult to accurately predict, which impacts the quantification accuracy of VESSs; these characteristics challenge the BES control scheme economy and the thermal comfort of occupants. To solve this crucial issue, a data-driven rolling optimization (RO) control approach for a BES that integrates a VESS is proposed. First, a BES state space model integrating the VESS is created to reflect the VESS adjustable potential and dynamic characteristics. Based on the above model, while aiming at a small BES data sample size, a support vector machine (SVM) is combined with RO to correct the day-ahead quantification errors of the VESS adjustable potential and enhance the economical operation and thermal comfort of the BES that integrates the VESS in uncertain environments. Comparative simulations validate the effectiveness of this VESS modelling and data-driven RO control approach. [ABSTRACT FROM AUTHOR]

With the increasing number of electric vehicles (EVs), the EV fast charging load will significantly affect the voltage quality of electricity distribution networks. On the other hand, EVs have potentials to change the choices of charging locations due to the incentives from the variations of charging prices, which can be considered as a flexible response resource for electricity distribution networks. In this paper, a charging pricing strategy of EV fast charging stations (FCSs) was developed to determine the pricing scheme for the voltage control of electricity distribution networks, which consisted of a simulation model of EV mobility and a double-layer optimization model. Considering the travel characteristics of users, the simulation model of EV mobility was developed to accurately determine the fast charging demand. Taking the total income of FCSs and the users’ response to the pricing scheme into account, the double-layer optimization model was developed to optimize the charging pricing scheme and minimize the total voltage magnitude deviation of distribution networks. A test case was used to verify the proposed strategy. The results show that the spatial distribution of EV fast charging loads was reallocated by the proposed charging pricing scheme. It can also be seen that the proposed strategy can make full use of the response capacity from EVs to improve the voltage profiles without decreasing the income of the FCSs. [ABSTRACT FROM AUTHOR]

• By fully using the associated information among subsystems, a novel unknown input method-based distributed state and fault estimation scheme is presented for a kind of continuous-time interconnected systems. Significantly, our method gets rid of the assumption that the first derivative of faults is equal to zero which was made in recent results. This, indeed, endows the developed method with some advantages over those existing ones. • To situate our design scheme in a more practical context, the faults studied in our paper do not need to satisfy the constraints such as generated from an exogenous system or having a known bound made in previous results. It should be noted that such designs take on more significance, since obtaining the information of faults to meet these constraints may be a hard task in practical. In addition, different from existing results, by introducing the derivative of output into the fault estimation design, our developed method can realize state and fault estimation without using the H ∞ technique, which is helpful to enrich the theoretical research of fault estimation for interconnected systems. • In contrast to some recent results where the observer synthesis conditions were presented in the augmented matrices framework, all the stability conditions in our paper are expressed in terms of original system matrices without using the popular augmented method, which is a new attempt for interconnected systems. Therefore, our design greatly reduces the computational complexity in theory and the observer implementation cost in reality. By the way, the regional pole assignment is also discussed in our scheme to not only avoid the singularity of observer gains, but also improve the estimation performance. This paper is devoted to the state and fault estimation design for continuous-time interconnected systems. By utilizing the associated information among subsystems, a novel unknown input method-based distributed observer synthesis scheme is presented to reconstruct system states and faults simultaneously. It is worth pointing out that our method has much broader applications, since the faults studied in each subsystem do not have to satisfy some constraints made in previous results such as having a known bound or the first derivative being equal to zero. By resorting to the Lyapunov theory and matrix transformation technique, some brand-new conditions are proposed to guarantee the stability of local error systems, by which observer gains can be solved in terms of linear matrix inequalities (LMIs). Particularly, all the conditions are expressed in the original system matrices rather than augmented matrices framework. Thus, computational complexity is reduced compared to previous related researches. At last, simulation experiments are shown to verify the validity of our distributed state and fault estimation scheme. [ABSTRACT FROM AUTHOR]

System inertia reduction, driven by the integration of renewables, imposes significant challenges on the primary frequency control. Electrification of road transport not only reduces carbon emission by shifting from fossil fuel consumption to cleaner electricity consumption, but also potentially provide flexibility to facilitate the integration of renewables, such as supporting primary frequency control. In this context, this paper develops a techno-economic evaluation framework to quantify the challenges on primary frequency control and assess the benefits of EVs in providing primary frequency response. A simplified GB power system dynamic model is used to analyze the impact of declining system inertia on the primary frequency control and the technical potential of primary frequency response provision from EVs. Furthermore, an advanced stochastic system scheduling tool with explicitly modeling of inertia reduction effect is applied to assess the cost and emission driven by primary frequency control as well as the benefits of EVs in providing primary frequency response under two representative GB 2030 system scenarios. This paper also identifies the synergy between PFR provision from EVs and “smart charging” strategy as well as the impact of synthetic inertia from wind turbines. [ABSTRACT FROM AUTHOR]

The increasing complexities of hybrid energy Microgrid (H-Microgrid) integrated with renewable generations, dispatchable distribution generators (DGs) and low-carbon buildings require more intelligent dispatch method. The building sector occupies the main body of the energy consumption, which represents a major potential contributor for reducing the daily operating cost of the H-Microgrid. In this paper, a building based virtual energy storage system (VESS) model was developed by utilizing the heat storage capability of the building. Then, a dynamic economic dispatch (DED) model of the H-Microgrid considering the VESS was developed. Finally, the VESS was integrated into the DED model of the H-Microgrid for daily operating cost reduction. The indoor temperature of the building was adjusted within the customer temperature comfort range to manage the charging/discharging power of the VESS. Numerical studies demonstrate that the proposed DED method can make full use of the available capacity of VESS to reduce the daily operating cost, and guarantee the customer temperature comfort level at the same time. [ABSTRACT FROM AUTHOR]

Considering the travelling behaviours of electric vehicles (EVs), an efficient power plant model of EVs (E-EPP) is developed for the active power regulation of the power system with large-scale wind farms. Based on the EV data base provided by the EU MERGE project, a generic V2G model (GVGM) is established. The Monte Carlo Simulation (MCS) method is implemented within the E-EPP to obtain the available response capacity of the EVs. A new active power regulation strategy based on the E-EPP is developed. A modified IEEE 118-bus system integrated with large-scale wind farms is used to verify the E-EPP model with the active power regulation strategy under different charging scenarios (dumb charging, smart charging and hybrid charging). The simulation results show that the E-EPP can improve the operating security and stability of the power system. The operation cost and the carbon emission are decreased by introducing large-scale wind farms. [ABSTRACT FROM AUTHOR]

An optimal day-ahead scheduling method (ODSM) for the integrated urban energy system (IUES) is introduced, which considers the reconfigurable capability of an electric distribution network. The hourly topology of a distribution network, a natural gas network, the energy centers including the combined heat and power (CHP) units, different energy conversion devices and demand responsive loads (DRLs), are optimized to minimize the day-ahead operation cost of the IUES. The hourly reconfigurable capability of the electric distribution network utilizing remotely controlled switches (RCSs) is explored and discussed. The operational constraints from the unbalanced three-phase electric distribution network, the natural gas network, and the energy centers are considered. The interactions between the electric distribution network and the natural gas network take place through conversion of energy among different energy vectors in the energy centers. An energy conversion analysis model for the energy center was developed based on the energy hub model. A hybrid optimization method based on genetic algorithm (GA) and a nonlinear interior point method (IPM) is utilized to solve the ODSM model. Numerical studies demonstrate that the proposed ODSM is able to provide the IUES with an effective and economical day-ahead scheduling scheme and reduce the operational cost of the IUES. [ABSTRACT FROM AUTHOR]

• A deep reinforcement learning method for indoor temperature control is proposed. • Predicted mean vote is adopted to evaluate people's thermal comfort. • The future information obtained by the deep learning method is adopted as the decision information of the agent. • The performance of the proposed method is verified in different situations. • The average energy cost reduces by 24.29% and 23.63% compared to other traditional control methods. The extension of in-home time has boosted people's increase demand for comfortable indoor temperatures. This paper aims to propose a smart home-based temperature control framework based on deep learning and reinforcement learning to automatically control indoor temperature. The heat pump power and the ventilation system volume could be adjusted by the proposed framework to minimize costs while maintaining indoor comfort. The uncertainty of future information and the requirement for continuous control make it challenging to build an intelligent temperature control system. Simultaneously, the human body is sensitive to different temperatures. Therefore, the selection of appropriate evaluation criteria for people's thermal comfort is essential. To address this problem, the Predicted Mean Vote (PMV) criterion is applied to scientifically evaluate human thermal comfort. The indoor temperature control problem is transformed into a Markov decision process. A deep learning framework (Proximal Policy Optimization PPO) based on continuous control actions is proposed to solve this problem. Combined with the deep learning method Long Short-Term Memory (LSTM), future state information is applied to improve the control stability. The proposed framework performs well under different conditions in summer and winter. The proposed framework performs well under different conditions in summer and winter. It is compared by the discrete action control method Deep Q Network and the PPO algorithm, which does not contain future state information. As a result, the proposed framework achieves 24.29% and 23.63% cost savings in winter and summer, respectively. [ABSTRACT FROM AUTHOR]

The game relationship between the Energy Server (ES) and the Energy Consumer (EC) in the Integrated Community Energy System (ICES) can change the energy supply and consumption behaviors of both participants, and affect the economy of the ICES planning scheme. This paper proposes a bi-level model to address a joint master-slave planning-operation problem of the ICES. Firstly, the revenue models of the ES and EC in the ICES is established. Secondly, a bi-level planning model based on Stackelberg Game for the ICES is proposed. In the upper level, a joint optimal planning model of energy conversion devices and energy prices is built and used by the ES (game leader) to maximize its profit. In the lower level, an optimal energy consuming strategy is proposed for the EC (game follower) to minimize its energy bill. The Karush-Kuhn-Tucker (KKT) conditions are utilized to convert the bi-level planning problem to a single-level mathematical program with equilibrium constraints (MPEC). A typical ICES is employed as a test system to illustrate the effectiveness of the proposed bi-level planning approach, and the results show that the proposed approach can reduce the investment cost of ES while ensuring the interests of EC. • A bi-level planning method for ICES based on Stackelberg game is proposed. • Revenue models of the ES and the EC are established. • A single level MPEC is derived by using the KKT conditions transformation. • An actual ICES shows the effectiveness of the proposed bi-level planning method. [ABSTRACT FROM AUTHOR]

• A hybrid-driven DEH is established to model the ICES under off-design conditions. • Data-driven PR and BPNNs methods are used to correct nonlinear equipment efficiency. • A two-stage scheduling method based on the hybrid-driven DEH is proposed. • RO strategy is adopted to enhance the adaption to supply–demand uncertainties. • The solution speed, accuracy and economy of scheduling schemes are improved. The integrated community energy system (ICES) is an effective means to promote the synergies among multiple energy carriers. However, the off-design performance of equipment challenges the accurate and economical scheduling of the ICES. To solve this problem, a two-stage scheduling method for the ICES based on a hybrid mechanism and data-driven model is proposed in this paper. Combing the mechanism energy hub (EH) model with a data-driven efficiency correction model, a hybrid-driven dynamic energy hub (DEH) with variable equipment efficiency is built first. The EH describes the multi-energy coupling relationship; the embedded efficiency correction model adopts data-driven approaches of polynomial regression (PR) and backpropagation neural networks (BPNNs) to accurately extract nonlinear characteristics of equipment efficiency. On this basis, a two-stage scheduling model for the ICES is developed. In the day-ahead stage, the PR method is applied to calculate equipment efficiency which varies with load rate. The day-ahead scheduling model is established with the aim of minimizing the operating cost. In the intraday stage, considering the effects of load rate, temperature, and atmospheric pressure, the BPNNs method is employed to further correct equipment efficiency using the latest data. Furthermore, a rolling optimization (RO) strategy is used to address the uncertainties of equipment efficiency and load demand to improve the accuracy and economy of the scheduling scheme. Case studies demonstrate that the proposed method can improve the solution speed and accuracy of the scheduling model, and enhance the operating economy of the ICES. [ABSTRACT FROM AUTHOR]

In order to pursue the low-carbon development around the world, a large scale of renewable generation will be connected to the power systems. Take the Great Britain (GB) as an example, the GB power system has a large wind energy integration potential. The intermittency of wind generation will have great impact on the system frequency stability. Electric vehicles (EVs) have a crucial role in decarbonizing the transport sector. To increase the utilization of wind energy, EVs are suggested to provide frequency response service to the power system due to their quick power reaction characteristic. This paper proposes a general dynamic EV frequency control strategy considering the travelling behavior of the EV users. A droop control method is used to regulate the EV charging/discharging power according to the frequency signal. A Forced-Charge Boundary (FCB) and a Forced-Charge Area (FCA) are proposed to guarantee sufficient energy in the EV battery for user’s travel at the plug-out time. A dynamic Virtual Energy Storage System (VESS) is developed to evaluate the frequency response capacity of the EV clusters. In the case study, the model of the GB power system is used to investigate the frequency control effect of the control strategy. The simulation results show that the proposed strategy provides effective EV frequency response to the power system and thus is able to facilitate the integration of wind energy. [ABSTRACT FROM AUTHOR]

The park-level integrated energy system (PIES), a typical user-side energy supply mode, has developed rapidly in recent years due to its economic and environmentally friendly advantages. Natural gas and part of the electricity of PIES are usually purchased from external gas or power systems and then converted to other forms of energy, such as heating and cooling. However, the uncertainties and correlation of the natural gas price (NGP) and electricity price (EP) bring high risk to PIES planning and may lead to economic deterioration of PIES at the operation stage. Therefore, a risk assessment method for PIES planning is proposed. The marginal distributions of NGP and EP are established by the kernel density estimation method. Then, the copula function is adopted to construct the joint distribution of NGP and EP considering their correlation, according to which the Monte Carlo method is utilized to generate joint scenarios of NGP and EP. On this basis, a risk assessment model considering the uncertainties of energy prices for PIES planning is established, where the conditional value at risk (CVaR) is adopted as the risk measure since it can quantify the impact of extreme uncertainties of energy prices on PIES planning. Case studies show that deepening the degree of multi-energy coupling and the utilization of distributed renewable energy generation can enhance the risk-resistance abilities of the PIES. The proposed method provides an effective tool for decision making related to PIES planning, which can help avoid the risk caused by the uncertainties and correlation of energy prices. • The uncertainties of natural gas price and electricity price are modelled. • The copula function is adopted to construct the joint distribution of energy prices. • A risk assessment method for PIES planning based on CVaR is proposed. • The risk loss of PIES planning caused by variation of energy prices is evaluated. [ABSTRACT FROM AUTHOR]

A directional control method (DCM) for power flows on a set of interface lines between two regions of power system considering static voltage stability margin is developed in this paper. A surface approximation approach is firstly used to obtain the relationship between the interface flow solution and the generation direction of generator (the portion of generation variation in each participating generator to satisfy the desired power increase on the interface and the system loss). Then, an optimization model is built to determine the optimum dispatching scheme of generators. This method not only can control the total power on the interface to satisfy the power demand but also can realize the directional control of power on each interface line based on the needs of operation. The proposed DCM is further extended to determine the optimum dispatching scheme of generators for maximizing the interface flow margin (IFM), which is the active power margin of the key transmission lines between two regions of power system constrained by static voltage stability. A modified continuation power flow (MCPF) is used to show and evaluate the impacts of the DCM on the IFM. The New England 39-bus system and the IEEE 300-bus system have been employed to verify the effectiveness of the DCM. [ABSTRACT FROM AUTHOR]

• A unit-level joint operation framework of wind farm and CHP plant is proposed, which is more applicable compared with the system-level method to the cooperation of CHP plant and wind farm. • CHP plant, heating network and heat exchanger is introduced in the modeling of heating system to incorporate the CHP plant into the joint system operation, and the energy flexibilities of heating networks are modeled based on a quasi-dynamic model of thermal energy transport. • A scenario-based optimal dispatch method of the joint system is established based on an ARMA sampling model, which improves the operational flexibility and the potential of balancing wind power uncertainty of CHP plant, thus improving the total revenue of joint system. A scenario-based optimal dispatch is proposed for joint operation of a wind farm and combined heat and power (CHP) plant considering energy flexibilities in heating networks. First, the heating network is modelled, and energy flexibility is used to improve the flexible adjustment ability of the CHP plant. Then, considering the uncertainty of wind power, a scenario-based optimal dispatch model for wind farms and CHP plants with heating networks is proposed to maximize the revenue expectation of the joint operation. The simulation results show that the joint operation revenue is improved compared with that of independent operation. Considering the energy flexibilities in heating networks, the CHP plant in the joint system can compensate for the fluctuations in wind power output, promote the integration and consumption of wind power, and reduce the penalty cost caused by the uncertainty of wind power. [ABSTRACT FROM AUTHOR]

Highlights: [•] The Spatial Temporal model (STM) runs based on systematic integration of power system analysis and transportation analysis. [•] For the first time Origin–Destination analysis was used to model the spatial and temporal characteristics of EV charging load. [•] The STM can provide both average and probabilistic values and can identify the critical network components to upgrade. [•] Compared with the previous studies in this area, the STM shows more comprehensive and accurate evaluation results. [Copyright &y& Elsevier]

Highlights: [•] A new hysteretic loop control strategy for frequency regulation is developed. [•] A statistical model to determine the capacity of BSHS is proposed. [•] Monte Carlo simulation is implemented to the statistical model. [•] The capacities of BSHS at different cumulative probability levels are determined. [•] The control strategies have close relationship with the capacity of BSHS. [ABSTRACT FROM AUTHOR]

• The proposed topology has a more flexible interconnection with hybrid microgrids. • The number of power switches is reduced compared with conventional MMC-based hybrid. • Improved operation capability can be obtained under unbalanced power distribution in microgrids. • A hierarchical energy control method is proposed with balanced capacitor voltages in MMC. • The microgrids can operate normally at different operation modes. Conventionally, the multilevel converter-based multi-terminal hybrid microgrids require a large number of power switches and have a limited operation capability under unbalanced power distribution in medium and low voltage (MV/LV) AC/DC microgrids. To solve this issue, this paper proposes the novel modular multilevel converter (MMC)-based five-terminal MV/LV hybrid AC/DC microgrids. The proposed hybrid microgrids realize the interconnection between the medium-voltage AC (MVAC), MVDC, low voltage AC (LVAC), and two LVDC terminals. In addition, the MVAC grid is connected to the AC terminal of MMC, and the MVDC microgrid is connected to the DC terminal of MMC through a dual active bridge (DAB) converter. Based on MMC, the compact interlinking converters are established, providing three LVDC terminals, which are connected to two LVDC microgrids and one LVAC microgrid through a DC/AC converter. Compared with the conventional MMC-based hybrid microgrids, the proposed topology can significantly reduce the number of power switches. Moreover, to overcome the control challenge of arm energy balancing in MMC and meet the requirement of different operation modes in microgrids, a hierarchical energy control method is proposed, where the low circulating currents are injected to balance the arm energy. Therefore, the system operation capability can be improved under unbalanced power distribution. Validation results in different conditions (power step, power reversal, and unbalanced MVAC voltages) indicate that by the proposed method, the arm energy and capacitor voltages in MMC are well balanced, and the proposed hybrid AC/DC microgrids can operate normally at different modes. [ABSTRACT FROM AUTHOR]

• A four-terminal interconnection scheme for the hybrid AC/DC microgrid is proposed. • Higher flexibility and a higher tolerance for unequal power distribution are obtained. • An improved energy control method is proposed to balance cell capacitor voltages. • The AC current component is maintained zero on the MVDC trans-mission lines. • The symmetry of the AC currents is guaranteed with this control method. With the large-scale integration of the distribution generations (DGs) and the increasing medium-voltage and low-voltage DC power demands, multi-terminal hybrid AC/DC microgrid has drawn great attention from researchers around the world. In order to reduce the number of power conversion stages and meet DC transmission demands under different DC voltage levels, this paper proposes a four-terminal interconnection scheme of the hybrid AC/DC microgrid, connecting one medium-voltage AC (MVAC) terminal, one medium-voltage DC (MVDC) terminal and two low-voltage DC (LVDC) terminals. The proposed interconnection scheme includes a modular multilevel converter (MMC) as the main interlinking converter of the MVAC grid and MVDC microgrid, and a series of dual active bridges (DAB) converters as two isolated LV DC microgrid interfaces. It has more flexibility for power supplies, especially MVDC transmission, and a more robust tolerance for unequal power distribution between the two LVDC Microgrids. To realize the DC capacitor voltage balancing control, an improved energy control method is proposed in this paper. The proposed method keeps DC capacitor voltage balance and AC current zero on the MVDC transmission lines, which contributes to the stability of the MVDC microgrid. In addition, the symmetry of the AC currents is also guaranteed with this control method. Validation results of a four-terminal hybrid AC/DC microgrid verify the effectiveness of the proposed microgrid and control scheme. [ABSTRACT FROM AUTHOR]

• Simultaneous robust normalization and H-infinity control problems are first addressed in a systematic way for uncertain TSFSSs. • Meaningfully, the uncertainties are involved in all system matrices including derivative matrices, which is more complicated in the design. • There are no restrictions imposed on the rank of derivative matrices with uncertainties, that is, the derivative matrices may be singular. • Without using any model transformations, the fuzzy PDSFC is effectively synthesized by resorting to the free-weighting matrix method. • All the conditions are presented as strict LMIs. Thus, it can be handled conveniently by Matlab. This work deals with the simultaneous robust normalization and H ∞ stabilization problems for uncertain Takagi-Sugeno fuzzy singular systems (TSFSSs) with time-delays via a novel fuzzy proportional-derivative state feedback controller (PDSFC). It is worth pointing out that the uncertainties involved in this paper exist in all different system matrices including E i. Without using any model transformations, some new criteria that ensure the closed-loop system to be robustly normal and stable for all admissible uncertainties are exactly deduced by means of the free-weighting matrix method, and clearly expressed as strict linear matrix inequalities. Meanwhile, the prescribed H ∞ performance is also achieved. Then, based on the obtained criteria, the specific expression of desired fuzzy PDSFC is given. Finally, the correctness and feasibility of our theoretical results are verified via two academic examples. [ABSTRACT FROM AUTHOR]

• A dynamic energy hub model considers varying energy conversion efficiencies. • The dependency between efficiency and load rate is included in the model. • A double-layer planning method is designed based on the model. • A case study demonstrates the effectiveness and superiority of this study. The energy hub is considered a unit where multiple energy carriers can be converted, conditioned, and stored, thereby providing the functions of input, output, conversion, and storage of multiple energy carriers using a defined coupling matrix. Thus, the energy hub is widely used in the planning and operation of integrated energy systems. However, the coupling factors (or the efficiencies of the energy conversion devices) in the energy hub coupling matrix are usually assumed to be constant for the sake of simplicity, which may result in unreasonable planning and operation schemes for the integrated energy system. For this reason, an integrated energy system planning method at the community level that considers varying coupling factors was developed in this study. First, a dynamic energy hub model was developed, where an efficiency correction model was built to determine the time-varying coupling factors with the variation in load rate. On this basis, a double-layer planning model was built to determine the optimal planning and operation schemes for the integrated community energy system. A typical integrated community energy system was employed as a test system to illustrate the effectiveness of the planning method, and the results were analysed. [ABSTRACT FROM AUTHOR]

• A belt conveyor system based virtual energy storage model was developed. • An optimal scheduling model of belt conveyor integrated with virtual energy storage was developed. • The charging and discharging characteristics of the virtual energy storage were analyzed. • Influence factor like arrival time of trains and ramp rate of the belt speed were analyzed. In order to reduce the high electricity cost of the belt conveyor system in a coal mine, a virtual energy storage model of the belt conveyor system is proposed based on the coal storage ability of silo. Through coordinated control of belt speed, feed rate, silo load rate and arrival time of the train, the virtual energy storage ability of silo is utilized to realize the power balance considering distributed generations in different period in response to the time-varying electricity price. The case study indicates that the optimal scheduling method in this paper can effectively reduce the electricity cost of the belt conveyor by 61.29%, and improve the power consumption level of the distributed generations, which can help realize the green and effective operation of the belt conveyor system. [ABSTRACT FROM AUTHOR]

Integrated community energy systems (ICESs) facilitate stakeholders to collaboratively create profit by planning and operating energy devices, including generation, conversion, and storage systems. However, the coupling and complementarity of multiple energy carriers, along with the comprehensive interaction among independent stakeholders, pose significant challenges in ICES planning and operating. These challenges necessitate balancing overall economic efficiency with individual stakeholder interests. This study investigates the economic interaction between the integrated energy service provider (IESP) and the photovoltaic energy prosumers (EPs) in ICES from a cooperative perspective, focusing on both planning and operating stage. A bilateral planning and operation method for the IESP and the EPs based on Nash bargaining theory is proposed in this paper. The proposed method establishes a cooperative and mutually beneficial relationship between the IESP and EPs, instead of a hierarchical paradigm. This study also presents an analysis of the relationship between the Nash bargaining problem and the social welfare maximization problem, demonstrating that solving the Nash bargaining problem can lead to a social welfare optimum. Additionally, an accelerated privacy-preserving distributed algorithm, in comparison to the central approach, is designed to solve the Nash bargaining problem. Numerical studies based on realistic data reveal that the Nash bargaining-based planning and operation method provides more benefits for both the IESP and EPs, as compared to the non-cooperation game method. • A planning and operation method based on Nash bargaining for ICES is proposed. • The social welfare maximum is achieved through the Nash bargaining solution. • An accelerated ADMM approach is proposed to ensure the stakeholders privacy. • The proposed method increases the profitability of IESP and EPs within the ICES. [ABSTRACT FROM AUTHOR]

In this paper, a novel synchronous off-policy method is given to solve multi-player zero-sum (ZS) game under the condition that the knowledge of system data are completely unknown, the actuators of controls are constrained and the disturbances are bounded simultaneously. The cost functions are built by nonquadratic functions to reflect the constrained properties of inputs. The integral reinforcement learning (IRL) technology is employed to solve Hamilton–Jacobi–Bellman equation, so that the system dynamics are not necessary anymore. The obtained value function is proved to converge to the optimal game values. And the equivalent of traditional policy iteration (PI) algorithm and the proposed algorithm is given in solving the multi-player ZS game with constrained inputs. Three neural networks in this paper are utilized, the critic neural network (CNN) to approach the cost function, the action neural network (ANN) to approach the control policies and the disturbance neural networks (DNN) to approach the disturbances are utilized. Finally, a simulation example is given to demonstrate the convergence and performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a novel fuzzy integral reinforcement learning (RL) based tracking control algorithm is first proposed for partially unknown fuzzy systems. Firstly, by using the precompensation and augmentation techniques, a new augmented fuzzy tracking system is constructed by combining the fuzzy logic model and desired reference trajectory, where the solution of actual working feedback control policy is converted into a virtual optimal control problem. Secondly, to overcome the requirements of exact original system information, the integral RL technique is utilized to learn the fuzzy control solution, which relaxes the repeatedly transmissions of system matrices during the solving process. Thirdly, compared with the existing standard solution, some crucial and strict aforementioned assumptions are removed and the system can be partially unknown by using the designed algorithm. Besides, under the novel fuzzy control policy, the tracking objective is achieved and the stability is guaranteed by Lyapunov theory. Finally, the developed integral RL tracking control algorithm for partially unknown systems is applied in a mechanical system and the simulation results demonstrate the effectiveness of the proposed new method. [ABSTRACT FROM AUTHOR]

In order to determine the optimal day-ahead scheduling schemes of the integrated community energy system (ICES), a two-stage multi-objective scheduling method (TMSM) was proposed, which consists of a multi-objective optimal power flow (MOPF) calculation stage and a multiple attributes decision making (MADM) stage. Firstly, the electric distribution network, the natural gas network and the energy centers (ECs) of the ICES were modelled. Secondly, five typical indices are considered to characterize the operation of ICES, namely the operation cost ( OC ) and total emission ( TE ) of ICES, the power loss ( PL ) and sum of voltage deviation ( SVD ) of electric distribution network, the sum of pressure deviation ( SPD ) of natural gas network. In order to tackle the computation problems resulted by the increasing number of objectives, the dimension reduction of objectives is employed. The indices of OC and TE are selected based on the analytic hierarchy process (AHP) method and set as the objectives at the MOPF calculation stage. Thirdly, all the five indices are considered during the MADM stage to determine the final day-ahead scheduling schemes from the alternative solutions obtained in MOPF. Numerical studies demonstrate that the TMSM is able to provide flexibility for the operation of ICES. The determined optimum day-ahead scheduling schemes are capable of satisfying and balancing operational needs in aspects of security, economy and environmental friendliness. [ABSTRACT FROM AUTHOR]

A two-stage hierarchical Microgrid energy management method in an office building is proposed, which considers uncertainties from renewable generation, electric load demand, outdoor temperature and solar radiation. In stage 1, a day-ahead optimal economic dispatch method is proposed to minimize the daily Microgrid operating cost, with the virtual energy storage system being dispatched as a flexible resource. In stage 2, a two-layer intra-hour adjustment methodology is proposed to smooth the power exchanges at the point of common coupling by coordinating the virtual energy storage system and the electric vehicles at two different time scales. A Vehicle-to-Building control strategy was developed to dispatch the electric vehicles as a flexible resource. Numerical studies demonstrated that the proposed method is able to reduce the daily operating cost at the day-ahead dispatch stage and smooth the fluctuations of the electric power exchanges at the intra-hour adjustment stage. [ABSTRACT FROM AUTHOR]

To improve the prediction accuracy and prediction speed of battery remaining useful life (RUL), this paper proposes an improved light gradient boosting machine (LightGBM)-based framework. Firstly, the features from the electrochemical impedance spectroscopy (EIS) and incremental capacity-differential voltage (IC-DV) curve are extracted, and the open circuit voltage and temperature are measured; then, those are regarded as multi HIs to improve the prediction accuracy. Secondly, to adaptively adjust to multi HIs and improve prediction speed, the loss function of the LightGBM model is improved by the adaptive loss. The adaptive loss is utilized to adjust the loss function form and limit the saturation value for the first-order derivative of the loss function so that the improved LightGBM can achieve an adaptive adjustment to multiple HIs (ohmic resistance, charge transfer resistance, solid electrolyte interface (SEI) film resistance, Warburg resistance, loss of conductivity, loss of active material, loss of lithium ion, isobaric voltage drop time, and surface average temperature) and limit the impact of error on the gradient. The model parameters are optimized by the hyperparameter optimization method, which can avoid the lower training efficiency caused by manual parameter adjustment and obtain the optimal prediction performance. Finally, the proposed framework is validated by the database from the battery aging and performance testing experimental system. Compared with traditional prediction methods, GBDT (1.893%, 4.324 s), 1D-CNN (1.308%, 47.381 s), SVR (1.510%, 80.333 s), RF (1.476%, 852.075 s), and XGBoost (1.119%, 24.912 s), the RMSE and prediction time of the proposed framework are 1.078% and 15.728 s under the total HIs. The performance of the proposed framework under a different number of HIs is also analyzed. The experimental results show that the proposed framework can achieve the optimal prediction accuracy (98.978%) under the HIs of resistances, loss modes, and isobaric voltage drop time. [ABSTRACT FROM AUTHOR]

Buildings are typically integrated with multiple distributed energy resources (DERs), enabling them to act as building prosumers engaged in both energy production and consumption. Peer-to-peer (P2P) energy trading among building prosumers is crucial to improve their benefits. However, further exploration is required to balance the benefits between building prosumers and the system operator (e.g., the integrated community energy system (ICES) operator) since they are different entities. In this context, this paper proposes a comprehensive network charge and energy sale pricing scheme for the ICES operator on heterogeneous building prosumers with P2P multi-energy trading. The interaction between the ICES operator and building prosumers is modelled as a bi-level optimization problem that belongs to the hierarchical structure, while considering the heterogeneity of thermal insulation performance of buildings. At the upper level, the ICES operator optimizes the electricity/heat network charge prices and electricity/heat sale prices to maximize its revenue. At the lower level, building prosumers with a parallel structure optimize the schedules including P2P multi-energy trading and buildings' heating loads to minimize their costs. Furthermore, to address the bi-level optimization problem with parallel and hierarchical coupling structures, an accelerated asynchronous distributed algorithm based on alternating direction method of multipliers (ADMM) is developed, integrating a warm start strategy and a dual update accelerated iteration strategy for further improving computational efficiency. Finally, case studies demonstrate that the proposed scheme can effectively benefit both the ICES operator and building prosumers in P2P multi-energy trading at the same time. Meanwhile, the feasibility and effectiveness of the proposed algorithm are validated. • An optimal pricing scheme for ICES operator on building prosumers is proposed. • A bi-level optimization is proposed for ICES operator and prosumers with P2P. • An accelerated asynchronous distributed algorithm based on ADMM is developed. • Benefits of ICES operator and prosumers through several scenarios are analyzed. [ABSTRACT FROM AUTHOR]

With the high penetration of the power electronic loads in the grid, the stability of static synchronous compensator (STATCOM) devices is greatly challenged. However, the conventional control methods for the modular multilevel converter (MMC) based STATCOM only consider the stability with small signal disturbances. This article proposes a novel dual-layer back-stepping control (BSC) for the MMC-based STATCOM. In the first layer, the BSC aims to regulate the sum of the capacitor energy and the reactive output current. In the second layer, the BSC aims to control the circulating current. Therefore, the proposed method possesses a fast dynamic response and accurate tracking with the Lyapunov stability of the MMC-based STATCOM. Compared with the arm-control-based BSC for MMC-based inverters, the proposed method has a simplified structure and a reduced computation burden. Moreover, the proposed method realizes the independent control between the output current and the circulating current. The simulation and experimental results verify the effectiveness of the proposed method. In addition, its robustness toward different circuit parameters and the operation ability under unbalanced grid fault is also verified. [ABSTRACT FROM AUTHOR]

The model predictive control (MPC) methods have been widely applied due to the fast dynamic performance and multiple control targets. However, they show certain limits when applied to the multilevel converters, such as unfixed switching frequency, large computation burden, and complex weighting factors selection. This article proposes a novel modulated MPC (M2PC) method for cascaded H-bridge (CHB) converter-based static synchronous compensator (STATCOM). Instead of multitarget integration and weighting factor design, the proposed method disintegrates the voltage balancing control process from the cost function. The evaluated control sets for output current control is decided by the search step and the search range in each period. By proper design and selection of the adaptive search step for the output voltage references under α–β coordinate, the search range can be reduced, which greatly reduces the number of evaluated control sets in each control period. Therefore, the dynamic of output characteristics and the low computation burden of the MPC method are guaranteed simultaneously. In addition, with the simple and independent integration of the capacitor voltage balancing control, the proposed method can be easily applied in the CHB converter-based STATCOM application. Furthermore, this article provides a delay compensation for the prediction process, and it provides a fixed switching frequency for each H-bridge cell. Simulation and experimental results verify the effectiveness of the proposed M2PC method. [ABSTRACT FROM AUTHOR]

• A P2P trading strategy is proposed for the energy balance service provider (EBSP). • The EBSP market power and market elasticity are considered in optimized pricing. • The EBSP could enhance the energy resource allocation ability of the P2P market. • The EBSP could obtain profit without harming the overall benefit of the microgrid. The peer-to-peer (P2P) energy trading technology can effectively promote the mutual exchange and complementation of energy among diversified prosumers in the community microgrid. To further improve the energy resource allocation performance of the P2P energy trading, an energy balance service provider (EBSP) can be involved in the market. EBSP is an independent commercial entity configured with high-capacity energy storage systems (ESS) or auxiliary power sources, participating in the P2P market for profit. The energy shifting ability of EBSP can further improve the overall benefit of the microgrid. However, the scale effect endows the EBSP with comparatively strong market power, which may impose influence on the equilibrium of the P2P market. From the perspective of EBSP, the P2P market can be regarded as a market with elasticity. In the existing researches on optimized pricing and trading strategies of the P2P market, such a characteristic was not fully considered, which may have negative impacts on the economic profit of the EBSP and the overall benefit of the microgrid. Therefore, a P2P energy trading strategy for EBSP considering market elasticity is proposed in this paper. A market equilibrium model for the community microgrid with EBSP is first established, which reflects the elasticity of the market. Then, an optimized pricing model and trading strategy are established and solved to maximize the EBSP's profit. Numerical results show that the proposed method can improve the profit of EBSP, meanwhile reducing the energy dispatching cost of prosumers, achieving peak load shifting and renewable energy self-consumption of the community microgrid. [ABSTRACT FROM AUTHOR]

Low-carbon buildings (LCBs) are normally equipped with distributed energy resources (DERs) such as photovoltaic (PV) panels and batteries, thereby creating LCB prosumers that both produce and consume energy. Peer-to-peer (P2P) energy sharing among LCB prosumers could bring higher economic benefits for themselves, and facilitate better local power balance for the power grid. To fully harness the benefits of P2P energy sharing for both LCB prosumers and the power grid, a bi-level optimization method for LCB prosumers and the power grid operator is proposed in this paper. The grid operator at the upper level imposes the optimal time-varying network charge to LCB prosumers at the lower level to maximize its profit. And LCB prosumers with the objective of minimizing their costs adjust the schedules including P2P energy sharing and their heating loads to respond to the grid operator's optimal network charge prices. Additionally, to further exploit the energy resources including heating loads in LCBs, the model predictive control (MPC) approach is integrated with the bi-level optimization in the presence of uncertainties. The bi-level optimization belongs to the NP-hard category, making it challenging to solve. To reduce the computational complexity, the bi-level optimization problem is converted to the single-level programming using Karush-Kuhn-Tucker (KKT) conditions. Numerical results illustrate that the bi-level optimization method can obtain a balanced scheduling scheme between the grid operator and LCB prosumers. In addition, incorporating the thermal inertia of building heating loads can provide more operational flexibility for both the grid operator and LCB prosumers. Furthermore, the costs of LCB prosumers based on MPC approach can be reduced with the decrease of forecast error level. • A bi-level optimization for grid operator and LCB prosumers with P2P is developed. • The time-varying pricing strategy for network charge of grid operator is proposed. • The bi-level solution method integrated with MPC approach is investigated. • The utilities of bi-level optimization through comparative scenarios are analyzed. [ABSTRACT FROM AUTHOR]

• An assessment model of solar energy accommodation capability is proposed. • The solar collector plays a dominant role in solar energy accommodation. • The transmission delay of the DHN can be fully utilized in the assessment. • An appropriate solar curtailment can help to promote solar energy accommodation. • The influence of the thermal energy storage is more significant thanelectric ones. With the rapid development of global solar energy applications in recent years, it is crucial to establish a scientific assessment method for solar energy accommodation capability to formulate a rational solar energy utilization scheme. With consideration of two typical solar utilization technologies, photovoltaic and solar thermal collecting technology, and the difference between the energy grades of electricity and heat, new assessment indicators are proposed to investigate the accommodation capability of solar energy. Based on the proposed assessment indicators, an assessment model is carried out to determine the solar accommodation capability of district integrated electrical and heating systems (DIEHSs) considering transmission delay of district heating network (DHN), which can inform the development of an optimal solar accommodation scheme. The effectiveness of the assessment model is verified through a DIEHS consisting of an IEEE 33-bus system and a DHN in China. Finally, specific strategies for enhancing solar energy accommodation capability are given by analyzing the influences of transmission delay of DHN, operating temperature range, solar energy curtailment rate, and energy storage on solar energy accommodation capability. [ABSTRACT FROM AUTHOR]

With the rapid development of wind industry, its integration causes inevitable and rapid power fluctuations in the power grid. To promote the integration of wind power and investigate the energy storage capability of electric vehicles (EVs), a smoothing control strategy is proposed to smooth the wind power fluctuations considering the battery energy storage system of EVs (EESS). Based on a proposed maximum operation area of an individual EV considering its traveling behaviors and traveling comfort level, the response parameters are defined to describe the response characteristics of the EV aggregator (EVA). The EESS can operate as an energy storage system composed of several EVAs with different response parameters. A smoothing control strategy is then developed to smooth the wind power fluctuations with the EESS. Considering the power fluctuation rate and the response capability of the EESS, the required power change of each EVA in the EESS is determined in the smoothing control. Then the required power output change is allocated to individual EVs in the EVA with the developed SOC‐adaptive method and the power output limits constrained by the maximum operation area of each EV. Comparative results are provided to validate the effectiveness of the proposed smoothing control strategy. The EESS is able to smooth the wind power fluctuations, and the power output limits and the traveling comfort levels of individual EVs are ensured with the SOC‐adaptive method and the constraints from the maximum operation areas. [ABSTRACT FROM AUTHOR]

With the integration of multiple types of loads and renewable generations, the number of system states significantly grows. As a result, running optimal power flow (OPF) to analyze a myriad of system states is challenging and this seriously restricts the efficiency of the state enumeration method. To address that, this paper proposes a Lagrange Multiplier based State Enumeration (LMSE) approach to accelerate the analysis without loss of accuracy. The core idea is to directly obtain the optimal load shedding of contingency states by Lagrange multiplier-based functions, rather than the time-consuming OPF algorithms. This approach can also be conveniently integrated with the impact-increment method and the clustering technique for further efficiency enhancement. Case studies are performed on the RTS-79 and IEEE 118-bus systems considering multiple types of loads, photovoltaics (PVs), and wind turbines (WTs). Results indicate that the proposed method can significantly reduce the computing time without compromising the calculation accuracy. [ABSTRACT FROM AUTHOR]

• A T-VESS is considered in real time P2P energy trading. • A two-stage P2P energy trading method is proposed based on MPC. • Trading actions are implemented considering multiple trading preference grades. • The proposed method is effective and utilities for prosumers are much improved. With the increasing penetration of distributed energy resources (DER) in the electric power system, Peer-to-Peer (P2P) energy trading has become a promising paradigm for future electric power systems. Building thermal load, which is an important demand side resource, should be considered carefully in the design of a P2P trading method. In this paper, we investigate the application of building thermal energy storage capability in P2P energy trading. We aggregate and model building thermal loads as a virtual energy storage and derive a time-varying virtual energy storage system (T-VESS) model to quantify the flexibility of a building. Key parameters of T-VESS, including charging/discharging rate, energy capacity, and state of charge (SOC), are analyzed so that T-VESS can be embedded in the building prosumer model to participate effectively in electricity-oriented P2P energy trading. We propose a real time P2P energy trading method of prosumers based on model predictive control (MPC). This method consists of an energy supply and demand quantification stage and a transaction price optimization stage, which can effectively capture the time-varying nature of T-VESS. To preserve the trading preference for prosumers, we propose a distributed P2P energy trading actions implementation method based on the continuous double auction (CDA) considering multiple trading preference grades. Numerical results demonstrate that the proposed P2P energy trading method considering T-VESS can reduce the operational cost of prosumers by 3.7%, while also promoting the local integration of renewable energy by 3.1%. Furthermore, compared to existing P2P trading methods considering single preference grade, the proposed method greatly enhances the utilities of both individual prosumers and the overall community. [ABSTRACT FROM AUTHOR]

Regional integrated system (RIES) bridges the gaps among different energy systems by various energy conversion equipment. While this integration brings forth numerous benefits, it introduces cross-system failures, where a contingency in one system may affect others. This poses significant challenges when evaluating the consequences of such contingencies. To address this, a risk assessment approach is developed for RIES, considering cross-system failures, as well as fluctuations in renewable generation and multi-energy loads. Event tree analysis (ETA) is adopted to simulate the propagation of failures and identify the affected components. Nine risk indices are proposed to quantify the operational risk of RIES at various levels. Additionally, root cause identification and weak point location are developed to formulate risk alleviation strategies. To validate the proposed method, a RIES with 101 nodes is utilized for risk assessment, and multiple risk alleviation strategies are determined. The results demonstrate the effectiveness of the proposed method, providing valuable insights for operators in ensuring the safety of RIES. • RIES risk assessment approach quantifies uncertainties of component failure and source-load fluctuation with nine indices. • Cross-system failures in RIES are classified into three categories to describe the interaction between subsystems. • Techniques for root cause identification and weak point location are proposed to alleviate risk. [ABSTRACT FROM AUTHOR]

Simulating the output performance of a proposed photovoltaic plant is a necessary phase prior to the construction stage and has been intensively studied for decades. The requirement for accurate and reliable simulation needs a comprehensive analysis of the photovoltaic power generator considering environmental factors and device behaviors. Unlike other published work, this paper presents an analytical approach to simulate the output performance of photovoltaic arrays using coupled multi-physics modeling without the support of historical data. The interplay among the electrical and thermal models, device characteristics, and device topologies are deeply investigated and formulated. In the electrical model, a parameter extraction method, as well as an iterative algorithm, are utilized on the equivalent circuit model for a quick solution of the I–V characteristics of the photovoltaic module. To obtain accurate results of the output power and its interaction with the environment, irradiation, temperature, humidity, wind speed, and wind direction are analyzed along with the photovoltaic output power in the thermal model. Meanwhile, this output power is constrained to the maximum power point tracking mechanism of the inverter. Real data from a photovoltaic plant are used for comprehensive analysis in both long-term and short-term scenarios. The experiment shows that the proposed approach is competitively accurate and feasible. Compared with the state-of-the-art studies, the proposed approach improves the simulation accuracy by 7.75%. • Coupling the environment factors improved the accuracy of PV module thermal analysis. • Power output and inverter MPPT mechanism are feedback to the multi-physics model. • Applicable to the planning phase of power station, no historical data required. [ABSTRACT FROM AUTHOR]

• A distributed Peer-to-Peer (P2P) energy transaction method is proposed. • A bi-level optimal transaction model is proposed for autonomous prosumers. • A real-time double auction market with continuous bidding is established. • The proposed method is effective and achieved privacy preservation and robustness. As massive integration of Distributed Energy Resources (DERs), the role of end-users in the Urban Community Microgrid System (UCMS) has transformed from traditional consumers into prosumers with capabilities of both energy production and consumption. The exchange of energy between autonomous microgrid prosumers can be achieved with the introduction of Peer-to-Peer (P2P) energy transaction, promoting the efficient allocation of energy in the UCMS. However, the existing centralized P2P energy transaction approaches require microgrid transaction brokers to obtain prosumers' private data, including energy resource configuration, operation status, and energy production/consumption schedule. With the enhancement of prosumers' awareness of privacy protection, it will be increasingly more difficult for the brokers to obtain such private data in practical application scenarios, resulting in obstacles on the implementation of such centralized approach. Thus, a novel distributed P2P energy transaction method based on the double auction market is proposed in this paper. Prosumers first generate the information of energy supply and demand autonomously utilizing distributed energy management model, then set the price targeting profit maximization, and finally initiate P2P energy transaction mutually in the double auction energy market. Compared with the existing centralized approaches, the method proposed in this paper can achieve the coordination and complementarity of energy in the UCMS, promoting economic benefit, energy self-sufficiency, and renewable energy self-consumption without sacrificing privacy preservation and robustness. [ABSTRACT FROM AUTHOR]