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• 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]

• 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]

This paper presents a novel approach for distributed management of energy communities. The proposed method utilizes a stochastic profile steering algorithm as a greedy heuristic. The optimization process considers random parameters such as local forecasted demand, photovoltaic (PV) production, and the initial energy of electric vehicles (EVs) as embedded scenarios. Profile steering coordinates the flexible electricity assets within an energy community by determining the contribution of each prosumer's profile to the average value of the objective function. It iteratively selects the prosumer that contributes the most until no further improvements can be made. This process scales linearly with the number of controllable prosumers and can achieve various community-level objectives, such as maximizing self-sufficiency or minimizing aggregated cost-of-energy, even when dealing with non-convex optimization problems for modeling each prosumer's local energy management system. The outcome of the proposed method optimizes the average value of the community's objective while ensuring that grid limitations are met within a specified probability. The proposed method is evaluated through simulations involving small-scale communities (5 households) and large-scale communities (100 households). The results demonstrate the efficiency, flexibility, and scalability of the proposed method, as well as its ability to reschedule the aggregated demand to ensure that grid limits are not violated with at least a 95% probability. • A new approach for distributed power aggregation of energy communities is proposed. • Stochastic profile steering is used as the main algorithm. • Average community-level goals are optimized while meeting probabilistic grid limits. • Local forecasted demand, PV production, and EVs of prosumers are considered as random variables. • Results demonstrate the efficiency, flexibility, and scalability of the proposed method. [ABSTRACT FROM AUTHOR]

Regional data center clusters have flourished in recent years to serve customers in a major city with low latency. The optimal coordination of data centers in a regional cluster has become a pressing issue because of their rising energy consumptions. In this paper, an online algorithm based on Lyapunov optimization framework is developed for the combined computing workload and energy coordination of data centers in a regional cluster. The proposed online algorithm is prediction-free and easy to implement. We prove that the workload queues and battery energy level will be within their physical limits, though their related time-coupling constraints are not considered explicitly in the proposed algorithm. The previous online algorithms do not have such a guarantee. A theoretical upper bound on the optimality gap between the online and offline results is derived to provide a performance guarantee for the proposed algorithm. To enable distributed implementation, an accelerated distributed coordination algorithm is developed based on the alternating direction method of multipliers (ADMM) with iteration truncation and follow-up well-designed adjustments, whereby a nearly optimal solution is attained with much enhanced computational efficiency. Case studies show that the proposed algorithm reduces the operational costs and saves computation time compared to online benchmarks. • The combined computing workload and energy coordination of data centers is studied. • A prediction-free online coordination algorithm is built with proven properties. • An accelerated ADMM algorithm is developed for distributed implementation. • The proposed coordination method greatly reduces the total cost of data centers. • The computation time is largely reduced compared to the traditional ADMM. [ABSTRACT FROM AUTHOR]

In low-inertia grids, distributed energy storage systems can provide fast frequency support to improve the frequency dynamics. However, the pre-determination of locational demands for distributed energy storage systems is difficult because the classical frequency dynamic equivalent response cannot capture the dynamic characteristics of the entire system. Additionally, optimal allocation of the distributed energy storage systems required for the different buses is challenging because of nonlinear constraints that account for these locational effects. This paper develops a method to evaluate locational frequency security. By simulating the worst-case G-1 contingency, the distribution network buses that violate the limit of the maximum rate of change of frequency are determined as the installation locations of distributed energy storage systems. We then propose a feasible region-based linearization method to replace the complex nonlinear constraints. Finally, we present a two-stage robust allocation model for distributed energy storage systems that is intended to maintain locational frequency security. The validity of the proposed method is verified through case studies performed on a modified IEEE RTS-24 bus system and a modified IEEE 118 bus system. The results show that the optimally allocated distributed energy storage systems effectively reduce the frequency safety risk that originally existed due to oscillation at the distribution network buses. • A locational frequency security evaluation method after G-1 contingencies is proposed. • Considering frequency oscillations, a feasible region-based linearization method is designed. • Distributed storage systems are optimally allocated for locational frequency security. [ABSTRACT FROM AUTHOR]

• Targeting United States residential HVAC electricity load using wind energy. • Partial load targeting concept using renewable energy source. • Capacity factor and estimation analysis of wind energy potential production. • Offshore versus onshore capacity factor estimated production performance. • Heating and cooling load curtailment methodology and analysis. This paper explores the concept of partial-load targeting through presenting a technical analysis for modelling the electrical energy demand of residential HVAC systems spanning the U.S. using onshore and offshore wind turbine technology. The proposed modeling technique uses DOE backed survey data and both known and theoretical capacity factor (CF) levels to determine the amount of wind turbines required to sustain the electrical load necessary for the heating and cooling components of residential housing units within all major U.S. climate regions. To perform the analysis, annual load data sourced from the energy information administration (EIA) surveys utilizing National Renewable Energy Laboratory (NREL) benchmark simulation house parameters are calculated and compared to wind turbine CF data to determine an equivalent range of turbines required to satisfy the target load. Two production profiles based on the CF range of each region are created using onshore and offshore scenarios. These profiles are then fitted to the corresponding average annual household load for the particular climate region. An analysis on the results is then conducted to determine overall potential load reduction as well as observe characteristics between the offshore and onshore data. [ABSTRACT FROM AUTHOR]

• A method for new power electronic equipment is proposed. • Novel software for the developed hardware is proposed. • The device decentralises the aircraft's power system. • This solution increases overall efficiency and safety. The More Electric Aircraft concept has arisen in an attempt to achieve the goals stated by different international organisations in terms of the concept of future aircraft. While some of these requirements are related to reducing the negative effects of airfreight such as CO2 emissions or fuel consumption, other requirements focus on aircraft safety enhancement. Nevertheless, if the way to achieve the vast majority of these goals is based on increasing electrification level of aircrafts, then their electric power system must be reviewed and reconsidered. Currently, literature proposes device redundancy as the main option for meeting aircraft safety requirements. This paper proposes alternative architecture, which consists of merging a new source code and recently developed topology, which makes it possible to decentralise the aircraft's DC bus, in order to increase its safety and meet the stated goals. While the new topology has been validated through small-scale prototyping, proposed software strategy has been validated in a virtual environment using simulations. The architecture developed enables a path to be selected in the electric power system: examining different options in the system, analysing which of those options are the most efficient and, finally, sending signals to configure the power system connections to power the load. Based on the results of those tests, the alternative proposed satisfactorily powers selected loads. [ABSTRACT FROM AUTHOR]

With the rise of the energy Internet and the reform of the power market, the multi-energy collaborative trading mode plays a fundamental role and acts as the framework in the construction of the integrated energy market. An integrated energy system model and its trading market mechanism are built in this paper. A new transaction strategy in the system is proposed to solve the problem of cascade utilization of thermal energy and flexibility of future market relationships. To facilitate the role transformation of stakeholders, the multiple agents system is used to decouple the complex relationship of the market transaction into two Stackelberg games of "two leaders and one follower". In addition, The Lagrange multiplier method is used to optimize the objective function and the optimal result is obtained by continuous iteration, and the existence and uniqueness of Nash equilibrium are verified. Eventually, The simulation results validate that the model has greater benefits for all stakeholders, new energy consumption and environmental protection. • The trading strategy proposed solves the flexible trading of multiple energies. • Decoupling the multi-energy market transaction into two Stackelberg games models. • The existence and uniqueness of Nash equilibrium in transaction model are verified. [ABSTRACT FROM AUTHOR]

• This paper proposes a concept that utilizes historical time-series data from transmission system operators (TSOs) to estimate the optimal day-ahead energy-mix proportion for a power system dominated by converters. • This paper employs a long short-term memory (LSTM) network as a data-driven model to forecast the day-ahead values of key parameters. • To ensure secure system functioning, an energy-mix operation and reserve scheduling model is developed. • This paper emphasizes the importance of power system security in incorporating inverter based resources (IBRs). Advancements in various scientific fields have encouraged the development of novel tools, techniques, components, methodologies, and innovations aimed at addressing the challenges encountered in modern power systems dominated by inverter-based resources (IBRs). This paper focuses on a concept that leverages historical time-series data obtained from transmission system operators (TSOs) to enhance the secure management and operation of power systems. By employing a data-driven model, the day-ahead values of power generation and load consumption are estimated and integrated with a dynamic model of the power system for further analysis. To optimize energy generation and ensure grid stability, an energy-mix operation and reserve scheduling model is utilized. This model optimally combines different power-generating technologies, including synchronous generators (SGs), grid-following converters (GFLs), and grid-forming converters (GFMs), to meet the energy demands of the day while enhancing the overall system strength. The findings are supported by quantitative analysis utilizing variables such as frequency, power production, terminal voltages, and system non-synchronous penetration (SNSP). Simulation results demonstrate that implementing the proposed concept enables the power system under consideration to operate securely, even in the face of a 38% increase in immediate load, with a maximum SNSP ratio of 59%. These findings highlight the effectiveness of the proposed approach in addressing the reliability, system dynamics, stability, control efficiency, and security challenges posed by IBR-dominated power systems. Furthermore, it is believed that this research contributes to the ongoing efforts in decarbonization, renewable energy integration, and combating global warming by facilitating the secure and optimized operation of renewable energy-dominated power systems. [ABSTRACT FROM AUTHOR]

• It is proposed a topology consisting of an input step-up stage, followed by a step-down stage and an unfolding inverter. • A decentralized control strategy of the DC-DC stage allows maximizing the renewable energy harvest and synthesizing an output current to be injected into the grid with low harmonic distortion. • The grid current controller proposed in this paper consists of a reduced number of resonant stages meeting the energy quality required by standards. • The article introduces a novel extension of a formerly presented design strategy for Proportional + Resonant controllers, to include harmonics compensation. • A 500 W prototype converter was implemented, and the experimental results show a fast transient response to step changes in both the input current and the phase angle reference. Also, the multi-resonant controller design presented in this article resulted in an injected current with a THD of 3.37%, compliant with the IEEE Std. 1547–2018. This paper presents a two-stage current-source DC-AC converter for grid-connected PV applications which is composed of an input step-up stage, followed by a step-down stage and an unfolding inverter. A decentralized control strategy of the DC-DC stage allows maximizing the renewable energy harvest using an Incremental Conductance MPPT algorithm and synthesizing an output current to be injected into the grid with low harmonic distortion. Double-loop PI controllers are used for the boost stage. The DC bus voltage of the buck stage is regulated using a PI controller, and an inner Proportional-Resonant (PR) controller tracks a sinusoidal reference. The PR controller proposed in this paper, includes a reduced number of resonant stages meeting the energy quality required by standards, which results in good stability margins. Finally, a SOGI-FLL algorithm synchronizes the inverter operation with the grid. Experimental results show an excellent dynamic response of the system, and the injected current complies with the IEEE Std. 1547–2018 specifications regarding harmonic content using a control law with a low computational burden. [ABSTRACT FROM AUTHOR]

• A hierarchical approach is proposed considering quartile method for optimization. • Proposed CEEMDAN-DMD can regress the energy based on pre-set energy proportion. • Proposed hierarchical allocation strategy can be used for severe power volatility. The development of renewable energy dominated power system and the integration of large-scale wind-storage energy system are inevitable trends of the future society. This paper developed a new methodology of hierarchical power allocation strategy considering wind power fluctuation. A novel idea is presented that fully considers the fluctuation and energy proportion. Based on the Quartile Method (QM) and Hampel-Savitzky Golay (HSG) strategy, a layered-based method that considers the volatility interval to effectively overcome the limitations of a single strategy caused by severe volatility interval is proposed. Then, an improved power division method based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Dynamic Mode Decomposition (DMD) is proposed where CEEMDAN is used to decompose the Intrinsic Mode Function (IMF) of the remaining fluctuations. Furthermore, according to the required setting of energy proportion, DMD strategy is used for power fitting of the hybrid energy storage system (HESS). Through experimental verification, the hierarchical strategy proposed in this paper can effectively reduce the probability of severe volatility interval or distortion interval and ensure the sequence in the ideal interval. Moreover, the proposed CEEMDAN-DMD strategy can realize the power division according to the energy proportion demand. In model verification, the volatility improvement ζλ 1 min = 85.4766%. Set the energy demand ≥ 80%, the fitting energy proportion accounts for 87.7533%, which outperforms the traditional power division method. In addition, it is demonstrated that the theoretical method proposed in this paper is reliable and promising. [ABSTRACT FROM AUTHOR]

• Peer-to-peer energy trading problem of multi-energy microgrids are investigated. • The concept of energy trading consistency is firstly proposed. • The off-design performance model of the energy conversion device is considered. • The decision-making problem is solved by multi-agent soft actor-critic approach. Multi-energy microgrid technology is an essential for addressing the diversification of energy demand and local consumption of renewable energy sources. Peer-to-peer energy trading has emerged as a promising paradigm for the design of a decentralized trading framework. Therefore, this paper investigated the external peer-to-peer energy trading problem and internal energy conversion problem of interconnected multi-energy microgrids. The concept of energy trading consistency to avoid unreasonable energy trading behavior is first proposed and an off-design performance model of the energy conversion device is considered to more accurately reflect the operating status of the device. The complex decision-making problem with significantly large high-dimensional data is formulated as a partially observable Markov decision process and solved using the proposed multi-agent deep reinforcement learning approach combining the centralized training decentralized execution framework and soft actor-critic algorithm. Finally, the effectiveness of the proposed method was verified through a case simulation. The simulation results showed that the proposed method can reduce the total cost compared with the rule-based method. [ABSTRACT FROM AUTHOR]

• A model for wind power system with sensor and actuator faults is established. • The distributed resilient multi-event triggering scheme (DRMETS) is derived. • The DRMETS is integrated into the fault-tolerant cooperative controller design. • The DRMETS based fault-tolerant cooperative control strategy is proposed. • It provides stronger reliability and higher wind energy utilization efficiency. In wind energy conversion power system, some sensor and actuator fall into abnormal condition which may lead to lower wind energy utilization rate and poor system performance or even system collapse. Meanwhile, with the increasing complexity of wind energy conversion power system, more device nodes compete for limited network communication resources, there are time-delays and packets dropout in data transmission, and these may reduce the efficiency of wind energy conversion. According to above consideration, the multi-event triggering mechanism based distributed resilient fault-tolerant cooperative control scheme for wind energy conversion power system is put forward. A novel model of the wind energy conversion power system is established when the sensor and actuator faults simultaneously. According to Lyapunov stability theory, the sufficient condition for system exponential mean-square stable is obtained. Different from previous fault-tolerant control method, the proposed control strategy considered the wind energy conversion power system with data transmission management under simultaneous stochastic sensor and actuator faults. The simulation demonstrates that the method proposed in this paper is effective and the multi-event triggering mechanism based distributed resilient fault-tolerant controller provides stronger reliability and higher wind energy utilization efficiency. [ABSTRACT FROM AUTHOR]

• This paper proposes a weighted consistency algorithm based on DMPC and state constraints, which takes into account the power constraints of each BESU and can quickly realize the adaptive power allocation of multiple energy storage units with less communication times. • Based on the proposed consistency algorithm, this paper designs a grouping coordination control strategy for energy storage units, which can reduce the charge/discharge conversion times of BESU, and prolong the life of energy storage system and improve the energy conversion efficiency without double capacity configuration and without reducing the rated power of BESS. • Based on the proposed consistency algorithm, this paper designs a method to reduce the low-power (low-efficiency) operation ratio of BESU, so as to improve the operation efficiency of the energy storage system. This paper proposes a distributed packet consistency control strategy to solve the distributed coordinated control problem among multiple battery storage units. In order to ensure that the distributed power allocation results meet the power constraints of each energy storage unit and reduce the communication requirements of distributed control, this paper proposes a weighted consistency algorithm based on distributed model predictive control and state constraints. A grouping control strategy of energy storage units is proposed. The weight of energy storage unit is set according to SOC information to improve consistency of SOC of energy storage unit. Meanwhile, based on the proposed consistency algorithm, an inter-group coordination control strategy and an efficiency improvement strategy of energy storage units are developed to improve the regulation ability of energy storage system, extend the life of energy storage system and improve the energy conversion efficiency. Finally, the proposed algorithm and control strategy are simulated and analyzed by building a microgrid system with eight battery storage units in Matlab. The simulation results show that the proposed algorithm and control strategy have certain advantages in improving convergence speed, optimizing control effect, prolong energy storage life and improve the operation efficiency of the energy storage system. [ABSTRACT FROM AUTHOR]

The increasing penetration of distributed energy sources demands higher observability of the distribution networks for better scheduling, operation, dispatching and control. However, the huge amounts of nodes in the distribution networks implies that any additional measuring instrument would be of huge costs burden for DSO. In this paper, we proposes a full-scale framework for line parameters, topology, phase label estimation and angle recovery utilizing smart meter measurements only for the non- μ PMU distribution networks. Firstly, we linearize the three-phase power flow model and induce the relationship of nodal voltage magnitudes and power injections. Then, a joint estimation method based on linear regression and topology characteristics is proposed to find line parameters, topology, and phase label. Finally, the nodal angle information is recovered by non-linear regression based on the estimation result. Simulations on both single-phase and three-phase networks are implemented. The results suggest that the proposed framework can provide an accurate estimation result with limited measurements. • Linearized power flow-based method to estimate line parameters without nodal angle. • It enables topology identification, phase labeling and nodal angle recovery. • The required data can be obtained from smart meters under current standards. [ABSTRACT FROM AUTHOR]

• Proposed an energy sharing model of MMGs considering the multiple uncertainties and the cheating behaviors. • Using robust optimization to deal with uncertainties brought by grid tariffs and renewable energy. • Cheating behaviors in energy transaction are analyzed and a cheating equilibrium mechanism based on ITM is proposed. Multi-microgrid system (MMGs) has drawn extensive attention recently because of its high energy efficiency. However, MMGs' operational efficiency can be affected by market price fluctuations and intermittent renewable energy. This paper proposes an energy-sharing model based on the Nash bargaining game between multi-microgrids. The proposed model provides a robust energy trading schedule to deal with uncertainties brought by grid tariffs and renewable energy. To ensure the model is tractable, the original game problem is equivalently converted into a system benefit maximization subproblem and an additional profit distribution subproblem to get optimal energy sharing power and prices. In addition, microgrid has the motivation to cheat for maximizing its benefits which may lead to the breakdown of cooperation. Furthermore, cheating behaviors in energy transaction are analyzed; the energy sharing scheme based on cheating equilibrium is derived by proposing an intermediary transaction mode. Finally, the alternating direction method of multipliers (ADMM) is used to protect the players' privacies in a distributed way. Simulation results show that the proposed model can realize stable cooperation, effectively reduce operating costs and immunize against multiple uncertainties and cheating behaviors. [ABSTRACT FROM AUTHOR]

• Renewables are variable and require operation flexibility. • Colombia expects more than 10 GW of solar and wind power capacity by 2030. • We quantify the flexibility requirements for the net load and ramps. • Conventional reserves must be doubled at noon. • Conventional generators must provide 7%–10% of additional ramps. Multiple factors drive the energy transition toward power systems based on renewable energies. The use of renewable energies brings new challenges for operators since they are difficult to predict. Power system flexibility is the ability to handle differences between supply and load and can be quantified to measure the effects of renewable energies on power systems. Colombia expects to triple the current solar and wind power capacity by 2030; therefore, it is essential to evaluate the flexibility of the Colombian power. This paper presents a pioneering analysis in the local context, filling a local research gap, by quantifying the flexibility requirements of the Colombian power system based on changes in the net load and its ramps and considering wind and solar generation scenarios and load projections. This analysis has not been conducted locally and can be used by policymakers to determine the country's energy transition path. The results show that renewable power impacts conventional generators. Hydro generation handles high ramp deviations well under normal conditions but faces challenges during low hydrology periods. The Colombian power system is projected to be flexible in 2030 but less so in 2050 due to overproduction and higher net load ramps. [ABSTRACT FROM AUTHOR]

Energy management algorithms of microgrids have attracted growing attention for the efficient operation and coordination of active end-users. In developing these algorithms, leader–follower game approaches could provide effective strategies when the behavioral and operational characteristics of active end-users are integrated into the methodology. However, since these approaches require the exact implementation of the strategy determined by the leader, it is of great importance to deal with the uncertainty of renewable energy generation, charging/discharging energy of storage systems, and load consumption. Thus, possible negative effects of these uncertainties on the outcome can be minimized with an appropriate billing procedure. In this paper, a Stackelberg game approach is proposed for the energy sharing management of a microgrid including prosumers and plug-in electric vehicle (PEV) charging stations (CSs), and a proper billing scheme is designed to increase the robustness of the approach on real-time applications. In the hereby game-theoretic approach, the energy demands of PEV CSs are determined as variable demands considering the operational characteristics of CSs. The energy planning of CSs is based on determining the number of PEVs that each station can accept over the vehicle charging levels for a specified time interval. Also, by the necessary modifications identified for the presented approach, the microgrid can operate independently from the utility grid and satisfy the requirements of islanded mode for the time slots in which the microgrid exchanges energy with the grid based on the end-game results. The energy management strategy proposed in this study presents a comprehensive solution capable of optimizing energy sharing in microgrids while taking into account end-users' operational characteristics and network requirements. Furthermore, the designed billing scheme ensures that the accuracy of the proposed game approach remains unaffected by the uncertainties, thus preserving the microgrid's profit. • Energy sharing management problem of a microgrid is modeled as a Stackelberg game. • Loads of PEV charging stations are determined by an integer programming problem. • The microgrid can operate independently from the utility grid under islanded mode. • A billing scheme is proposed to minimize the negative effects caused by uncertainties. [ABSTRACT FROM AUTHOR]

This paper studies the price-based demand response problem in a deregulated retail electricity trading, aiming to coordinate the energy consumption behavior of end-users under dynamic retail prices. The challenge here is that in addition to the hierarchical decision-making process between utility company and end-users considered in existing works, the non-cooperative and competitive interdependence among end-users cannot be ignored. To address this issue, we first construct a novel Stackelberg–Nash game, in which the Stackelberg game is used to capture the hierarchical decision-making process between utility company and end-users, while the Nash game is dedicated to describing the interdependence among end-users. Then the existence and uniqueness of the Stackelberg–Nash equilibrium is provided along with theoretical analysis. On the basis of the analysis of equilibrium, we propose a distributed iterative algorithm with an adaptive step size, which is benchmarked with a fixed step-size algorithm. The comparison results on a real-life residential retail electricity market show that our proposed algorithm has better performance in terms of effectiveness and scalability. • Develop a Stackelberg—Nash game model to characterize the interactive relationship. • Proof the existence and uniqueness of Stackelberg–Nash equilibrium. • Propose a distributed iterative algorithm with an adaptive step size. [ABSTRACT FROM AUTHOR]

• Proposing a short-term wind power forecasting model based on VMD-GRU to mitigate the impact of wind power uncertainty. • Implementing refined P2G two-stage operation to investigate the advantages of hydrogen energy efficiency. • Constructing a thermoelectric integrated demand response model with adjustable thermoelectric ratio to reduce load peak-valley differences. • Building a low-carbon economic scheduling model for IES containing electric hydrogen production based on the VMD-GRU short-term wind power prediction model. The integration of energy systems (IES) enhances the interaction between the electricity, gas, and heat systems, and the concept of low-carbon development can further reduce the carbon emissions of IES. However, the uncertainty of wind power output and the complexity of different energy chains pose significant challenges to the low-carbon operation of IES. Therefore, this paper proposes a low-carbon economic dispatching strategy for IES containing electric hydrogen production based on short-term wind power prediction. Firstly, a variational mode decomposition- gate recurrent unit network prediction model is employed to enhance the accuracy of ultra-short wind power forecasting, which reduces the impact of wind power output uncertainty on wind power grid connection. Secondly, a refined two-stage P2G operation model for refining the electric hydrogen production device is constructed to decrease the carbon emissions of IES. Thirdly, a thermoelectric integrated demand response model is established to adjust the heat and power demand proportion, further reducing the IES's carbon emissions. Finally, the case study is performed based on IEEE standard system to verify the effectiveness of the proposed strategy. Simulation analysis shows that introducing electrolysis for hydrogen production can reduce IES carbon emissions by 12.90%. In addition, introducing an adjustable thermal-electric comprehensive demand response can reduce IES carbon emissions by 1.543% while lowering the overall cost by 5.24%. The proposed strategies can simultaneously consider the low-carbon and economic aspects of IES. [ABSTRACT FROM AUTHOR]

• Accordingly, a flexible traction power supply system (FTPSS) composed of a TT and multi-port power hub and its coordinated control strategy are proposed for VU compensation and energy conservation in this paper. The main contributions can be summarized as follows: • Different from previous research, a power hub is installed in the TPSS to achieve power flow control among the utility grid, traction network, and DC grid. The FTPPS with the power hub can enable VU compensation for the utility grid, which also offers a friendly interface with distributed PV and ESS for on-site access. • A three-port VU compensation method based on active power flow control and reactive power injection is presented, which divides the system demand compensation capacity and reduces the performance requirements for power electronics, compared with traditional compensation methods. • A coordinated control strategy with a hierarchical structure is designed for FTPSS. The control strategy enables real-time power management to enhance energy utilization and mitigate VU in the system layer and coordinates multiple converters to realize the multi-function operation of the system in the converter layer. To mitigate voltage unbalance (VU) and eliminate the neutral sections while reducing the energy consumption of railways, a flexible traction power supply system (FTPSS) with the power hub is proposed. Different from conventional schemes, the proposed system provides a friendly interface to realize the on-site access of distributed photovoltaic (PV) generation along the railway line and concentrated integration of energy storage systems. The power flow is centrally managed by the power hub to reduce energy consumption and the proposed three-port VU compensation method is coupled with the power hub to suppress negative sequence current caused by trains. Considering the volatility of PV generation and traction loads, four operation modes including grid-connected, valley filling, VU compensation, and peak clipping mode, are proposed to dynamically regulate the operation of FTPSS. Moreover, a coordinated control strategy with a hierarchical structure is developed to enable the operation modes. The strategy is implemented with a global controller and multiple local controllers, which can realize real-time power management and flexible transition among different modes. Finally, different simulation and experimental case studies based on the field data are carried out to comprehensively verify the feasibility and effectiveness of the proposed FTPSS and control strategy. [ABSTRACT FROM AUTHOR]

• A multi-VPP energy-sharing model is proposed based on GNB theory. • The network transmission cost encourages the consumption of renewable energy nearby. • The non-convex GNB-based model is divided into two sub-problems by transformation. • A distributed method based on the fast-ADMM is developed. • A contributing factor for profit distribution is applied to endorse renewable energy. The benefits of small-scale renewable energy resources are restricted by a feed-in-tariff strategy that works with distribution grids only. Virtual Power Plant (VPP) aggregates diverse distributed energy resources on the demand side for efficient control, which can improve their revenue and encourage the growth of renewable energy. Based on the general Nash bargaining theory, this paper proposes a multi-VPP energy-sharing mechanism. This approach enables VPPs to increase their revenue by engaging in peer-to-peer energy transactions. First, a general Nash bargaining-based model is proposed for multi-VPPs energy sharing considering peer-to-peer energy transactions. The model is then decomposed into two subproblems: minimizing the total cost of the VPP energy-sharing alliance, and the cost allocation of peer-to-peer energy transactions. Considering the privacy protection of VPPs, the first subproblem is solved by a distributed algorithm based on the alternating direction multiplier method. In the second subproblem, due to the introduction of a bargaining factor that is positively related to each VPP's contribution to energy sharing alliance, the cost allocation better reflects their contribution. The numerical test demonstrates the proposed energy-sharing mechanism, with little computational complexity, delivers a cost distribution result that is comparable to the classical Shapley value while promoting the development of renewable energy resources. [ABSTRACT FROM AUTHOR]

• The concept of SPEV sharing is explored in the energy scheduling of clustering islands. • The space-temporal coupled characteristics of SPEV and battery packs are analyzed. • The energy scheduling and passenger scheduling constraints of SPEV are described. • An optimal energy scheduling model for clustering islands is established by considering the operational flexibility of SPEV. • Simulation results validate the effectiveness of the proposed model for the diversified operation demands of clustering islands. Pelagic clustering islands are rich sources of renewable energy. However, they are isolated by natural sea areas, making it difficult to realize the interconnectivity of energy generation and utilization among different types of clustering islands. This paper proposes the strategy of coordinated and optimal energy scheduling for pelagic clustering islands based on shared power exchanging vessels (SPEV). First, the energy interconnection model among different types of islands is analyzed based on the concept of SPEV sharing. Second, the space-temporal coupling of SPEV navigation is established by considering SPEV sharing transportation capacity and the charging-discharging state of mobile battery packs. On this basis, a clustering islands energy scheduling model is established to minimize the comprehensive operation costs of clustering islands, considering the battery pack charge–discharge constraints, SPEV navigation route constraints, and passenger traveling constraints. The established energy scheduling model fully explores the space-temporal energy transfer flexibility of SPEV considering meeting the travel demands of passengers. Finally, clustering islands in the South China Sea are considered in the case study, and the simulation results indicate that the proposed SPEV-based clustering islands energy scheduling strategy can effectively improve the renewable energy consumption rate and the operating economy of clustering islands. [ABSTRACT FROM AUTHOR]

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

• A new idea of utilizing fault transient energy of DFWT to provide emergency power support for the power system is proposed. • The dynamic model of rotor speed of DFWT in fault process is established, and the calculation method of fault transient energy of DFWT is proposed. • The calculation method of control duration time and reference value is proposed, and the transient energy emergency control method is proposed. The anti-interference ability of the power system continues to decline with the increase of wind power penetration. Due to the limitation of the reduction of the synchronous generators proportion and the response speed, the emergency power controllability of the power system with large-scale wind power is insufficient, and the stability problems of the power system continue to highlight under the grid fault. At the same time, wind turbines can hardly provide emergency support for the power system due to the maximum power point tracking mode. Therefore, the transient energy of doubly fed induction generator based wind turbine (DFWT) during the grid fault is depicted, which is accumulated by the imbalance between the input mechanical power and output electromagnetic power. And a new idea of utilizing the fault transient energy of DFWT to provide emergency power support for the power system is proposed in this paper. Firstly, the expression of stator short-circuit current of DFWT in the crowbar input and reactive power support stage is derived. The dynamic model of the rotor speed of DFWT is established further, and the calculation method of fault transient energy of DFWT is proposed. Next, in order to maximize the utilization of the transient energy, the calculation method of control duration time and control reference value is proposed, and the transient energy emergency control (TEEC) method of DFWT is proposed. The case studies and experimental results show that the TEEC method can not only provide stable power support for the power system, but also alleviate the short-term power impulse of DFWT after the grid fault removal, and accelerate the recovery of the rotor speed of DFWT, which is conducive to the safe and stable operation of DFWT and the power system. [ABSTRACT FROM AUTHOR]

Modular Multilevel Converter based High Voltage Direct Current (MMC-HVDC) is the preferred solution to the problem of long-distance transmission from large-scale offshore wind farms (OWFs). When an offshore AC asymmetric fault occurs, OWFs and MMC-HVDC will face the challenge of fault ride-through with power electronic converters on both sides of the fault location. In this paper, a Multi-stage Sequential Network Energy Control (MSNEC) is proposed for offshore AC asymmetric fault ride-through (FRT). Firstly, the constraint relationship among the sequence networks under AC asymmetric faults is revealed. Secondly, the energy control potential of WFMMC is fully explored from the perspective of energy transformation and transfer. Energy control reduces the fault phase current amplitude by putting in submodules at the fault phase. Additional sub-modules are put in the non-faulted phase to realize series voltage dividing effect to actively prevent non-faulted phase fault overvoltage. The MSNEC considers the FRT requirements for multiple stages of fault occurrence, fault steady state, and fault recovery. It combines sequential network control, energy control, and voltage support of the converter station. It realizes AC asymmetric FRT from four aspects: power equivalence, negative sequence suppression, energy control, and voltage stabilization. Finally, a simulation model of OWFs connected to the onshore grid via MMC-HVDC is established in PSCAD/EMTDC. Simulation results verify MSNEC not only improves the AC asymmetric FRT capability of the OWFs and WFMMC, but also promotes the efficient consumption of renewable energy and improves the economics of the OWFs grid-connected system. [Display omitted] [ABSTRACT FROM AUTHOR]

In order to ensure the stable energy supply for isolated island areas scattered around the globe, a reliable and flexible energy supply system, and corresponding efficiency and intelligent energy management strategies are indispensable. In this paper, power to gas(P2G) is introduced to promote the absorption of distributed renewable energy, and facilitate the deeply coupling of electric–gas networks in multi-energy complementary energy system. Besides, considering the inherent characteristics and actual coupling situation of each module, six possible cooperative game and non-cooperative game schemes are proposed, under the fixed time-sharing electricity price mechanism and dynamic incentive electricity price mechanism, to compare the differences in energy utilization efficiency, pollution emission level and total revenue. The final study results show that under the two price mechanisms, the synergetic response of fuel cell and gas turbine makes the curtailment rate of distributed renewable energy decreased by 5.68% and 3.76% respectively, and the output share of DG decreased by 5.77% and 7.36% respectively. It has realized efficient utilization of energy and emission reduction of pollutants. In terms of revenue, the cooperative game mode with dynamic electricity price mechanism increases the total revenue of the system by 25.02%. • A electric–gas deeply coupled comprehensive energy model is established. • Four comparative game modes are proposed and six sets of energy scheduling schemes are developed. • Optimal scheduling strategy of and island microgrid under dual electricity price mechanisms. • Users have a better electricity experience. • The absorption rate of distributed renewable energy is improved and emissions of polluting gases is reduced. [ABSTRACT FROM AUTHOR]

• Propose a distributed voltage control (DVC) scheme for unbalanced distribution networks with DGs. • Use preventive and corrective control modes to coordinate maximum energy usage of DGs and voltage regulation. • Design a novel mode switching method to eliminate severe voltages fluctuations caused by frequent mode switching. • Realize voltage information synchronization based on consensus through information exchange between adjacent controllers. This paper proposes a distributed voltage control scheme for unbalanced distribution networks (DNs) with single-/three-phase distributed generations (S/T-DGs) based on distributed model predictive control (DMPC). The active and reactive power outputs of each phase of DG units are optimally coordinated to regulate three-phase voltages of all buses to be close to the nominal value and mitigate voltage fluctuations. According to the operation conditions of three-phase DNs, two control modes are designed: preventive and corrective modes. In the preventive mode, the measured voltages of each phase are within the predefined limits, only the reactive power outputs of DG units are optimally adjusted to minimize the voltage deviations of each phase of all critical buses from the nominal value and mitigate the reactive power variations; while in the corrective mode, both the active and reactive power outputs of DG units are optimized to minimize the voltage deviations and the curtailed active power of DGs. The relationship between voltage variation of each phase and power injection is determined using an efficient analytical sensitivity calculation method. The DG controller only exchanges information with adjacent controllers and solves the local DMPC problem. A modified Finnish distribution network with 6 SDGs and 2 TDGs was used to verify the control performance of the proposed distributed voltage control scheme. © 2017 Elsevier Inc. All rights reserved. [ABSTRACT FROM AUTHOR]

With the large-scale application of microgrid technology in distribution networks, emerging energy transactions among multiple microgrids as well as between microgrids and the grid increase the operational burden to the power system. To coordinate these energy transactions, a coalitional game theoretic energy transaction algorithm for networked microgrids is developed in this paper to improve energy exchange efficiency. First, a distance-oriented method for maximizing coalition utilities is proposed to guide the order of transactions in a coalition, and the corresponding coalition utility would be fairly allocated by the Shapley value. Second, a modified coalition formation algorithm consisting of perturbation, merge and split procedures is designed to obtain a D c -stable otherwise a better D h p -stable partition on the network, in which no microgrid has an incentive to leave this partition, and the corresponding theoretical proof is given in this paper. Finally, relative to the non-cooperation mode and the Merge&Split procedure, simulation results show the advantages of the proposed algorithm in reducing costs and facilitating energy transactions in the network. • Solved the cooperative energy transaction problem for networked microgrids in the network. • A distance-oriented method is proposed to calculate the total utility for every valid microgrid coalition. • A perturbation procedure is introduced to obtain a D c -stable otherwise a better D h p -stable partition. • A theoretical proof for stability and optimality of the obtained coalition partition. [ABSTRACT FROM AUTHOR]

• The Stackelberg game operation framework with the IESO as the leader and IESs as the follower is constructed. • The IESO dynamic pricing model and IES operation optimization model based on Stackelberg game are established. • The existence and uniqueness of the Game equilibrium solution are proved. • The advantages of Game optimization from economic benefits and environmental costs are discussed. Integrated energy system (IES) brings new solutions to promote energy coupling and improve energy efficiency. However, with the transformation of energy market from traditional vertical integrated structure to interactive competitive structure, traditional optimization methods are difficult to reveal the game pattern among IESs. In this paper, a Stackelberg game model of integrated energy system operator (IESO) and integrated energy system (IES) is established to study the IESO dynamic pricing behavior and the IES operation optimization strategy. Firstly, this paper takes the IESO as the leader and each IES as the follower to construct the Stackelberg operation framework. Secondly, the Stackelberg game model between the IESO and the IESs is constructed and solved by the double mutation differential evolution algorithm, and the existence and uniqueness of the game equilibrium solution is proved. Finally, an integrated energy system park is taken as an example to demonstrate the rationality and effectiveness of the model established in this paper. The results show that, for IES, the method can achieve economic benefits while taking into account the environmental cost; and for IESO, it can achieve the IESs optimal energy efficiency while taking into account its own benefits. This paper can provide guarantee and theoretical basis for the IES operation optimization and the interaction between IESs and IESO in terms of algorithm and transaction mode. [ABSTRACT FROM AUTHOR]

• Based on the energy hub, the architecture of the park-level integrated energy system is constructed considering the coupling and conversion between multiple energy sources and the demand response on the user side. • A multi-objective robust model and opportunity model based on IGDT are established from the perspectives of risk aversion and risk preference, respectively. • Compared with the traditional carbon trading mechanism, the introduction of the ladder-type carbon trading mechanism can significantly reduce carbon emissions. In order to meet the challenge of global low-carbon development, the concept of integrated energy system (IES) has been proposed and demonstrated, and its low-carbon operation mode is the research hotspot at present. In this paper, a multi-objective optimal scheduling model considering the participation of park-level IES (PIES) in the carbon market is proposed, which takes into account the multiple uncertainties on the renewable energy and load. Firstly, for the PIES operator (PIESO) participate in the carbon market, this paper introduces the ladder-type carbon trading mechanism. Then, for the uncertainties of the renewable energy and load, this paper carries out uncertainty modeling from the perspective of PIESO risk aversion and risk preference based on IGDT method. Finally, a multi-objective optimal scheduling model including economic and environmental objective is established. The actual PIES is selected for case study, and the results show that the operating profit of PIESO under the risk aversion strategy decreases by 5.91%, the operating profit of PIESO under the risk seeking strategy increases by 6.12%. Aiming at the impact of ladder-type carbon trading on system operation profits and carbon emissions, three scenarios were set, including whether to introduce a ladder-type carbon trading mechanism, changes in the initial price and the tradable carbon emission ratio. The results show that compared with the traditional carbon trading mechanism, the introduction of the ladder-type carbon trading mechanism reduces carbon emissions by 2.73%, and the setting of reasonable ladder-type carbon trading price and tradable carbon emission ratio can significantly reduce the carbon emissions, improve operating profit of PIES. [ABSTRACT FROM AUTHOR]

• A novel smart soft open point (sSOP) is proposed to interconnect between railway network with distribution grid. • The sSOP provides additional flexibility and controllability for both networks. • An integrated simulation platform of rail and grid has been developed to evaluate the proposed sSOP. • The aggregated braking efficiency of the railway network is improved. • The local consumption of the RES in the LV grid is increased. • The energy consumption from the LV grid supplying point is reduced. This paper presents a novel smart soft open point (sSOP) that interconnects railway electrification system to the local low voltage (LV) distribution grid to provide additional flexibility and controllability for both networks. The proposed sSOP builds on the concept of soft open points, adding an energy storage system and embedding a rail and grid (R + G) energy management system to enable the energy transfer between the two networks at different average power levels. The smartness of the proposed soft open point is realised in the integration of an energy storage system to act as an energy buffer between these two networks of different dynamics. The paper demonstrates that the proposed sSOP improves the braking efficiency of trains and increase the local use of renewable energy through employing an integrated simulation platform between a railway and smart grid simulators that takes into account the trains' timetables as well as the daily variation of the grid loads and renewable energy generation. The results show that an appropriate design and control of the sSOP enables the recuperation of most of the braking energy available from the railway and that energy is enough to mainly supply the local power distribution grid, thereby effectively implementing the concept of smart grids. [ABSTRACT FROM AUTHOR]

• Improve the performance of micro-grid based on demand response programs. • Reduce operating costs of micro-grid based on probability distribution function models. • Improving the speed and accuracy of dynamic response system based on the Modified Shuffled Frog Leaping Algorithm. • Improving micro-grid performance in the presence of stochastic and non-stochastic power generation sources based on probability models. In this paper, an energy consumption optimization problem is investigated for a representative Micro-Grid (MG) system including Distributed Energy Resources (DERs) such as Wind Turbines (WTs), Photo-Voltaic (PV) arrays, Combined Heat and Power (CHP), Fuel Cell (FC), and boiler to supply the electricity and thermal load demand instantaneously. This problem is mathematically formulated considering resources and load uncertainties using Beta, Normal, and Weibull distribution function with suitable coefficients. In this regard, a model based on the minimization of objective function consisting of operating costs, pollution, and distribution lines electrical losses is presented and solved using the Modified Shuffled Frog Leaping Algorithm (MSFLA). The main objective of this paper is to choose the best collection of DERs and other resources in MG to study which of them is optimum to achieve the objective function. Demand response program (DR) based on the electricity price is considered as a Time of Use (TOU) tariff in which the simultaneous simulations will be figured out. The comparison of the proposed method with the existing method has verified the superiority of MSFLA approach to solve this optimization problem. [ABSTRACT FROM AUTHOR]

Electrification of the heat network in buildings together with a rise in popularity of Electric Vehicles (EVs) will result in a need to make investments in the electrical energy infrastructure in order to prevent congestion. This paper discusses the influence of hydrogen in future smart microgrids on these investments. Moreover, smart control strategies, i.e., EV management and demand response programs are used in this paper to lower the peak of electrical energy demand resulting in the reduction of these investments. Performances of microgrid with different levels of hydrogen penetration are discussed. It is shown that an increase in the level of hydrogen in the microgrid will reduce the electric grid investments costs but is not economically more beneficial than using 'green' gas due to the higher total economic costs. • Comparison battery and fuel cell electric vehicles in V2G operations. • Reduction of the peak of electrical energy transfer by hydrogen. • A construction of a future residential hydrogen-based energy infrastructure. • Hydrogen in a microgrid will increase the total economic costs for consumers. [ABSTRACT FROM AUTHOR]

• Hydropower is a renewable source of energy with low energy generation tariff and higher energy conversion efficiency. • Performance of a Pelton turbines are affected by silt erosion. • The correlations are developed based on the historical operation parameters which can be used to accurately predict the performance of a machine. • Artificial Neural Network (ANN) based correlations accurately predict the plant load (i.e., generated power). • Correlations can be utilized to prepare an effective O&M strategy for the real-time condition monitoring of the machine. Hydropower is a renewable and reliable energy source that can be utilized to fulfil energy demands and promote energy sustainability. In hydropower plants, Pelton turbines are installed at high-head and low-discharge sites. In Pelton turbines, needle, nozzle, buckets, and splitter are most exposed to silt erosion. An eroded turbine takes more discharge than rated for generating the same amount of power. Therefore, to minimize the losses due to silt erosion, real-time condition-based monitoring of a hydropower plant is necessary. In this paper, correlations are developed using the historical data collected from the Urgam hydropower plant to predict the generated power. The artificial Neural Network (ANN) technique has been used to develop correlation, and the performance of this model has been compared with the model developed using the Curve Fitting technique. Based on the comparison, it has been found that the ANN model performs better than the curve-fitting model in forecasting the generated power with an R2-value of 0.9522 and Mean Absolute Percentage Error (MAPE) of 2.40% at 3.1515% Root Mean Square Percentage Error (RMSPE). The developed model can be utilized to plan a maintenance schedule of machines based on real-time conditions. It can also help to decide which machine should be taken in maintenance first. [ABSTRACT FROM AUTHOR]

• Multi-source models help solve energy prediction for different types of buildings. • LSTM networks with multi-kernel maximum mean difference build multi-source prediction models. • Multi-source transfer learning models achieve better average prediction performance compared with single-source models. • Dynamic time warping relatively accurately measures the similarity between building energy consumption datasets. Transfer learning can use the knowledge learned from the operating data of other buildings to facilitate the energy prediction of a target building. However, most of the current research focuses on the transfer of a single source building of the same type or from the same region. A single source domain produces domain shift due to the difficulty of aligning the distribution with the target domain. To address this problem, this paper proposes a novel multi-source transfer learning energy prediction model based on long short-term memory (LSTM) and multi-kernel maximum mean difference (MK-MMD) domain adaptation. This model was used for the short-term energy prediction of different types of buildings lacking historical data. In addition, dynamic time warping (DTW) was used to select the source domain. Multiple multi-source models and corresponding single-source models were compared on a collection of buildings in the Higashida area of Fukuoka Prefecture, Japan. On the experimental datasets, the results showed that DTW relatively accurately measured the similarity between building energy consumption datasets. Compared with the results of the single-source transfer learning models, the multi-source transfer learning models achieved better average prediction performance, and their mean absolute percentage error (MAPE) improved the prediction accuracy by 6.88–15.37%. [ABSTRACT FROM AUTHOR]

Transmission power lines are essential to supply electrical energy to consumption centers. Keeping a reliable transmission system requires the early identification of faults. Image-based inspection of transmission lines makes fault identification faster and more accessible since it can be carried out using unmanned aerial vehicles (UAVs) in hard-to-reach places. In this paper, it is proposed to use a hybrid version of the You Only Look Once (YOLO) using ResNet-18 classifier, for power system inspection based on real images of failed components recorded by UAVs. This work assumed a dataset including 1,593 power grid inspection pictures for a supervised training. Based on YOLOv5x, with an mAP of 0.99262, the proposed method was superior to YOLOv5n, YOLOv5s, YOLOv5m, and YOLOv5l for object detection. For the multiclassification task, with an F1_score result of 0.96216, the proposed Hybrid-YOLO was superior to distinct architectures as the VGG-11, VGG-13, VGG-16, VGG-19, ResNet-34, ResNet-50, ResNet-152, DenseNet-121, DenseNet-161, DenseNet-169, DenseNet-201, YOLOv5, YOLOv6, and YOLOv7 versions. • The UAVs are used for the inspection of transmission lines in hard-to-reach places. • Real photographs from field inspections are used to evaluate faulty insulators. • The proposed strategy was compared to well-established methods. • The Hybrid-YOLO outperforms the standard YOLOv5, YOLOv6, and YOLOv7. • Based on ResNet-18 a hybrid method has a better F1_score than other YOLO versions. [ABSTRACT FROM AUTHOR]

• A multi-agent electricity-heat-hydrogen P2P trading optimization model is proposed. • Hydrogen is involved in P2P transactions and used for natural gas blending. • The multi-energy trading power and trading price are obtained based on Nash bargaining. • The bargaining cooperative interaction can benefit each agent simultaneously. The P2P transaction mode has significant potential in improving energy utilization, increasing economic efficiency, and promoting low carbon operation of the system. With the development of integrated energy system, the coupling between multi-energy systems is continuously strengthened, and the study of multi-energy P2P transactions is of great significance. Considering the characteristic that natural gas can blend with hydrogen, this paper proposes a multi-agent electricity-heat-hydrogen trading model by taking hydrogen produced on the load side as a P2P transaction object. Based on Nash bargaining theory, the multi-energy transactions model is equivalent to a cooperative game model. Then, the Nash bargaining model is transformed into two continuous subproblems of minimizing operating cost and maximizing transaction payment. The optimal cost of each agent operating independently is used as the negotiation rupture point, and the alternating direction multiplier method is used for sequential solution to obtain the multi-energy trading power and trading price, respectively. Finally, the effectiveness of the proposed method is verified by using the distribution network IEEE 33-bus and natural gas 11 node systems. The results show that the revenue of subject 1 is increased by 11.9%, and the operating costs of the other subjects are reduced by 3.4%, 2.7% and 3.1%, respectively. The overall carbon tax cost of the system is reduced by 1 196.59 CNY, which can effectively reduce the carbon emission of the system. [ABSTRACT FROM AUTHOR]

• A novel lightweight network constructed by two convolutional layers and dense layers, which approximately occupies 2.48 MB and requires 1.12 million floating point operations (MFLOPs). • The sequence-to-sequence learning method is introduced into the proposed network to monitor the state and power of concerned appliances in real-time. • More accurate appliance energy usage profile information is derived and provided for the edge users with continuous disaggregation results updates within defined time window. • Experiments on ground truth data validate the efficiency of the proposed model and demonstrate its application potential on edge devices. Non-intrusive load monitoring (NILM), extracting the appliances' usage profiles by decomposing a household's aggregate electricity consumption, has become increasingly important for home energy management. Studies show that users are more likely to adjust their usage behaviors if the real-time feedback on their energy consumption details is provided. Recent state-of-the-art deep learning models have delivered a good performance on load disaggregation; however, these models require tremendous computation and memory to tune huge numbers of parameters, which makes them difficult to be implemented on edge devices to derive real-time appliance usage information for users while keep user's privacy. In this paper, a lightweight sequence-to-sequence neural network model consisting of only two convolutional layers and dense layers is proposed, which can provide users with both real-time appliance power usage and improved energy consumption profiles. The online results are produced by picking up every end-point of output sequences of the network, whereas the past energy profiles or offline results of appliance can be obtained by averaging all points of the output sequence. By implementing the model in edge device, the user's privacy is maintained. The lightweight NILM algorithm is evaluated with low-frequency sampling data in two public available datasets: UKDALE and REFIT. Assessment results prove that the proposed model exhibits comparable or even better disaggregation performance with dramatically reduced model size and computation requirements, and thus demonstrates its application potential on edge devices. [ABSTRACT FROM AUTHOR]

This paper proposes a multi-stage hybrid energy management strategy for multiple microgrids (MMGs) to reduce energy abandonment and load losses. The proposed energy management model consists of three stages including a decentralized autonomy stage, a coordinated operation stage, and a reserve reallocation stage, respectively. First, in the decentralized autonomy stage, a day-ahead energy management model is established with the objective of minimizing the comprehensive management cost of individual microgrids. Especially, the uncertainty from renewable energy and load demands are quantified by interval forecast algorithm, phase space reconstruction technique, machine learning, and kernel density estimation, which simplifies the forecasting processes while capturing multivariate data more comprehensively. Second, the coordinated operation stage aims to encourage the power interaction among MMGs to achieve maximize the benefit of MMGs while suppressing the power fluctuations of the tie line between the MMGs and the main grid. Then, in the reserve reallocation stage, taking the actual values of energy abandonment and load losses of a week before the management day as the dataset, machine learning algorithms are applied to predict energy abandonment and load losses. Meanwhile, the actual energy abandonment and load losses are reduced by dispatching reserve resources. Finally, simulations on an MMGs system containing four microgrids are conducted to testify the rationality and validity of the proposed energy management strategy. [ABSTRACT FROM AUTHOR]

• Interconnected transactive MGs (ITMGs) and their day-ahead scheduling is discussed. • The resilient operation is considered during the main grid disconnections. • A two-stage MILP model is introduced to solve in Robust-stochastic framework. • Both the 24-hour operational cost and resilient operation of the ITMG are studied. • Emergency demand response program and its trigger condition is discussed. The interconnected transactive MGs (ITMGs) concept is an efficient way for economical operation of MGs. For power systems under the danger of stochastic disasters, a priority should be given to the resiliency enhancement of these ITMGs to have the highest restoration in the times of disconnection from the main grid. This paper introduces a two-stage hybrid stochastic programming/robust optimization (SP/RO) day-ahead scheduling of ITMGs that takes into account the possibility of disconnection from the main grid for several hours. The proposed resiliency-oriented scheduling in this paper is a two-stage mixed-integer linear optimization problem and aims to reduce the operation cost of the whole ITMG. Furthermore, the excess power can be traded among MGs apart from the available power of renewable resources, dispatched power of storage and dispatchable units, and demand response programs. Thus, this scheduling needs to consider the optimal hourly transactive energy among the MGs in both normal and grid disconnected modes of the ITMG. The load curtailment and demand response programs are the last options for maintaining the balance in the conventional structure of MGs that this paper utilizes for ITMG. Obtained results showed that load curtailment of the ITMG is limited by the implementation of transactive energy between MGs. Furthermore, the reduction in renewable and dispatchable generation capacity and their impact on transactive energy and demand response programs during resiliency is discussed. Also, to enhance the conservativeness and to consider the worst cases in this scheduling, the uncertainties in the start time of the islanding and its duration are considered in the problem. [ABSTRACT FROM AUTHOR]

• This paper proposes a protection scheme based on line attenuation characteristics. • The proposed protection scheme does not rely on boundary elements. • The proposed protection scheme has stronger resistance to fault resistance and noise. The line-commutated converter-voltage source converter (LCC-VSC) three-terminal DC transmission system is one of the effective means for large-scale new energy transmission and consumption, and a fast and reliable protection scheme is the key to its reliable operation. However, the LCC-VSC DC system has characteristics such as LCC converter and VSC converter control strategies, fault response, and uneven boundary elements, which makes it difficult to directly apply the existing protection principles. To the above problems, this paper first proposed a single-ended protection principle based on line attenuation characteristics. The protection principle uses the ratio of the high and low-frequency signals of the line mode voltage to express the line attenuation characteristics and uses this ratio to identify internal and external faults. A model of the LCC-VSC three-terminal transmission system is established to verify the feasibility and effectiveness of the proposed protection scheme. The simulation results show that the proposed protection principle can accurately identify internal faults and external faults and has a strong ability to withstand fault resistance and noise interference. [ABSTRACT FROM AUTHOR]

• Blockchain should be implemented based on practical demands of energy systems. • Limitations of blockchain and their impacts on energy systems are analyzed. • Potential directions for energy systems to tackle these impacts are summarized. Recently, the blockchain technology has attracted widespread attention due to its advantageous features, e.g., decentralization, transparency, traceability, and immutability. To make full use of renewable energy resources, new generation energy systems advocate the deep integration of information technology in real-world energy projects, among which blockchain has become one of the most used technologies. However, with the continuous development of related studies and projects, blockchain has begun to expose more and more limitations. As a result, the application of energy blockchain, i.e., blockchain applied in energy systems, is facing various challenges caused by these limitations. This paper briefly reviews popular application scenarios of energy blockchain, analyzes generic limitations of blockchain and their impacts on energy systems, and looks into possible directions that could deal with these limitations for future blockchain-based energy systems. We argue that blockchain is not a panacea for energy systems because blockchain's component technologies have their own generic issues, and that the application of energy blockchain should be accompanied with improvement measures that conform to practical requirements of energy systems. As far as we know, this paper is one of the few works that deeply analyze the shortcomings of the blockchain technology and potential countermeasures in the energy field. [ABSTRACT FROM AUTHOR]

For implementing Autonomous Demand Side Management (ADSM) in the smart grid, when there is a large number of consumers, there are some problems like low convergence speed and large volume of calculation for finding the optimal amount of energy consumption in any time slot. In this paper, we propose a new method to speed up the convergence of ADSM algorithms and to reduce the volume of computations. When the consumers try to calculate their energy consumption individually in parallel, a problem of peak shifting will be appeared, which causes divergence or slow convergence rate in the ADSM algorithm. Therefore, we modify the ADSM algorithm such that the consumers can avoid peak shifting while making decisions in parallel. The proposed algorithm increases the convergence rate via solving a primal problem instead of the main problem, and decomposing it for each consumer to solve it in parallel. The proposed algorithm is compared with the ADSM algorithms suggested for the same system model in the literature. Our results show a significant improvement in convergence rate and computation volume. In summary, the proposed algorithm is more than 7.5 times faster than the existing algorithms and reduces the number of required operations by more than 2.8 times. • The parallel computing algorithms and dual decomposition method are combined in ADSM. • ADSM algorithms are proposed that prevent peak shifting with acceptable convergence. • High speed convergence ADSM algorithm without central load scheduling server. [ABSTRACT FROM AUTHOR]

• Coordinating conventional Volt-VAr devices with distributed energy resources. • Incorporating demand response program into the Volt-VAr control problem. • Utilizing ε-constraint approach to generate Pareto optimal solutions. • Using IGDT to develop a robust decision-making approach. • Developing a mixed-integer linear programing framework. Distributed energy resources (DERs), such as renewable energy sources (RESs) and energy storage systems (ESSs), are inevitable parts of future power grids. However, the high penetration of RESs may cause voltage violation issues in distribution levels. To this end, the application of the Volt-VAr management (VVM) scheme is highlighted. This paper aims to maintain voltage levels within standard ranges with optimal coordination of different resources at minimum operational costs and voltage deviation. In more detail, this article develops a new VVM in distribution networks to effectively coordinate novel smart grid technologies such as PV inverters, time of use demand response program (TOU-DRP), energy storage systems (ESSs) with traditional devices, including on-line tap changer transformer (OLTC) and switchable capacitor banks (SCBs). Because the coordinated VVM is a bi-objective model that minimizes both operating cost and voltage deviation, it is proposed to use the ε-constraint technique to identify optimal Pareto solutions. To handle the uncertainty related to RESs and load demands, a robust technique based on information gap decision theory (IGDT) is also utilized. The formulation is configured as a mixed-integer linear program (MILP) and solved by CPLEX. It was implemented on the 118-bus distribution network under various conditions. The simulation results clearly show that the coordination of DERs and traditional devices has a significant impact on maintaining voltage levels within permissible ranges while minimizing operational costs. A higher degree of uncertainty radius can also be obtained if DRP participates in the VVM scheme. [ABSTRACT FROM AUTHOR]

The large-scale renewable energy integration has brought challenges to energy management in modern power systems. Due to the strong randomness and volatility of renewable energy, traditional model-based methods may become insufficient for optimal active power dispatch. To tackle the challenge, this paper proposes an autonomous control method based on soft actor–critic (SAC), a deep-reinforcement learning (DRL) strategy recently developed, which provides an optimal solution for active power dispatch without a mathematical model while improving the renewable energy consumption rate under stable operation. A Lagrange multiplier is introduced to the SAC (LM-SAC) to promote algorithm performance in optimal active power dispatch. A pre-trained scheme based on imitation learning (IL-SAC) is also designed to further improve the training efficiency and robustness of the DRL agent. Simulations on the IEEE 118-bus system with the open platform Grid2Op verify that the proposed algorithm effectively achieves better renewable energy consumption rate and robustness compared with existing DRL algorithms. • SAC is used in power systems, which realizes real-time optimal dispatch of power. • LM-SAC based on the Lagrange multiplier method is proposed to improve SAC. • A pre-trained scheme based on imitation learning is designed to propose IL-SAC. [ABSTRACT FROM AUTHOR]

A renewable energy consumption method based on variational mode decomposition and combined regulation of thermal power and storage system is presented in this paper. By decomposing the equivalent load curve using variational mode decomposition, the equivalent load curve is divided into parts with different center frequencies and energy system constraints and solved separately. We then take the maximum wind power absorption rate as the upper model target and solve it. At the same time, through the operation curves of the thermal power unit and battery storage system, the lower optimization model is established to minimize the system's operating cost with carbon emission costs. The example shows that the regulation method with a storage system improves the consumption rate of wind power and reduces carbon dioxide emission compared with the regulation method without a storage system. Compared with the method without decomposition, the power output curve of the thermal power unit is smoother under the new method, effectively improving the solving speed. It reduces the operating cost of the system and its carbon dioxide emissions. • Variational mode decomposition of equivalent load curve and analysis of its characteristics. • Upper levels model improves wind power accommodation rate. • Lower levels model reduces carbon emissions and operating costs of thermal power units. • Frequency block adjustment makes output curve of thermal power unit smoother. [ABSTRACT FROM AUTHOR]

• The electrical equipment was divided into time-shifting and non-time-shifting power. • The starting time of time-shifting load was selected as the variable in PSO algorithm. • The battery capacity was selected to meet the demand of the power peak at night. With the continuous expansion of the development scale, photovoltaic integrated greenhouses are becoming an important type of smart grid prosumer. The demand side management (DSM) of energy is becoming increasingly important in terms of satisfying the energy consumption requirements of production and increasing the economic benefits of the same piece of land. By analyzing greenhouse production activities and the operation of electrical equipment, a particle swarm optimization (PSO) scheme is proposed in this paper, which selects the starting time of time-shifting powers as optimization factors and maximizes the income of power generation as the objective function. In addition, this paper analyzes the problem of energy consumption scheduling in greenhouse production. The results show that this scheme helps to optimally schedule the greenhouse energy consumption and achieves maximum profit by ensuring the reasonable operation of the system. The optimization scheme is beneficial to guide the production activities in greenhouses and the construction of photovoltaic integrated greenhouse systems. [ABSTRACT FROM AUTHOR]

• Optimal capacity management (OCM) to facilitate the energy accounting and local energy market (LEM). • OCM and its relation to an LEM based on blockchain technology. • Voltage management using OCM to preemptively ensure good power quality. • Validation of the methodology using test and real feeder located in Austria with real measurements. This paper presents a methodology to optimally share the available grid capacity among customer assets connected within a low voltage distribution grid. Distributed energy resources (DERs) and a new generation of loads such as heat pumps, thermal, hydrogen, electric storages, and vehicles are increasingly being connected to distribution grids. These DERs and loads are intermittent and it is essential to optimally control them for the safe operation of the grid. Additionally, there is increased interest in the local generation, production, trading, and consumption of energy. New regulations to establish local energy communities (LEC) have come to fruition among member nations across Europe. This is to provide a control, market, and legal framework for managing such distributed generators and flexibilities in low and medium-voltage distribution grids and conclusively empower end-users to democratize the energy system. Within a LEC, a local energy market (LEM) is to be implemented. A significant constraint of a LEM or energy accounting system is the grid settlement process. The grid should remain in a steady state when the bids in the market are executed. The methodology discussed in this paper will preemptively stabilize the grid and generate limiting profiles at various locations for individual flexibilities that are part of the local energy market. This is achieved by using an Optimal Capacity Management system which generates limiting profiles at the points of common couplings of various controllable devices in the grid. The controllable devices are required to maintain their active power injection and consumption within the generated limiting profiles to ensure optimum grid level. This will ensure that grid limits are maintained, which are simulated on a test feeder and also applied to a real network model from the Heimschuh pilot site in Styria, Austria. [ABSTRACT FROM AUTHOR]

Climate change prompts humanity to look for decarbonisation opportunities, and a viable option is to supply electric vehicles with renewable energy. The stochastic nature of charging demand and renewable generation requires intelligent charging driven by predictions of charging behaviour. The conventional prediction models of charging behaviour usually minimise the quadratic loss function. Moreover, the adequacy of predictions is almost solely evaluated by accuracy measures, disregarding the consequences of prediction losses in an application context. Here, we study the role of asymmetric prediction losses which enable balancing the over- and under-predictions and adjust predictions to smart charging algorithms. Using the main classes of machine learning methods, we trained prediction models of the connection duration and compared their performance for various asymmetries of the loss function. In addition, we proposed a methodological approach to quantify the consequences of prediction losses on the performance of selected archetypal smart charging schemes. In concrete situations, we demonstrated that an appropriately selected degree of the loss function asymmetry is crucial as it almost doubles the price range where the smart charging is beneficial, and increases the extent to which the charging demand is satisfied up to 40%. Additionally, the proposed methods improve charging fairness since the distribution of unmet charging demand across vehicles becomes more homogeneous. • The paper shows the crucial role of asymmetric prediction losses for smart charging. • We propose a methodology to quantify consequences of prediction losses. • Archetypal smart charging schemes are used to evaluate the prediction models in application context. • The asymmetric predictions almost double the range of prices where the time-of-use scheme is viable. • The energy demand gets satisfied to a higher extent and with improved fairness. [ABSTRACT FROM AUTHOR]