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• The paper focused on Energy Internet system. • Peer to peer energy transactions are considered. • The proposed method is based on bus impedance matrix. • The proposed method introduces direct and optimal route selection without computational complexity. • The proposed method approved its efficiency on IEEE systems. The Energy Internet (EI) has emerged as a promising field within smart grids, addressing capacity limitations and discrepancies in energy resources. It facilitates a peer-to-peer (P2P) energy trading environment for EI prosumers and consumers, reducing reliance on the main grid. To ensure an efficient P2P trading process, it is essential to route energy through the path with minimal power loss optimally. The Energy Router (ER) plays a pivotal role in governing power flow through EI. This paper presents the development of a novel real-time Zbus-based method for directing P2P energy transactions within the EI considering congestion management. The proposed method depends on bus impedance matrix in forming the routing matrix. Firstly, the ER's structure and function are outlined in relation to the network, and then to design the EI's topology using an adjacency matrix representation. Secondly, the proposed method methodology is presented in steps and explained on simple 7-bus microgrid. Additionally, a novel congestion management method is integrated into the routing algorithm, considering transmission line loading and available capacity during route selection. Furthermore, the paper applies a power flow technique to the selected routes to optimize power flow performance. The proposed method demonstrates enhanced directness and accuracy compared to existing approaches. Its efficiency is validated by testing a modified IEEE 14-bus system and the standard IEEE 30-bus system. Importantly, the method successfully identifies the minimum loss path, despite the unnecessity of power loss calculations in the routing algorithm. The key contribution of this research lies in providing a comprehensive and practical solution for P2P energy transactions in the EI, showcasing substantial improvements in accuracy, efficiency, and congestion management compared to previous methods. [ABSTRACT FROM AUTHOR]

This paper introduces multi-use applications to unlock behind-the-meter flexibility by integrating grid-serving capabilities with user-oriented control strategies. We deploy a hierarchical flexibility coordination process in a co-simulation research environment and propose a control strategy prioritizing self-consumption optimization with a secondary focus on grid-serving applications. This approach enhances the viability of behind-the-meter technologies, particularly benefiting battery energy storage systems with high investment costs. It also facilitates active grid operation amid the growing presence of distributed energy resources. The research's key contribution lies in designing an information architecture to efficiently utilize behind-the-meter flexibility for grid-serving processes. The paper demonstrates the feasibility of a multi-use operation, combining user-oriented optimization and grid-serving flexibility provision for local grid congestion management. The implemented control algorithm in a home energy management system achieves grid-serving objectives while maintaining behind-the-meter asset performance. Co-simulation results show that in less than 2.5 % of simulated time steps, transformer utilization exceeds operational limits in both scenarios, indicating the viability of multi-use operation for leveraging new value streams and managing grid congestion simultaneously. • Multi-use applications can unlock flexibility potentials hidden behind-the-meter. • Behind-the-meter flexibility can serve for local grid congestion management. • Stacking grid-serving applications obtains good results regarding utilization. • Multi-use applications can add new value streams to behind-the-meter flexibility. [ABSTRACT FROM AUTHOR]

• This paper proposes a comprehensive optimization program to increase economic efficiency and improve the resiliency. • A demand response program integrated with home energy storage systems is presented to optimize energy consumption. • To modify the consumption pattern of household consumers, a real-time pricing algorithm is proposed. • A self-healing system reconfiguration program integrated with distributed energy resources is presented. • Real data of California households are considered to model the home appliances and home energy storage systems. This paper proposes a comprehensive optimization program to increase economic efficiency and improve the resiliency of the Distribution Network (DN). A Demand Response Program (DRP) integrated with Home Energy Storage Systems (HESSs) is presented to optimize the energy consumption of household consumers. Each consumer implements a Smart Home Energy Management System (SHEMS) to optimize their energy consumption according to their desired comfort and preferences. To modify the consumption pattern of household consumers, a Real-Time Pricing (RTP) algorithm is proposed to reflect the energy price of the wholesale market to the retail market and consumers. In addition, a Self-Healing System Reconfiguration (SHSR) program integrated with Distributed Energy Resources (DER), reactive power compensation equipment, and Energy Storage Systems (ESSs) is presented to manage the DN energy and restore the network loads in disruptive events. The reconfiguration operation is performed by converting the isolated part of the DN from the upstream network to several self-sufficient networked virtual microgrids without executing any switching process. Real data of California households are considered to model the home appliances and HESSs. The proposed comprehensive program is validated on the modified IEEE 123-bus feeder in normal and emergency operating conditions. [ABSTRACT FROM AUTHOR]

• To solve the problem of regenerative braking energy recovery in traction power supply systems, a techno-economic capacity configuration strategy considering the decoupling control strategy for the interphase- bridging inverter-based three-port topology for railway energy router (IBI-RER) is proposed in this paper. • In the technical model, a multi-layer decoupling control strategy considering capacity constraints is proposed to address the complex coupling relationship between the active and reactive power injected into the traction bus by IBI-RER. • In the economic model, a multi-threshold energy management strategy is suggested to solve the issue of the current single-threshold energy management strategy being unable to reduce energy costs and power demand costs simultaneously. • Additionally, the economic model considers the constraint of payback period, making the project more attractive in practical engineering situations. The interphase-bridging inverter-based three-port railway energy router (IBI-RER) is a promising technology compared to conventional schemes due to its simple architecture and reduced equipment power rating. However, the three-port structure is strongly coupled, resulting in complex interactions among α/β-phase AC-side active and reactive power and DC-side power, which have a significant impact on the system capacity. Therefore, this paper proposes a techno-economic capacity configuration strategy considering the power decoupling control strategy for the IBI-RER. The capacity configuration strategy considers several objectives, including regenerative braking energy (RBE) utilization, power quality, comprehensive benefit, and payback period. To address the strong coupling of active and reactive power, a multi-layer optimal control strategy considering capacity constraints is proposed to ensure that the capacity configuration objectives can be achieved. Furthermore, a multi-threshold energy allocation strategy is designed to target the reduction of the power demand charge and the energy charge, based on the objective of maximizing the comprehensive benefit. The simulations and experiments based on the measured data of a traction substation verify the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

• This paper proposes the research on dimension reduction for visualization of simplified security region of integrated energy system considering renewable energy access. • The imbalance between energy demand and supply caused by the renewable energy uncertainty is defined as a special N-1 fault. • An interval security region model of integrated energy system is constructed, according to the N-1 safety constraints of equipment and pipeline. • An engineering simplification approach for interval security region model is proposed, in order to quantify the security boundary, security distance, and interval total supply capability. • A dimension reduction observation method based on observation variable optimization is proposed, which can enlarge the volume unaffected by renewable energy uncertainty. This paper proposes the research on dimension reduction for visualization of simplified security region of integrated energy system considering renewable energy access. Reason: Integrated energy system is conducive to achieving low-carbon transition and efficient utilization of energy. As the penetration of renewable energy increases, the security of integrated energy system faces severe challenges. It holds significance for comprehensively studying N-1 security test method in the operation scenario of integrated energy system, the impact of renewable energy on the IES security region model, and the optimal selection for observation variables. Methods: Firstly, a model for interval security region of integrated energy system is constructed, according to the N-1 safety constraints of energy hub key equipment and pipeline. The imbalance between energy demand and supply caused by the renewable energy uncertainty is defined as a special N-1 fault. Then, an engineering simplification approach for interval security region model is proposed, in order to quantify the security boundary, security distance, and interval total supply capability. On the basis, a dimension reduction observation method based on observation variable optimization is proposed, the selection of observation variables help to expand the volume of area unaffected by renewable energy uncertainty. Results: To the existed problem in current research, an example is presented to verify the effectiveness of proposed method, which brings forward N-1 security test method in the operation scenario of integrated energy system, analyzes the impact of renewable energy on the security region model, and realizes the optimal selection for observation variables of simplified security region. Conclusions: Interval security region of integrated energy system can intuitively estimate the security status of operating points and observe the security boundaries. The simplified model can satisfy the needs of calculating precision. Optimal selection of observation variables is necessary, and the volume of area unaffected by renewable energy uncertainty can be effectively increased. [ABSTRACT FROM AUTHOR]

• Risk-based procedure for managing a distribution transformer by its dynamic rating. • Formulation of an admissible risk level with an intuitive physical meaning. • Quantile regression forests to forecast transformer load and ambient temperature. • Numerical experiments are based on actual industrial data to validate the proposal. • Results are provided from energetic and economic perspectives. Electrical distribution networks are often inadequate to comply with the increasing load demand and to enable new installation of distributed energy resources. This inadequacy may result in upgrading existing transformers of in disconnecting the entire load during demand peaks, and into wasting a portion of the total renewable energy produced during favorable weather conditions. Increasing the capacity of electrical distribution network components is therefore mandatory in order to avoid these negative circumstances; however replacing lines and transformers is costly, and often the benefits are not worth the investments. Fortunately, the capacity of distribution networks can be unlocked by managing the electrical components by their dynamic ratings, allowing operating them even beyond their rated ampacity for a limited interval of time. This paper proposes a new risk-based procedure to manage distribution transformers by their dynamic rating. Probabilistic forecasts of loads and ambient temperatures are the inputs of the procedure, which are obtained in this paper from quantile regression forests. The procedure quantifies, in a probabilistic framework, the insulation thermal ageing due to the transformer (over)load in terms of the cost sustained for rewinding the transformer. The corresponding estimated risk is made available to the distribution system operator, who may therefore take a decision comparing it to an arbitrary level of risk coverage, favoring either the power delivery to customers or the reduction of the transformer ageing rate; this level is formulated in this paper having an intuitive physical meaning, thus enabling its setting also by non-expert operators. Comprehensive numerical experiments based on actual data are presented under different configurations and scenarios, in order to validate the proposal from an energetic and economic point of view. [ABSTRACT FROM AUTHOR]

• Presenting a linear thermal model for a Li-ion battery using McCormick envelopes. • Analyzing the generated heat during continuous charging and discharging. • Optimal operation and controlling the temperature of a large-scale battery pack. The growing adoption of microgrids necessitates efficient management of electrical energy storage units to ensure reliable and sustainable power supply. This paper investigates a thermal management system (TMS) for maintaining the longevity of large-scale batteries. To streamline the thermal modeling of batteries, the McCormick relaxation method is employed to linearize a nonlinear and interdependent heat generation model. The thermal model of the battery follows a nonlinear behavior where the generated heat makes the battery system temperature soar, thereby affecting the thermal performance of the battery. To showcase the efficacy of the proposed approach, four distinct case scenarios are studied, highlighting the critical importance of batteries within microgrid operations. A comparative analysis is conducted between linear and nonlinear models for TMS performance. A quantitative assessment based on simulation results demonstrates the precision of the linearized model, particularly in a multitemporal optimal power flow and day-ahead scheduling of microgrids incorporating energy storage units. Controlling the battery temperature within a permissible range (from 15 °C to 40 °C) is achieved by using a heating, ventilation, and air conditioning (HVAC) system. The paper explores the economic implications of energy storage units in microgrids by extracting and comparing daily operational costs with and without battery integration. The findings reveal that the inclusion of energy storage units yields substantial economic benefits, with potential profit margins of approximately 20 % during typical working days and 60 % on weekends. [ABSTRACT FROM AUTHOR]

• This study is the first to comprehensively investigate the conflicting effects of virtual inertia on VSG transient stability under different severities of grid voltage sag, providing new insights into the control of VSG. • Faults, such as grid voltage sags, are classified into two types: transient tolerable faults and transient intolerable faults. The proposed fault-type classification provides a clear insight into the transient impact of virtual inertia on VSG under different severities of faults. • We propose a fault-type identification method based on LVQ neural networks, which can distinguish between the two types of faults within 5 ms, enabling timely control strategy adjustment. The virtual synchronous generator (VSG) control, widely used for voltage source inverters, is prone to the loss of synchronization (LOS) under large disturbances such as grid voltage sags. Despite the significant amount of research that has been conducted on the transient stability of VSGs, the existing conclusions on the influence of virtual inertia under different fault conditions remain controversial. In this paper, the conflicting effect of virtual inertia on VSG transient stability under different severities of grid voltage sag is comprehensively investigated. Faults, like grid voltage sags, are classified into two types in this paper: transient tolerable faults and transient intolerable faults. Moreover, a unified control framework for rapid fault-type identification and transient stability improvement is proposed. The Learning vector quantization (LVQ) neural networks are used to distinguish fault types within 5 ms. Based on the fault-type identification results, the proposed control strategies are applied to enhance the transient stability of VSG. Finally, simulations and control-hardware-in-loop experimental validations are carried out to demonstrate the effectiveness of the proposed framework. [ABSTRACT FROM AUTHOR]

This paper investigates the energy management of an Energy Internet (EI) that integrates multiple energy networks. Based on the structure of EI and the fluctuation of load, an optimal energy management model considering part of the load as time-varying factor is proposed. Under the influence of load, the actual energy management problem (EMP) is formulated as a time-varying optimization problem subject to a set of global and local constraints. Its optimal solution varies continuously with time, which leads to difficulties in obtaining optimal solutions by conventional algorithms. Consequently, to obtain better tracking performance, this paper proposes a distributed inertial projection neurodynamics algorithm (DIPNA) based on the Nesterov accelerated gradient descent method. This algorithm tracks the optimal solution of the EMP with a fast convergence rate O 1 / t 2 , providing a referenceable active power for each energy unit in real time. Finally, the performance evaluation results demonstrated the effectiveness and fast convergence of the proposed algorithm. • Collaborative management of multiple energy sources throughout the energy internet system is achieved. • The model takes into account the fluctuation of load • Proposes a distributed reach dynamics algorithm with fast convergence characteristics. [ABSTRACT FROM AUTHOR]

• The proposed single-ended method uses the extra information brought by the time interval between the operations of the circuit breakers at two line ends; the proposed method also considers distributed line parameters. Therefore, the proposed method has higher fault location accuracy than the existing method. • The proposed formulation of the fault location problem with distributed parameters is subtly simplified by Taylor expansion, and a non-iterative fault location method is proposed. The risk of unconvergence is systematically avoided and the fault location accuracy is ensured at the same time. • The proposed method does not rely on the assumptions of legacy methods (e.g., the fault current and the terminal current are in the same phase, the system is homogeneous, etc.). The proposed method does not require the parameters of the remote power system. Fast and accurate fault location methods can save costs and ensure power supply reliability. This paper proposes a novel noniterative single-ended phasor domain fault location method with distributed parameter model for AC transmission lines. Due to the limitation of information, the legacy impedance-based single-ended fault location methods usually are based on assumptions on the line or system to obtain more information. The method proposed in this paper takes advantage of the additional information brought by the operation mode of circuit breakers. The proposed method only needs the local measurements and does not need the measurements and the equivalent source parameters of the remote system. The proposed method only uses single-ended data and is not affected by communication and synchronization. The proposed method is noniterative and there is no risk of non-convergence. In addition, the method proposed in this paper does not rely on the assumptions of legacy methods and considers the distributed parameters, avoiding the disadvantages of the legacy methods. The effectiveness of the proposed method is verified through a lot of case studies in PSCAD/EMTDC, with different fault types, fault locations and resistances. © 2017 Elsevier Inc. All rights reserved. [ABSTRACT FROM AUTHOR]

• This paper analyses the drawback of conventional FI-based FSI schemes. • A fuzzy-based method to provide the quantified possibility of fault is presented. • An FSI method using dynamic partial trees is proposed for reducing the FSI time. • This paper suggests a framework for the proposed algorithm in the DAS environment. • The proposed method guarantee accurate and fast FSI with a lack of FI information. In a distribution management system (DMS), fault section identification (FSI) is an essential function for the self-healing capability of smart distribution networks. Conventional FSI methods based on the flag of fault indicators (FIs) cannot be properly utilized for distribution networks with distributed energy resources (DERs) since false FIs, unintentionally activated by fault currents flowing from the downstream of a fault location due to the presence of wye-delta transformers for the DER interconnection, are expected to be generated. For more accurate FSI, this paper proposes a fuzzy-based FSI method using the fault current information measured on feeder remote terminal units (FRTUs) installed at automatic switches in distribution lines (DLs). The proposed method takes advantage of the fuzzy evaluation to provide an intuitive index for decision-making by quantifying the possibility of fault for each candidate section. Furthermore, to achieve faster FSI by reducing waiting time for FSI, the partial trees using only validated FIs with fault current information for the faulty DL are dynamically generated, thereby enabling the fault section can be identified regardless of whether the fault current information from every FRTU is available or not. Case studies on IEEE 33-bus distribution system considering various fault conditions are conducted and simulation results show that the proposed method can accurately identify correct fault sections under false FI and communication failure situations with the improved time required for the FSI. [ABSTRACT FROM AUTHOR]

In this paper, a novel photovoltaic (PV) system planning framework is proposed for distribution grids. The main contribution of this work is that the volt-var control (VVC) capability of PV inverters is duly considered during the planning stage to reduce the expected power loss cost and meanwhile counteract uncertain voltage fluctuation and deviation caused by random PV production and load demand. Specifically, the proposed PV system planning framework is formulated as a two-stage stochastic optimization model, where the first stage is to determine the planning decisions of PV systems before the uncertainty realization, and after the uncertainty is observed, the second stage coordinates PV inverters and existing legacy VVC devices (capacitor banks) to minimize the power loss and voltage fluctuation in a multi-timescale manner. To solve the formulated complex optimization problem, the original model is decoupled and solved using an iterative solution method where commercial solvers can be directly used to obtain the optimal solutions. Besides, a data-driven based scenario selection method based on Generative Adversarial Network (GAN) is proposed to capture the uncertainties of PV production and load demand with the full diversity of behaviours. Finally, the proposed framework is tested on IEEE 37-node and 123-node test systems to demonstrate the effectiveness of the proposed method. This paper can provide important insights into investment strategies of renewable resources designed by power grid authorities, which can benefit from the proposed framework to maintain the reliable system operation and reduce the renewable energy curtailment. [ABSTRACT FROM AUTHOR]

Reducing transient effects of power quality in the distribution network of a microgrid requires a better understanding of the dynamics of power flow and distributed energy resources (DERs) present within the microgrid. Instead of labor intensive and detailed physics-based distribution network and DER modeling, this paper shows how power flow dynamics can also be modeled in a dynamic equivalent with a data-driven approach that exploits the time dependent covariance of power flow at different locations within a microgrid. The proposed approach results in multi-input multi-output (MIMO) linear time invariant (LTI) discrete-time filter concatenated by a static nonlinearity to model both dynamics and power losses. Estimation of the MIMO LTI dynamics is done by the Covariance Based Realization Algorithm (CoBRA) that can handle noisy perturbed measurements and provide a low order MIMO LTI filter with a normalized steady-state gain. The additional static nonlinearity is found by the estimation of the admittance matrix of the distribution network within the microgrid. The main contribution of this paper is proposing a physics-informed data-driven approach to modeling microgrid power flow dynamics, where the CoBRA method is implemented to learn parameters from data while observing a priori physical constraints. The approach is illustrated on experimental data from a microgrid with multiple DERs and it is shown that an excellent agreement in power flow dynamics is obtained over a large operating range, which demonstrates its advantage over the N4SID method. • A data-driven approach to modeling microgrid power flow dynamics is proposed. • The discrete-time covariance between active and reactive power flow is leveraged. • A multi-input multi-output linear dynamic model with constraints is considered. • A static nonlinear part to model power loss is formulated. • Both simulation and experimental results demonstrate high prediction accuracy. [ABSTRACT FROM AUTHOR]

An active power oscillation damping (APOD) control is one of the representative unbalanced fault compensation strategies. Recently, various studies have been performed on the effect of the APOD with the internal power dynamic analysis of a Modular Multilevel Converter (MMC); however, most of the papers have considered only the active power component for analyzing the internal dynamics of the MMC. During the APOD operation, while reactive power is supplied to the grid for the voltage compensation, phase-leg power imbalance can be aggravated. To enhance stability and reliability, the phase-leg power imbalance phenomenon should be sufficiently considered at the development stage of the controller. This research discovers that the phase-leg power imbalance rate could be determined by the magnitude and phase of the negative sequence component of the grid voltage and the amount of reactive power. In addition, the unbalanced power component can be accurately calculated only based on the grid voltage information and the reactive power reference. Also, this paper proposes a phase-leg power equalization method (PPEM) using unbalanced power component estimation. Furthermore, an improved control structure is also proposed to achieve more accurate phase-leg power equalization using the internal leg-energy dynamics analysis. The effectiveness of the proposed methods is verified through high-fidelity PSCAD/EMTDC time-domain simulations using Point-to-Point (PTP) MMC-HVDC system and Hardware-in-the-Loop Simulation (HILS) with the MMC-MVDC system. • The phase-leg power imbalance phenomenon occurs by the current component corresponding to the reactive power in MMC system under APOD strategy. • The phase-leg power imbalance rate is related to the amount of the reactive power and the grid voltage imbalance rate. • The tendency of the phase power imbalance according to reactive power and voltage dip factor is analyzed under the APOD strategy. • Based on the analysis of the phase-leg power imbalance phenomenon, a phase-leg power equalization method that can achieve precise phase-leg power balancing is proposed. • The effectiveness of the proposed methods is verified through high-fidelity PSCAD/EMTDC time-domain simulations and the HILS test platform. [ABSTRACT FROM AUTHOR]

• A dynamic node admittance matrix of the RPC-network-train system is established. • Resonance impedance/frequency sensitivity (RIS and RFS) are defined. • Resonance impact factor (RIF) based on RIS and RFS can locate the key factors. • RPC is designed by the multi-objective sorting optimization strategy (MOSOS). The coupling of railway power conditioner (RPC), network, and train will change the impedance characteristics of the existing traction power supply systems, which will lead to the unclear law of high-frequency harmonic resonance. This paper aims to study the resonance mechanism of the RPC-network train coupling system, and then solve the resonance problem of the existing traction power supply system from the perspective of reshaping the high-frequency impedance of the system. Firstly, the RPC-network-train coupling system's dynamic node admittance matrix is established. After that, a comprehensive resonance evaluation method is proposed to obtain the resonance impact factors (RIFs) of multiple virtual impedances in the control strategy and the filtering parameters, which can realize the comparison and selection of five existing impedance reshaping strategies. To ensure the safe and stable operation of the RPC-network-train coupling system, a multi-objective sorting optimization strategy (MOSOS) considering resonance suppression, harmonics filtering, filter cost, and control system stability is proposed for the RPC based on the RIFs. Finally, the effectiveness of the proposed method is verified by the simulation result in MATLAB/Simulink and the experiment result in the StarSim hardware-in-the-loop (HIL) platform respectively. [ABSTRACT FROM AUTHOR]

[Display omitted] • Improvement of IPP's benefit structure using power to X stations. • Incorporating uncertainty with chance constrained two-stage stochastic optimization. • Contribution analysis according to the hierarchical level of power to X stations. • Enhancing energy operational efficiency through power to X utilization. The carbon neutrality policy aiming for net zero carbon emissions has led to a significant increase in the use of renewable energy sources (RES) globally. However, due to their uncertain nature, RES can cause imbalances in power demand. Recently, Power-to-X (P2X) station technology has gained attention as a solution to the uncertainties of RES and as a means to enhance the capacity and efficiency of RES operations. P2X stations can be utilized when power demand imbalances occur due to the uncertain output of RES or when the power system cannot accommodate the power supply from RES due to various stability issues. Specifically, when supply disruptions occur in the power system due to RES uncertainties, P2X stations contribute to preventing RES curtailment by supplying power to electric vehicle (EV) fuel sources, producing heat using electric heat pumps (EHP), or producing hydrogen using electrolyzers (ELZ), thus improving the uncertain financial benefits for independent power producers (IPP). This paper proposes a mixed-integer linear programming (MILP) based chance-constrained two-stage stochastic optimization (CCTS) approach to address imbalances in power demand from RES and to enhance the profitability of IPP by finding the optimal planning and operational solutions for P2X stations. The proposed method provides hierarchical level results, demonstrating that economic benefits can increase by up to 60.2% with the application of P2X stations and that curtailed energy from RES can be reduced by up to 76.5%. The proposed methodology is also validated for its superior performance by being compared with both the non-linear stochastic chance constraint method and the stochastic method. [ABSTRACT FROM AUTHOR]

Proliferation of distributed energy resources (DERs) and proactive consumers has mooted over the pre-existing pricing mechanisms for active distribution networks (ADNs). This entails the need for a pricing mechanism in conjunction with cost-effective ADN operations. Locational marginal price (LMP) is a well-established pricing mechanism of the day-ahead wholesale market in most countries and provides economic signals and incentives to market participants. However, the alternating current optimal power flow (ACOPF) model (being non-deterministic polynomial-time hard) has inherent complexities and convergence issues. Besides, the approximations involved in its implementation for transmission networks may not be applicable to ADN due to their technical and structural differences. Hence, a distribution LMP (DLMP) model is indispensable for the evolving ADN. This paper proposes a network-dependent sensitivity-based branch-flow quadratic OPF model for evaluating active and reactive power DLMPs of ADNs. The DLMPs are calculated using the sensitivities and dual variables of the OPF model, which consist of incremental costs for energy, loss, congestion, and voltage components. These signals would offer an equitable price for each DER, accounting for their contribution to network conditions. The efficacy of the proposed model has been elucidated on the 33, 69, 118, and 141-node ADNs, and the results are compared with five state-of-the-art models. • A quadratic approximated branch-flow OPF model based on network-dependent current sensitivities is proposed for ADN. • The model considers real and reactive power prices, voltage limits, power losses, and line limits, employing sensitivities to formulate power flow and loss equations. • Explicit modeling of active and reactive power DLMPs and their components associated with energy, loss, voltage, and congestion, using the sensitivities and dual variables of the proposed OPF model. • Case studies on various ADNs to validate the accuracy and effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

The characterization of the competitive interdependence among power consumers in power trading system contributes to pursuing efficient power supply and suppressing peak–valley load differences. Even though Stackelberg–Nash game approach has been applied to retail electricity trading with a single utility company and a group of end-users under competitive interdependence, the extension to multi-cluster retail electricity markets is not focused yet and the best strategies of end-users are still decoupled to each other. To bridge this gap this paper investigates the price-based demand response problem in multi-cluster retail electricity markets to coordinate the energy consumption behavior of the end-users and the retail price behavior of the utility companies. Adopting the structure of hierarchical game theory, the cluster retail price is controlled by the utility company to affect the energy consumption demand of end-users within the corresponding cluster and hence increase the income of both the utility companies and the end-users. In order to describe the coupled competition relationship among the bottom end-users in the system, the Cournot competition model of the differentiated oligopoly market is constructed by taking energy consumption as the individual strategy of end-users. The existence and uniqueness of Nash equilibrium and Stackelberg Nash equilibrium in such a hierarchical game system are proved, and a seeking algorithm is proposed to seek the Stackelberg Nash equilibrium, which is regarded as the extension from the existing algorithm for multi-cluster setup. This work gives an insight to theoretically check whether the utility companies need to redesign the contract with the power generator to ask for larger maximum supply. • A power-system trading model for multi-cluster retail electricity markets is presented, where both the utility function and its best-response strategy are mutually independent of the game participants. • Using the backward induction, the existence and uniqueness of Nash equilibrium and Stackelberg Nash equilibrium in such a hierarchical game system is shown with a proper seeking algorithm. • The influence of the retail price of the utility company and the cap price of end-users in multi-cluster retail electricity markets are examined for the first time using a Cournot oligopoly game. • The proposed model helps to theoretically check whether the utility companies need to redesign the contract with power generator asking for a larger maximum supply. [ABSTRACT FROM AUTHOR]

• Adding distribution network reliability constraints to the CVPP scheduling framework; • WCVaR used to quantify the uncertainty of renewable energy outputs; • Fully excavate the scheduling potential of flexible loads; • Completed sensitivity analysis for different risk confidence levels. Commercial virtual power plant is one of the most efficient means to solve the problem of multi-type distributed power supply and flexible load scheduling. Aiming at the problems of uncertain renewable energy sources and reliable distribution system, a flexible interactive coordinated control method for commercial virtual power plants based on worst-case conditional value-at-risk (WCVAR) is proposed in this paper. In order to reduce the risk of voltage exceeding limits and increased network losses at distribution network nodes, combined with the requirements of technology virtual power plants for power flow control technology in distribution networks, a commercial virtual power plant net profit model based on the reliability of distribution network operation is established. The worst-case conditional risk value theory is used to quantify the risks caused by the uncertainty of wind, water and photovoltaic outputs. A commercial virtual power plant optimization scheduling model based on the WCVAR is established with the goal of minimizing risk cost, and the model is solved by commercial software Cplex. By simulating and analyzing the actual operating data of the improved IEEE-33 distribution system, the output characteristics of commercial virtual power plants under different risk confidence levels are compared and analyzed. The simulation results show that this method fully explores the scheduling potential between electricity, power grid, load and energy storage in commercial virtual power plants, flexibly adjusts risk preferences according to scheduling needs, and achieves flexible control of benefits and risks in commercial virtual power plants. [ABSTRACT FROM AUTHOR]

Nowadays, the high penetration of inverter-interfaced distributed energy resources (DERs) has raised a well-founded concern regarding the interaction of these devices with the upstream grid. The harmonic interaction phenomenon between these players can be described by time-domain simulations, using high computational effort switched and/or average models often impractical for large power systems. This paper proposes an innovative low-computational-burden model named passive impedance network (PIN), capable of accurately representing DERs in electric power systems (EPSs). Through passive RLC components, the PIN model can describe DER dynamic behavior at a certain frequency of interest, making it able to represent harmonic flow within the EPS nodes. The proposed model is compared with the switched model, average model, and an experimental setup under different grid distortions and injected current profiles. A comparison of the total demand distortion (TDD) between the proposed PIN model and the experimental results is carried out. The effect of DER parameter deviations on PIN model accuracy is addressed, in which the absolute error of the PIN model TDD values are 0.65% and 0.06% for the experimental and switched model, respectively. Besides, the PIN model presents an improved computational performance compared to the time-domain-based switching model, requiring 99% less computational burden. • Modeling grid interaction of distributed energy resources. • Low computational burden model based on the Norton model. • Introducing inverter representation for harmonic interactions. • Mathematical representation of a single-phase inverter in a steady state. • Grid connected inverter model with large simulation step size. [ABSTRACT FROM AUTHOR]

• A scenario clustering and reduction strategy based on the nodal voltage fluctuation is proposed to obtain the input stochastic parameters of the optimization model. • Considering multiple scenarios of renewable energy generation power, a large-scale stochastic optimization model is developed for optimal scheduling of the virtual power plant. • Based on multi-temporal power flow decoupling, a distributed optimization algorithm is proposed to solve the optimization problem in rolling time domain. The virtual power plants are aggregations of distributed generators, grid-connected devices in user side. The operation of virtual power plants affects the economic benefits and environmental benefits. However, due to the stochastic and fluctuating nature of power generation from renewable energy sources, the optimal scheduling problem for the virtual power plant containing high-dimensional random variables is difficult to solve. The conic power flow constraints are considered in the virtual power plant day-ahead optimal scheduling models to maximize the revenue by virtue of optimizing the flexible resources such as energy storage system and interruptible load. To quantify the uncertainties in the optimization problem, this paper proposes a network state-based power scenario reduction strategy for renewable energy generation, where typical scenarios are selected by the state of the grid voltage. The proposed day-ahead scheduling model is a mixed-integer, nonlinear, large-scale, stochastic optimization problem with high dimensional random variables, which is difficult to be solved directly by traditional centralized method. By temporally decoupling the charging and discharging model of energy storage systems, a distributed optimization algorithm is proposed based on multi-temporal power flow decoupling optimization model. The simulation results show that the proposed distributed optimization algorithm has high accuracy and good convergence, the virtual power plant can achieve day-ahead optimal scheduling and effectively promote renewable energy accommodation under the security and reliability of the grid. [ABSTRACT FROM AUTHOR]

With the massive participation of new energy in the operation of distribution network (DN), the uncertainties arising from both load and renewable generation have brought challenges to its secure and reliable operation. In this paper, a model of day-ahead and intraday optimal scheduling based on dynamic partitioning (DP) of DN is constructed to realize the refinement of operation in both temporal and spatial dimensions, as well as to improve the reliability of system operation. For the strong fluctuation characteristics of the source-load in DN, the DP is achieved under the premise that the partition structure in each period is tight and the internal power supply has the ability to balance the power fluctuations. In order to improve the regional autonomy of DN, a day-ahead multi-objective optimal scheduling model is constructed based on the DP with the optimization objectives of weak inter-partition power coupling, lowest deficiency rate of flexibility, and lowest operating cost. And a scheme for articulation is proposed to ensure the effective guidance of day-ahead for intraday. Then the intraday dispatch model is constructed by shifting the focus to the incorporate characteristics of DP and quickly smooth the net load fluctuations. Finally, the performance and reliability of the proposed models are verified via case studies. • Multi-time scale economic scheduling method based on dynamic partitioning. • Day-ahead scheduling model for reserving flexibility resources for each partition. • Translating day-ahead distribution network flexibility into day-ahead economics. • The energy management is also refined in both temporal and spatial dimensions. [ABSTRACT FROM AUTHOR]

• A multi-microgrid energy real-time optimal control scheduling strategy is proposed. • Energy storage devices can actively participate in optimal energy scheduling. • Improved resilience and flexibility of energy dispatch for multiple microgrid. • Significantly reduce the number of microgrid connections to the distribution grid. • Maximising the full uptake of renewable energy in multi-microgrid systems. With the increasing prominence of the energy crisis and global climate issues, it has become an important cornerstone of the energy revolution to improve the utilization rate of renewable energy and gradually reduce the use of fossil energy. Nonetheless, the erratic fluctuations and instability of renewable energy sources such as wind and solar energy have presented certain obstacles to the effective utilization of renewable energy. In order to maximize the utilization of renewable energy, enhance its utilization efficiency, and reduce the carbon emission of power supply, this paper first proposes a real-time collaborative optimal energy scheduling and dispatching control model for multi-microgrids. Subsequently, it proposes a real-time optimal control and dispatching strategy for multi-microgrid energy based on storage collaborative. This model considers the energy storage device as an energy management controller, enabling it to participate in the energy collaborative dispatch of multi-microgrid. This enables the energy storage device in the microgrid not only to actively participate in the optimal scheduling of energy, but also to make the energy in the storage device be reasonably distributed in the multi-microgrid system, to maximize the full absorption of renewable energy in the multi-microgrid, thereby reducing the dependence of the microgrid on the distribution network, and significantly improving the elasticity and reliability of the multi-microgrid system. Finally, the feasibility and effectiveness of the proposed optimal control scheduling strategy are verified by simulation. [ABSTRACT FROM AUTHOR]

• Based on the local market, a VPP-DSO coordination strategy is proposed. • Providing access to local markets improves the profits of both the VPP and the participants. • The behavior of DERs in the local flexibility market is compensated by a price base on loss-sensitivity. • A VPP operation framework for uncertainty is proposed by applying robust optimization. As distributed energy resources (DER) are increasing, a variety of business models and markets are appearing. Business models such as virtual power plants (VPPs) contribute to enhanced stability of the power system while improving the profits of participating resources. However, the uncertainty of the behavior of DERs and the resulting stability issues are a major barrier to DER deployment and require a coordination framework between grid operators and business models to overcome. Therefore, in this paper, a hierarchical robust day-ahead coordination of a VPP and distribution system operator (DSO) based on the local market is proposed to improve the voltage stability of the distribution network by considering the uncertainty of DERs. For fairness reasons, the contribution of DERs to the distribution system can be addressed by a market-based approach. Local markets are divided into local energy markets (LEM) and local flexibility markets (LFM), which can consider the uncertainty and profitability of DERs. First, the DER determines the seller and buyer status of the LEM based on its self- scheduling with expected prices and is matched based on a double-auction mechanism. At this time, the worst-case scenario of load and renewable energy generation predicted by the Gaussian process (GP) is considered. The mismatched amounts in the LEM are aggregated by the VPP and bid into the wholesale energy market (WEM) operated by the TSO (Transmission system operator). Subsequently, DSO operates a LFM that coordinates microgrid (MG) and VPP on the distribution grid to increase voltage stability. The LFM and WEM problems are considered as interval optimization in the form of mixed integer linear programming and are applied together. The flexibility supply resulting from the adjustment of VPP is compensated by a loss-sensitivity-based flexibility price. Since voltage stability exists in upper and lower bounds, the upper and lower bound of net load through GP-based forecasting is considered along with the linearized network constraints. The results are validated through Monte-Carlo simulation-based scenarios on IEEE benchmark system in MATLAB environment. As a result, it is proved that the proposed model improves the distribution network voltage stability and the profit of the participants. [ABSTRACT FROM AUTHOR]

The growing utilization of wind energy resources has been evidenced to have had substantial positive impacts on mitigating climate change consequences. Unlike stationary wind turbines, mobile wind turbines (MWTs) can travel along the local transportation system (TS) via a truck, supplying power to microgrids (MGs). This spatiotemporal flexibility can provide significant benefits, including enhancing system resilience in the aftermath of high-impact low-probability (HILP) events and contributing to energy system decarbonization. In this paper, we propose a two-stage stochastic optimization model for the service restoration problem by coordinating the routing and scheduling of MWTs with hydrogen storage systems (HSSs) in MGs. The first-stage problem focuses on optimizing the deployment of MWTs based on the shortest-path information obtained through Dijkstra's algorithm. The second-stage problem aims to minimize the expected power outage costs while accounting for wind energy uncertainties estimated through a Monte Carlo simulation approach. Case studies on an integrated transportation and energy network – a central Alabama interstate TS and four IEEE 33-node test power systems – demonstrate the effectiveness of the proposed restoration scheme in boosting MGs resilience and reducing carbon emissions. • Design a two-stage model to assign mobile wind turbines in hydrogen-based microgrids. • Use the shortest-path algorithm to enhance efficiency of MWTs' dispatch decision. • Integrate microgrids and a transportation system, enabling use of wind energy. • Discuss insight on striking balance between power outage costs and carbon emissions. [ABSTRACT FROM AUTHOR]

• An unprecedented very large-scale T&D dataset covering Texas is made publicly available. • The sheer size of this dataset is intended to provide a challenge for the scientific community. • This lays the foundation for demonstrating the coordination of T&D. • It can help foster the improvement of algorithms and tools for years to come. Distributed energy resources, such as rooftop solar, have rapidly expanded in recent years, given declining costs and the desire to reduce carbon emissions. With more energy resources located in the lower-voltage distribution system, it is increasingly helpful to utilize combined transmission and distribution (T&D) system models to analyze interactions between these normally-distinct subsystems. This paper proposes a methodology for creating very large-scale, highly detailed, combined T&D systems that are synthetic—that is, free from non-public data—yet still realistic. The methodology creates very large-scale combined T&D systems by merging the most up-to-date techniques for creating synthetic distribution feeder networks with the latest methods for building synthetic, meshed bulk-power transmission networks. This methodology is demonstrated on a T&D system geolocated in Texas, and benchmarked with co-simulation results. Validation demonstrates that the resulting syn -texas-TDgrid synthetic test system realistically represents characteristics found in actual networks, addressing the lack of available T&D test systems. With over 15,000 feeders and 46 million electrical nodes, this T&D dataset has applications for research in optimal power flow algorithms, voltage control, reconfiguration, and T&D coordination schemes under high adoption of distributed energy resources. [ABSTRACT FROM AUTHOR]

Multiple DC microgrids are interconnected to form a DC microgrid cluster, which can effectively improve the renewable energy consumption capacity and power supply reliability of the microgrid cluster through sharing of energy storage among the participating DC microgrids. However, the existing methods are not able to flexibly coordinate the power flows between DC microgrids connected by a multiport converter. In this paper, a distributed cooperative control method is proposed for a DC microgrid cluster with multiple voltage levels connected by a multi-port interconnected converter. A multi-active-bridge (MAB) converter acts as a bridge for power exchange among microgrids, enabling the sharing of distributed energy storage among microgrids under a sparse communication network. The improved secondary control overcomes the drawbacks of droop control and achieves deviation-free voltage regulation and accurate load/source sharing of the energy storage converters within the cluster. The proposed secondary control can be used to retrofit the existing DC distribution grids to permit access of renewable energy. In addition, the improved voltage observer provides the voltage information of other nodes in a microgrid in addition to the energy storage information, reducing the voltage estimation error caused by the uneven distribution of energy storages. Finally, hardware-in-the-loop (HiL) experiments of the cluster with three DC microgrids verify the control performance, the flexible plug-and-play capability, and the high tolerance of communication failure. • A MAB converter acts as a bridge for power exchange among multiple microgrids. • Achieving DESs sharing in a microgrid cluster under sparse communication networks. • Both the secondary control and the MAB control are fully distributed. • An improved voltage observer enhances the accuracy of microgrid voltage control. [ABSTRACT FROM AUTHOR]

Highlights • The applied control strategy does not depend on any tuning parameter. • The converter does not require any separate output filter. • The series transformer impedance is controlled indirectly by flux (voltage) injection to its secondary. • This system does not require any signal analysis tool for voltage disturbances extraction. • The "smart branch" can conduct the harmonics directionally. Abstract A novel "Smart Branch" is introduced in this paper which compensates for various power quality disturbances. This "Smart Branch" includes a series transformer which its impedance is controlled indirectly by voltage injection to its secondary. By using a finite control set-model predictive controller (FCS-MPC), the "Smart Branch" can be controlled adaptive, flexible and straightforward. The "Smart Branch" is installed at the PCC, where the load current and the PCC voltage can be measured locally without using a communication channel. The "Smart Branch" controller tracks pure sinusoidal waveforms as its references. Thus the "Smart Branch" hardly produces harmonics and therefore its output filter can be eliminated. The controller can be used for multi-objective optimization of power quality in power systems and especially in microgrids. A droop based method is developed for adjusting the objectives' weightings in the cost function of the MPC. In the last part of the paper, by using the extensive simulations and scenarios, the figure of merits of the "Smart Branch" is proved. [ABSTRACT FROM AUTHOR]

Highlights • Optimal grid reconfiguration mechanism is used to handle wind energy uncertainty. • Electric vehicle mobility is modeled as a flexibility tool to transfer energy. • The paper deals with day-ahead electricity energy markets. • Stochastic programming formulation is proposed to model wind energy uncertainty. Abstract This paper investigates the optimal grid reconfiguration (OGR) strategy in the smart grids with the random behavior of the many electric vehicle mobility (EVM) fleets and wind energy uncertainty. The increased utilization of EVMs in the power system with stochastic volatile behavior along with the high penetration of wind energy sources (WESs) can create new challenges in the system that will affect the system flexibility greatly. Here, the electric vehicle is mobile and there are distributed devices with deferrable options to supply energy at various times and locations. Accordingly, in this paper, the OGR is used as the remedial action to enhance the system flexibility to handle uncertainties. The main aim of this paper is to increase system flexibility and reduce expected operating costs by the coordination between EVM fleets, OGR and WESs' uncertainty. Beside, this paper provides a novel method to evaluate the system flexibility while explicitly considering thermal unit generation and the EVM fleets. Numerical tests demonstrate the effectiveness of the proposed approach for analyzing the impact of EVM fleets on the system flexibility and hourly wind energy dispatch. [ABSTRACT FROM AUTHOR]

Highlights • Economic dispatch of MG under AC power flow constraints is studied. • CHP units with non-convex feasible operating zones are utilized. • Robust optimization method is used to consider the uncertainty of market price. • Scenario-based model is used to consider the uncertainty of renewable units. • Information gap decision theory is used to model load uncertainty. Abstract Micro-grids (MGs) are appropriate solutions to various problems in power systems. Different types of energy sources such as renewable energy sources, combined heat and power (CHP) plants and distributed energy resources can be integrated in MGs. Local generation of mentioned energy systems can help system operators to satisfy energy demands with no need to the purchased power from grid. In this paper, a new optimization model based on mixed-integer non-linear programming (MINLP) is proposed to solve economic dispatch of MG containing CHP units, conventional power generators, heat-only units, wind turbines (WTs), photovoltaic (PV) system and battery storage system under uncertainties. The main goal of proposed paper is to find the optimal solution of economic dispatch of MGs in power systems in which technical constraints like AC power flow limitations are taken into account. In the proposed paper, the effects of dual dependency of produced heat and power by CHP units as well as the impact of electrical energy storage and heat buffer tank are studied in the optimal solution of economic dispatch of MGs. Uncertainties of renewable generation units, price of grid power and load are taken into account using scenario-based method, robust optimization approach and information gap decision theory (IGDT), respectively. The proposed model is studied regarding a 14-bus test system in various cases and the results are presented to evaluate the performance of employed methods. [ABSTRACT FROM AUTHOR]

DC is reappearing in the power system – it can be seen on the generation side as solar photovoltaics and wind farms with AC/DC/AC conversion; on the transmission side as HVDC lines and on the consumer side as a variety of modern electronic loads. Power distribution is an area where DC has not yet made any practically extensive appearance, this area is still in research phase. Related to DC distribution, the concept of DC microgrids is also witnessing a significant research contribution in the recent times. One of the research areas besides system control, protection etc., is the energy efficiency of the system. This paper gives an overview of the recent and relatively old research efforts in the field of efficiency/energy-savings analysis for the DC power distribution system. Furthermore, critical analysis of the previous research efforts has been provided and gap in the present body of knowledge related to this field has been identified. The requirements of a comprehensive analysis, mathematical modelling and system designing from a future study in this direction have been mentioned. The findings of this paper can serve as guidance for further investigation and research for the energy savings potential of DC power distribution networks. [ABSTRACT FROM AUTHOR]

• A strategy for distributed Peer-to-Peer (P2P) energy trading was proposed. • A quantification strategy for flexible resources was proposed. • A pricing strategy with privacy and intelligence was proposed. • The impact of renewable energy uncertainty on bidding decisions was considered. • The strategy protects privacy, improves benefits, and enhances local energy balance. With the widespread application of distributed power generation, traditional energy consumers are becoming active prosumers. Giving full play to the role of the electricity market in resource allocation, peer-to-peer (P2P) energy trading between multiple prosumers and consumers helps increase the revenue of electricity sales and reduce the cost of electricity consumption. The participation of flexible resources in P2P energy trading achieves local power complementarity, improves benefits, and reduces the operating pressure of lines. To ensure the benefits of prosumers and stimulate their enthusiasm, it is necessary to propose an effective trading strategy to fully allow flexible resources to participate in P2P energy trading. Therefore, this paper establishes a P2P energy market based on the continuous double auction (CDA) mechanism. Based on the model predictive control (MPC) theory, we propose a minimum cost rolling optimization model to optimize the bidding quantity of flexible resources. Moreover, the Automatic Learning (AL) pricing strategy with privacy and learning is proposed. The AL bidding price strategy enables interactive learning among prosumers. In addition, the influence of bidding price on bidding quantity during the trading process is also considered. Furthermore, this paper considers the impact of renewable energy source (RES) uncertainty on the bidding decisions of prosumers. Finally, the effectiveness of the proposed strategy is verified by the simulation results of cases. The simulation results demonstrate that the proposed strategy can improve the benefits for prosumers, enhance the local balance ability, achieve price learning, and increase the transaction rate. [ABSTRACT FROM AUTHOR]

• Compared with the traditional flexible interconnection method, the EDC-DN power supply architecture proposed in this paper does not simply use MTDC to achieve feeder interconnection but first optimizes and adds dynamic reactive power compensation devices based on voltage source converters in the context of a high percentage of DG access to the grid, and then the dynamic reactive power compensation equipment between different feeders is interconnected on the DC side by adding additional lines, thus weakening the voltage fluctuation of the grid when DG is connected while achieving closed-loop flexible interconnection between multiple feeders. This can achieve closed-loop flexible interconnection between multiple feeders while weakening the voltage fluctuation of the grid when DG is connected, provide a flexible active power adjustment path for high proportional DG access to DN, and realize power mutual aid between different feeders and network-level bearing of DG. • For the issue of power supply recovery after EDC-DN fault, a comprehensive analysis was conducted on the impact of different fault locations on the power supply recovery process. The collaborative plan between EDC and DAS was first proposed, clarifying the information interaction content between the distribution network automation central station, feeder sub-station, and EDC sub-station during the power supply recovery process. An EDC-DN two-stage power supply restoration architecture considering EDC continuous regulation, combinatorial optimization of interconnection switch and sectionalizing switch is proposed and designed, which fills the technical gap in the integration of EDC and DAS systems. • This paper demonstrates the real-time dynamics of EDC in the EDC-DN power supply restoration process through simulation. The restoration process includes a fast restoration phase based on EDC continuous regulation of power supply, a non-fault section transfer process based on mechanical switch action, and an economically optimal operation phase using an optimal combination of switches to reconfigure the distribution network topology. The current power supply restoration strategy of SOP focuses on the final restoration state of EDC continuous regulation and switch combination rather than on the whole process of power supply restoration. This paper considers the real-time dynamics of the entire EDC-DN power supply restoration process. It fills the technical gap in the current SOP power supply restoration without considering the intermediate process. The flexible interconnection device (FID) enables multiple distribution feeders in closed-loop mode to support a higher proportion of distributed generation (DG) and large-scale new loads connected to the distribution network (DN). Moreover, the closed-loop operation mode based on FIDs provides an effective technology roadmap for high-quality and highly reliable power users, especially the quick restoration of power supply after the failure of DN. This paper proposes a new supply shape in which the multi-terminal DC links are flexibly embedded in the AC DN (EDC-DN) to realize closed-loop operation, achieving lower investment costs than the existing mode that replaces tie switches with soft open points (SOPs). Meanwhile, a two-stage power supply quick recovery control technology is proposed for the EDC-DN, including the fast power supply recovery stage based on EDC and the optimizing restoration stage based on network reconfiguration. Firstly, the role of EDC in the process of power supply restoration of three different faults on AC feeder is analyzed. Secondly, a detailed power supply recovery process is proposed, which is based on a distribution automation system (DAS) and the real-time control capability of EDC. Thirdly, the power supply restoration second stage is implemented, which is based on network reconfiguration with switch combination and EDC continuous variable optimization. Finally, a 58-node DN with an EDC system was constructed using PSCAD/MTDC simulation software to verify the effectiveness of the two-stage strategy for EDC-DN power restoration. © 2017 Elsevier Inc. All rights reserved. [ABSTRACT FROM AUTHOR]

Microgrids are useful solutions for integrating renewable energy resources and providing seamless green electricity to minimize carbon footprint. In recent years, extreme weather events happened often worldwide and caused significant economic and societal losses. Such events bring uncertainties to the microgrid energy scheduling problems and increase the challenges of microgrid operation. Traditional optimization approaches suffer from the inaccuracy of the uncertain microgrid model and the unseen events. Existing reinforcement learning (RL) - based approaches are also hampered by the limited generalization and the increasing computational burden when stochastic formulations are required to accommodate the uncertainties. This paper proposes a new parallelized reinforcement learning (PRL) method based on the probabilistic events to handle the microgrid energy uncertainties. Specifically, several local learning agents are employed to interact with pertinent microgrid environments in a distributed manner and report outcomes to the global agent, which will optimize microgrid energy resources online during extreme events. The stochastic microgrid energy optimization problem is reformulated to include all possible scenarios with probabilities. The advantage estimate functions of learning agents are designed with a backward sweep to transfer the outcomes to the value function updating process. Two simulation studies, stochastic optimization and online testing, are performed to compare with several existing RL approaches. Results substantiate that the proposed PRL method can achieve up to 20% optimization performance improvement with 4 and 28 times less computation cost than Q-learning with experience replay and multi-agent Q-learning approaches, respectively. • The paper proposes a new parallelized RL method based on probabilistic events. • The knowledge aggregation assembles the state and action from the local agents. • The global state/action vectors are built for online decision-making process. • The advantage estimate functions of learning agents are designed. [ABSTRACT FROM AUTHOR]

• This paper presents an improved method based on sliding mode control for the pulsed power supply system in DC shipboard microgrids. • The presented method has the benefits of fast dynamic response and robustness. • The effectiveness is verified by comparing with conventional method with Matlab simulation. Pulsed power load (PPL) is a special load type in shipboard microgrids (SMGs), which consists of the generation module, energy storage system, and various types of loads. Having a reliable power supply to shipboard loads is a challenge as the SMG operates in islanded mode in most cases. Particularly, the PPLs require high transient power transfer with fast dynamics and strong robustness. Conventional solution to supply for the PPL is based on proportional-integral (PI) control, which can be used by linearizing the system around the equilibrium operation point. However, for a pulsed power supply (PPS) system, the load demand drastically changes in a short time, usually in millisecond level, making the operating point changes when the pulsed power is triggered or terminated. To supply the PPL with fast dynamics and robustness, an improved PPS control method is presented in this paper. By adopting a nonlinear sliding mode control (SMC) method, fast voltage regulation and robust pulse power tracking can be achieved. In the PPS, the PPL power demand is divided into two terms: one is the average power that is supplied by the SMG and the other is the fast pulsed power that is supplied by the storage capacitor. The size and cost of the storage capacitor are reduced as it is intentionally driven to a deep discharge. The PPS system configuration and coordination principle, SMC controllers, and sizing of passive elements in the PPS are analyzed in detail. The effectiveness of the presented PPS is verified by simulation results. [ABSTRACT FROM AUTHOR]

• Operation mechanism for multi-energy virtual power plant to participate in the peak regulating market is established. • A day-ahead robust bidding model is established to minimize the cost of multi-energy virtual power plant. • Multiple energies such as electricity, cooling energy, thermal energy, and natural gas are involved in the peak-regulation market. Multi-energy virtual power plant (MEVPP) can aggregate flexible resources such as energy storage and flexible loads that decentralized in the region to meet the access conditions in the peak-regulation ancillary service market. However, the uncertainties in energy sources and loads bring adverse impact on the operation of MEVPP. Therefore, this paper proposes a day-ahead robust bidding strategy for MEVPP to participate in the peak-regulation market. Firstly, this paper analyzes the impact of uncertainties for MEVPP on the peak-regulation market. On this basis, the operation mechanism for MEVPP in the peak-regulation market is proposed by considering the integrated demand response (IDR). Additionally, the day-ahead two-stage robust bidding model is established to minimize the operation cost of MEVPP. Finally, the case studies show that the day-ahead robust bidding strategy can effectively reduce the peak-regulation deviation penalty compared with traditional deterministic optimization. Specifically, with the proposed robust bidding strategy, the total revenue in the actual operation stage is increased by 5.16% and 8.45% on sunny day and raised by 8.28% and 15.35% on cloudy day when the predicted deviations are respectively 20% and 30%, comparing with traditional deterministic optimization. [ABSTRACT FROM AUTHOR]

• The subject of this paper is the parameter calculation method of passive balancing compensation in 3P-4 W low voltage systems with unbalanced loads to overcome limitations of compensator parameter algorithm based on CPC. The main contributions of this paper are as follows: • The fundamental frequency current is decomposed from the point of view of parallel compensation, including fundamental optimal working current i w1 , active current i a1 , reactive current i r1 , voltage asymmetry dependent current i d1 , positive-sequence, negative-sequence and zero-sequence unbalanced currents ( i u1 +, i u1 -, i u1 0). • The parameter calculation method of passive balancing compensator oriented the fundamental current components is developed in 3P-4 W systems supplying unbalanced and linear load. Four compensation levels are presented when one or some components of the detrimental current are selected as compensation objects in the parameter calculation process. The fully compensation is achieved in Level 4 where the system works most effectively with the fundamental optimal working current i w1. • The method takes into account the influence of line impedances and nonsinusoidal voltages on the compensator parameter calculation, so it is suitable for four-wire system with symmetrical or asymmetrical, sinusoidal or nonsinusoidal supply voltages. The unbalanced loads in low voltage three-phase four-wire(3P-4W) systems increase the energy and the voltage losses of the power supply system, and even cause serious voltage deviations. The passive compensation method using the Currents' Physical Components (CPC) – based power theory does not consider the voltage losses of phase and neutral line impedances, and it is only suitable for the situations of symmetrical voltage. Under the conditions of supply voltage distortion, load unbalance and the consideration of line impedances, this paper improves the calculation method of passive compensation parameters oriented the current components in four-wire systems, which can make the system work efficiently with the fundamental optimal working current balanced without harmonic amplification. The effectiveness and adaptability of the proposed method to the background conditions are verified, based on the comparison of MATLAB / Simulink simulation results. [ABSTRACT FROM AUTHOR]

This paper presents a new droop-based control strategy for hybrid microgrids (HMG) with improved power sharing. When ac microgrids (AC-MG) and dc microgrids (DC-MG) are present in a distribution grid, there is an opportunity to interconnect them via an interlinking converter (IC) and form a HMG. Power sharing and adequate voltage/frequency control are the main functions of AC-MG and DC-MG control systems in standalone mode. When it comes to a HMG, active power sharing throughout the whole system must also be properly achieved by controlling the IC active power throughput. Furthermore, the possibility of participation of IC in AC-MG reactive power adds some complexity to a HMG control system. In this paper, a new decentralized control strategy is presented for a HMG which relies on regulating the voltage magnitude of a common bus in each microgrid. In this regard, new droop characteristics for sources across both microgrids as well as IC are proposed. The proposed droop characteristics result in better active/reactive power sharing across both microgrids and at the same time results in better voltage regulation. The derivation of new droop characteristics is thoroughly discussed in this paper. Simulation results are used to show the improvement achieved. [ABSTRACT FROM AUTHOR]

This paper proposes a sensor fault tolerant control (FTC) strategy for electronically-coupled distributed energy resource (DER) units in grid-connected microgrid systems. First, a fault dependent dynamical model of the DER unit incorporating sensor faults is proposed. The FTC strategy is then developed using a sliding mode observer (SMO) which has an inherent robustness property. The rational for using a SMO is the need to detect and reconstruct any fault that may occur in output measurements of the system as a result of sensor failure. The fault signals are reconstructed by using a robust approach that encompasses uncertainties in the system, such as frequency variation. This paper also represents the cyber attack as an unknown sensor fault, and consequently the DER current sensor measurements manipulated by a data integrity attack can be detected and reconstructed. The reconstructed fault signals are then used to modify the faulty sensor measurements, which are fed to the voltage source converter (VSC) controller. This ensures accurate generation of pulse width modulation (PWM) signals and consequently accurate tracking of real and reactive power references. Different case studies are tested on a detailed nonlinear model of the DER system. The impact of faults on the DER performance and the effectiveness of the proposed FTC scheme are evaluated through extensive simulation studies. Experimental verification of the method is provided to further validate the proposed approach. [ABSTRACT FROM AUTHOR]

This paper proposes a robust control method for power sharing among dispatchable electronically-coupled distributed energy resources (DERs) in an autonomous microgrid. The DER units operate in parallel to meet the total aggregated load demand. The aggregated load is assumed to be uncertain, unbalanced and/or nonlinear, thus it is considered as a source of unmodeled dynamics. The main objective of this paper is to design an H ∞ robust controller to enable tracking reference commands of the voltages at the output of the DERs in the presence of unmodeled dynamics. In addition, the H ∞ control design problem is combined with other control methods, such as constrained optimal control and regional pole placement, to achieve an optimal control objective, and to improve transient response. The optimal controller is achieved by minimizing an upper bound on H 2 performance. The regional pole placement is achieved by defining a linear matrix inequalities (LMI) region. This multiobjective H 2 / H ∞ problem, combined with regional pole placement are expressed as a set of LMIs, and solved by using standard convex optimization algorithms. To show the effectiveness of the proposed method in maintaining the desired voltage at the DER outputs, in presence of unbalanced and nonlinear loads, simulation results are provided for a single DER unit, and the performance of the proposed control strategy is compared with three different controller strategies. Power sharing among multi DER units in autonomous networks is then demonstrated through digital time-domain simulation studies using MATLAB/SimPowerSystems Toolbox. [ABSTRACT FROM AUTHOR]

Advanced microgrid (MG) is a likely model for reaching the goal of 100% renewable grid. A complete advanced MG control must steer the power flow in grid-connected mode; regulate voltage/frequency in islanded mode; and perform power sharing among distributed energy resources (DERs) in both modes. Power-based control (PBC) is a well-known centralized control capable of achieving these three targets. A serious disadvantage of usual MG structures using PBC approach is the existence of a single centralized grid-forming converter to form the islanded MG. Centralized grid-forming converter is a single point of failure, which reduces the capability of the MG expansion, and it is an expensive element for the MG. This paper proposes a MG control strategy using an improved centralized control together with a droop-based power-loop in distributed voltage-controlled mode (VCM) converters to allow the MG to operate in both modes, without a centralized grid-forming converter and critical islanding detection. Moreover, the new approach considers single- and three-phase DERs, controlled in VCM or current-controlled mode, and DERs with self-imposed limits, characterizing a heterogeneous MG. Real-time hardware-in-the-loop setup is used to test the proposed control strategy in several operating conditions considering ten heterogeneous DERs. Results show grid power flow control, power sharing, unbalance compensation, voltage/frequency restoration, and smooth transitioning between operating modes without critical islanding detection. • Precise grid power flow control. • Multiple grid-forming converters. • Proportional power sharing among heterogeneous distributed-energy resources. • Islanded operation with voltage and frequency restoration. • Smooth transitioning between operating modes without critical islanding detection. [ABSTRACT FROM AUTHOR]

Distributed energy resources (DER) are integrated into a microgrid through dc-dc power electronic converters. The bidirectional dc-dc converter regulates charging and discharging operations of ESS. Model predictive control (MPC), is a high-performance control technique for these converters, but it is limited in robustness to parameter mismatch, model uncertainties and sensor measurement noise. Therefore, in this paper, an improved hybrid cascade-parallel extended state observer (CP-ESO) is proposed, for model-free predictive control, to guarantee both robustness to parameter/model uncertainties and measurement noise suppression. A novel structurally-adaptive ESO scheme is also proposed to improve the disturbance rejection of CP-ESO during transient response. These results are supported by analysis and design guidelines for the selection of optimal sub-frequencies. Experimental results, for a bidirectional dc-dc boost converter, validate the effectiveness of the proposed methods against model uncertainties, external disturbances from variable input voltage and load, as well as measurement noise. • Analysis and design guidelines for multi-frequency extended state observers (ESO). • Improved cascade-parallel extended state observer with better noise suppression. • Novel structurally-adaptive ESO that gives better transient disturbance rejection. • Experimental validation with a bidirectional dc-dc boost power converter. [ABSTRACT FROM AUTHOR]

The modular multilevel converter (MMC) based multiport energy router (MER) provides multiple connecting points such as HVAC terminal, HVDC terminal and dc terminals of submodules (SMs). The uneven power distribution of SM to distributed energy resources (DERs) is allowed and required for MMC-MER but may cause imbalance on capacitor voltages, further leading to SM overvoltage. The existing circulating current injection methods have the problems of significantly increasing the current stress or voltage stress of SM power devices, which will increase the power rating and cost of power devices. This paper develops a novel reactive fundamental frequency circulating current injection method that can suppress the high voltage stress resulted by static capacitor voltage difference and high current stress caused by second-order circulating current injection. The key of the proposed method is the determination of reactive fundamental frequency circulating current reference combing with power distribution of SM to DER, which is on the basis of derived analytical expressions between power limits and reactive fundamental frequency circulating current. Finally, simulation and experimental results validate the feasibility of proposed method. [ABSTRACT FROM AUTHOR]

• Integration of blockchain characteristics into the collaborative carbon emission reduction game model of both the supply and demand sides of electricity is proposed to encompass source control and terminal suppression. • Blockchain is utilized to trace electricity types within the electricity market and to quantify the carbon emissions generated by the demand side of electricity. • By incorporating blockchain, users can actively engage in individual-level carbon trading to control and reduce carbon emissions. • Blockchain facilitates enhanced carbon emission reduction efforts by the power supply side. The task of carbon emission reduction is severe in the power industry in China under the national goal of "carbon peaking and carbon neutrality". The current carbon reduction is mainly based on supply side, but the effect is limited. To solve this problem, this paper proposed a coordinated supply–demand carbon emission reduction strategy based on blockchain technology. Based on the carbon emission reduction of complementary thermal power and renewable energy under the carbon trading mechanism of power supply side, users are made to participate in the individual level carbon trading mechanism with the help of blockchain technology, and the carbon emission reduction in power demand side is guided through the market mechanism, thus forming a supply–demand collaborative carbon emission reduction strategy of source side control and terminal inhibition. By analyzing the decision changes of both the supply and demand sides of electricity before and after the introduction of blockchain, quantifying the influence of blockchain on electricity quantity, electricity price and users' utility in turn, and establishing the personal carbon trading mechanism supported by blockchain, a game model of two-side interaction between supply and demand was constructed. The simulation results show that the collaborative carbon emission reduction strategy based on blockchain gives full play to the potential of carbon reduction in power demand side, and the personal carbon trading mechanism can better inhibit terminal carbon emissions, which is conducive to the deep carbon emission reduction of the power industry. [ABSTRACT FROM AUTHOR]

In this paper, selling price determination problem for an electricity retailer has been studied. In the proposed model, the selling price is determined under fixed pricing (FP), time-of-use pricing (TOU) and real-time pricing (RTP). It is shown that the selling price determination based on RTP by the retailer can lead to the higher expected profit. Furthermore, to exchange power between retailer and power market, the bidding and offering curves should be prepared to bid and offer to the day-ahead market. Therefore, this paper proposes an information gap decision theory (IGDT) for obtaining of optimal bidding and offering strategies of retailer. IGDT assesses the robustness and opportunity decisions of optimal bidding and offering strategies in the presence of market price uncertainty while retailer considers whether robustness decision (risk-averse) or opportunity decision (risk-taking). It is shown that risk-aversion and risk-taker influence the expected profit and optimal bidding and offering curves. Meanwhile, the scenario-based stochastic framework is used for uncertainty modeling of market prices, client group demand and variable climate condition containing temperature, irradiation and wind speed. To validate the proposed model, three case studies are used and the results are compared. [ABSTRACT FROM AUTHOR]

To solve the reactive power and negative-sequence problems in electrified railways, this paper presents a novel railway power supply system, which synthesizes two secondary-side voltage ports of the Scott transformer to form the 3rd voltage port supplying the traction loads. The system can simultaneously ensure satisfactory power quality for the grids and cancel the neutral section out of the traction substation. The configuration and operation principle of the system are first explained, and a comparison with two other traction power supply systems is conducted. To reduce the operation currents and compensation capacity, a partial compensation strategy based on current decomposition is proposed. Compared with other strategies, the proposed strategy is characterized by a more intuitive physical mechanism and concise expressions, and hence has the advantage of smaller calculation and flexible application. On control, the reference detection based on this strategy is also explored to generate compensation currents instantaneously. In this paper, the mathematical relationships between the power quality indices and current components are established. And the capacity reduction analysis with partial compensation is given in detail. Both the simulation and experimental results are presented to show the feasibility and effectiveness of the proposed system and the strategy. [ABSTRACT FROM AUTHOR]

• This paper proposes a new passive anti-islanding strategy for photovoltaic systems. • The proposed strategy reduces the voltage stress of photovoltaic inverters. • The performance of the proposed strategy in fault ride-through operation is proved. This paper presents the performances of a new passive anti-islanding protection with minimal switching losses for three-phase grid-connected photovoltaic power systems. The novelty of the proposed strategy consists of five conventional passive relays, which are as follows: over/under current, over/under voltage, over/under frequency, rate of change of frequency, and dc-link voltage-based anti-islanding methods. Integrating these methods in a synergistic way reduces the limitations of each method, while combining the strengths and benefits of each method in islanding detection. As such, the dc-link voltage-based method consists in supervising the dc-link voltage in voltage source converters to reduce the voltage stress and increase performance. The performance in islanding prevention is determined by the detection time of islanding operation mode. The proposed anti-islanding protection was simulated under complete disconnection of the photovoltaic inverter from the electrical power system, as well as under grid faults as required by new grid codes. [ABSTRACT FROM AUTHOR]

• This paper proposes a new emergency service market in which BESS can compete reasonably, fully considering the SOC of energy storage equipment. • The AEBS market takes dynamic risk as factor. The dynamic demand and transaction pricing of BESS are more accurate and proactive on risk nodes. • This paper introduces a penalty mechanism to curb the occurrence of dishonest behavior in the LMP method. The power systems with a high proportion of clean energy face both greater uncertainty in power generation and the threat of stability brought by low inertia. After a fault, the rapid drop in power grid frequency, large voltage fluctuations, and insufficient reactive power support can lead to new energy generation units being disconnected from the grid. Fast active and reactive support will play an important role in suppressing fault propagation in low-inertia systems and preventing large-scale power outages. The reserve and frequency regulation market is a necessary means to deal with power grid failure events, but many blackout events occurred due to insufficient reserve capacity and ineffective market transaction mechanisms. As a flexible power regulation resource, BESS (battery energy storage system) has been incorporated into the power ancillary service market planning. In some engineering cases, the frequency regulation ancillary service provided by energy storage has also shown its advantages. However, there is a lack of in-depth research on how to gather BESS to take part in AEBS (ancillary emergency backup services) in the early stage after the failure. Therefore, in view of the dynamic change of power system risks and the different response speed of different backup resources, this paper proposes to establish a segmented combined backup mode to realize the combination of emergency backup at the initial stage of failure and frequency regulation backup during the fault recovery process. A related model of AEBS demand assessment and emergency backup service pricing mechanism is established. And considering the capacity, life loss and opportunity costs of energy storage for AEBS, a LMP (locational marginal price) method is used to calculate the electricity price of AEBS provided by BESS. It has proved the effectiveness by constructing a grid AEBS based on BESS used by the IEEE 30 node system. [ABSTRACT FROM AUTHOR]

• Proposes a scheme of premium power supply scheme based on leasing mitigation equipment against voltage sag (PSLM), including the stakeholders, the leasing modes and the leased property. • Presents an algorithm of SE trip probability when SE is equipped with ME, to measure the mitigation effect of ME. The three technical parameters of ME are included in the method. • Proposes the optimal configuration model of ME, including the two objective functions and three constraints. • Proposes a calculation model of rental of PSLM. Small and medium-sized sensitive users are eager to have premium power supply. The common solution is purchasing mitigation equipment (ME). However, some of they have the financial pressure that cannot afford the mitigation. In order to solve this problem, this paper proposes a premium power supply scheme based on leasing mitigation equipment against voltage sag (PSLM). First, the scheme of PSLM is proposed, which consists of stakeholders, leasing modes and leased property. Second, this paper presents an algorithm of sensitive equipment (SE) trip probability considering effect of ME, to calculate trip probability of SE when it is equipped with ME, which reflects the mitigation effect that is related to profits of scheme. Then technical parameters of ME are optimized by an optimal configuration model, considering tolerance of SE under voltage sag. The cost of the scheme is determined by the optimization of technical parameters of ME. Furthermore, this paper proposes a calculation model of rental of PSLM, to maximize the revenues of both stakeholders. Non-dominated sorting genetic algorithm-II is used to solve the proposed two models. Finally, the effectiveness and feasibility of the proposed PSLM is verified by a case study of a grain oil manufacturer in the south China. [ABSTRACT FROM AUTHOR]

In modern electric grid system, a decision maker (DM) has to operate the system with multiple aims in mind. Traditional electric system was not so complex and emphasis used to be given only in optimizing the cost of energy dispatch. However, recent regulations restrict the environmental emissions caused by the energy sources. Hence, optimum energy generation and dispatch is a very critical issue in modern grid system. When, viewed as an optimization problem complying with both economy and environmental restriction, it is a very challenging one. Researchers in the past have solved such problems as multi-objective Economic Load Dispatch (ELD) problem. This paper attempts to solve the same problem as an Optimum Active Power Dispatch (OAPD) problem using a very recently developed optimizer called ‘Exchange Market Algorithm’ (EMA). The problem is modelled as both single and multi-objective problem. The EMA algorithm proceeds for the global optima through two of its main phases; i.e. balance market phase and oscillated market phase, each having both exploitation and exploration. The superior search capability of EMA is successfully exploited in this paper to attain various objectives. Programs are developed in MATLAB and tested on standard IEEE 30 bus comprising of six thermal units. The results obtained using EMA are compared with other methods reported in consulted literature. Simulation results demonstrate the authority of EMA in terms of its computational efficiency and robustness. [ABSTRACT FROM AUTHOR]