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The state-of-the-art centralized computing framework applied to the optimal market dispatch of energy storage systems (ESS) aggregates data from local ESS units for training on the cloud server due to the limited computing resources on edge. However, this approach poses several challenges, including the lack of joint optimization for multiple ESS units, susceptibility to single point of failure and attacks, and inadequate data privacy protection for ESS owners. This study proposes an adaptive personalized federated reinforcement learning (FRL) for multiple-ESS optimal dispatch in various electricity markets with electric vehicle and renewable energy, achieving both the joint optimization of multiple ESSs and avoiding the degraded performance of FRL's local model. Under an adaptively ESS-related differential privacy protection, local devices and the cloud server are specialized to form multiagent deep reinforcement learning (DRL) model for bidding energy, regulation, and third-party services and update the global models, respectively. Given the adaptability of personalization layer to different agents and clients, an adaptive personalization method is developed by calculating the number of personalization layers with the relative loss of each agent and client in the iteration process. The case study shows that the adaptive personalized FRL outperforms conventional FRL, DRL and optimization algorithms. [Display omitted] • FRL-based algorithm for multiple-ESS optimal market dispatch. • Multiagent DRL model for bidding energy, regulation, and third-party services. • Adaptive personalization layers with the relative loss of each agent and client. • Adaptively ESS-related differential privacy method. [ABSTRACT FROM AUTHOR]

Hydrogen as a low-carbon clean energy source is experiencing a global resurgence and has been recognized as an alternative energy carrier that can help bring the world to a carbon neutral future. However, getting to scale is one of the main challenges limiting the growth of the hydrogen economy. In particular, the high cost of transporting green hydrogen is bottlenecking the international trading and wider adoption of hydrogen for global carbon natural objectives. In order to explore incentives for the global hydrogen economy and develop new pathways towards the carbon neutral future, the concept of hydrogen credit is proposed by this research and a framework of trading hydrogen credits similar to carbon credits in the international market is established. This research aims to contribute to the overall uptake of green hydrogen financially rather than relying on the physical production, transportation, and storage of hydrogen. Case studies are presented to demonstrate the feasibility and efficiency of the proposed hydrogen credit framework, as well as the great potential of a global hydrogen credit market. • The concept of hydrogen credit is proposed the first time. • The green hydrogen credit system is closely coupled with the carbon credit market. • A framework of trading hydrogen credits is designed to stimulate hydrogen economy. • This work contributes to the global uptake of green hydrogen financially. • It provides a new pathway towards the net-zero emission/carbon neutral future. [ABSTRACT FROM AUTHOR]

Due to low electricity rates at nighttime, home charging for electric vehicles (EVs) is conventionally favored. However, the recent tendency in support of daytime workplace charging that absorbs energy produced by solar photovoltaic (PV) panels appears to be the most promising solution to facilitating higher PV and EV penetration in the power grid. This paper studies optimal sizing of workplace charging stations considering probabilistic reactive power support for plug-in hybrid electric vehicles (PHEVs), which are powered by PV units in medium voltage (MV) commercial networks. In this study, analytical expressions are first presented to estimate the size of charging stations integrated with PV units with an objective of minimizing energy losses. These stations are capable of providing reactive power support to the main grid in addition to charging PHEVs while considering the probability of PV generation. The study is further extended to investigate the impact of time-varying voltage-dependent charging load models on PV penetration. The simulation results obtained on an 18-bus test distribution system show that various charging load models can produce dissimilar levels of PHEV and PV penetration. Particularly, the maximum energy loss and peak load reductions are achieved at 70.17% and 42.95% respectively for the mixed charging load model, where the system accommodates respective PHEV and PV penetration levels of 9.51% and 50%. The results of probabilistic voltage distributions are also thoroughly reported in the paper. [ABSTRACT FROM AUTHOR]

Overhead cables are widely applied in power distribution and transmission networks due to their financial and geographical merits. But since the environment issue has become a general concern, negative impacts of overhead cables are no longer negligible. Although overhead transmission lines are gradually being replaced by underground cables for the purpose of improving the security and stability of power transmission system, a large number of overhead cables are still in use. If buildings are built below the high voltage cables, people who stay inside will be adversely affected by power frequency magnetic fields. In order to solve this issue, our paper studies the distribution of magnetic field produced by a 500 kV cable, and proposes a shielding method to reduce the indoor field intensity. In this research, field distribution is analyzed through simulation, and the maximum indoor field intensity is calculated and compared with safety limits in the guide rule of limitation. Shielding method proposed in this paper provides good protection performance and halves the indoor intensity of magnetic field produced by the 500 kV overhead cable. [ABSTRACT FROM AUTHOR]

Deregulation of the retail electricity market has led to the emergence of an increasing number of electricity plans with competitive rates. Electricity customers now have more flexibility in choosing an electricity provider and electricity plan based on individual consumption needs. In this paper, a feature engineering hybrid collaborative filtering-based electricity plan recommender system (FECF-EPRS) is proposed for helping the customer get the right electricity plan. This system is composed of three-segment models for missing feature estimation, feature crosses construction, and electricity plan recommendation. It only takes easy-to-obtain household appliance usage features as inputs and outputs ratings for different plans. Through the test of real electricity market data, the FECF-EPRS shows a greater improvement in terms of recommendation accuracy, which can provide more accurate recommendations to customers and more reasonable pricing references for retailers. • Developing an electricity tariff recommender system for customers with different household energy consumption patterns. • Proposing a missing feature value filling method based the link between different appliances. • Introducing a Feature Crosses method to enhance recommendation accuracy. [ABSTRACT FROM AUTHOR]

• A novel hierarchical control method for EVCSs in an unbalanced distribution network. • A three-phase EV charging scheduling optimization model to maximize PV hosting capacity. • Ancillary service functions of active power transfer and voltage droop control. PV hosting capacity of a practical three-phase unbalanced distribution network is expected to increase, thus increasing safe PV penetration and speeding up the decarbonization of power systems. However, power unbalances and bus voltage violations are two major obstacles limiting further increases in the PV hosting capacity. With the popularization of electric vehicles (EVs), EV charging stations (EVCSs) as controllable loads can effectively provide grid ancillary services, which helps enhance the PV hosting capacity. In this regard, this paper proposes a hierarchical control method for EVCSs in an unbalanced network, consisting of central day-ahead scheduling and local real-time dynamic control. It is novel that the local control capability of EVCSs is reserved in the central scheduling. A three-phase day-ahead EV charging scheduling optimization model is developed to reduce the power unbalance and the bus voltage violation, and in turn, improve the PV hosting capacity. Moreover, active power transfer and voltage droop control are developed as two ancillary service functions for local real-time control responding to dynamic network operating conditions. Numerical simulations verify the effectiveness of the proposed method in enhancing PV hosting capacity and providing grid ancillary services. [ABSTRACT FROM AUTHOR]

The global resurgence of hydrogen as a clean energy source, particularly green hydrogen derived from renewable energy, is pivotal for achieving a carbon-neutral future. However, scalability poses a significant challenge. This research proposes innovative business models leveraging the low-emission property of green hydrogen to reduce its financial costs, thereby fostering its widespread adoption. Key components of the business workflow are elaborated, mathematical formulations of market parameters are derived, and case studies are presented to demonstrate the feasibility and efficiency of these models. Results demonstrate that the substantial costs associated with the current hydrogen industry can be effectively subsidized via the implementation of proposed business models. When the carbon emission price falls within the range of approximately 86–105 USD/ton, free access to hydrogen becomes a viable option for end-users. This highlights the significance and promising potential of the proposed business models within the green hydrogen credit framework. • Business models for trading green hydrogen credits are designed the first time. • It provides innovative financial solutions to decrease the cost of green hydrogen. • Incentives of utilizing green hydrogen are created using proposed business models. • The adoption of proposed trading framework can stimulate global hydrogen economy. • This work accelerates the progress towards a carbon-neutral future. [ABSTRACT FROM AUTHOR]

Highlights • Propose a joint planning model for active distribution systems. • Develop a new stochastic modelling approach for renewable energy. • Develop a new multi-objective optimization method for the proposed model. Abstract This paper proposes a multi-objective joint planning model for Active Distribution Networks (ADNs). The model determines the location and size of Electric Vehicle Charging Stations (EVCSs), Renewable Energy Sources (RESs), Battery Energy Storage System (BESSs), and distribution network expansion schemes, with the objectives of minimizing the total investment and reliability cost of the distribution network and maximizing the EVCSs' charging service capability. A scenario-based stochastic modelling approach based on Wasserstein distance metric and K-medoids scenario analysis is developed to model the stochastic nature of renewable generation. A multi-objective optimization algorithm, Multi-Objective Natural Aggregation Algorithm (MONAA), is applied to solve the proposed model. Case studies are conducted on a coupled 54-node distribution system and 25-node traffic system to validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Recently, a SU(4) π -flux Hubbard model on the honeycomb lattice has been proposed to study the spin-orbit excitations of α -ZrCl 3 (Yamada et al., 2018 [27]). Based on this model with a zigzag edge, we show the edge defects can induce edge flat bands that result in a SU(4) edge ferromagnetism. We develop an effective one-dimensional interaction Hamiltonian to study the corresponding SU(4) spin excitations. Remarkably, SU(4) spin excitations of the edge ferromagnet appear as a continuum covering the entire energy region rather than usual magnons. Through further entanglement entropy analysis, we suggest that the continuum consists of fractionalized spin excitations from the disappeared magnons, except for that from the particle-hole Stoner excitations. Moreover, in ribbon systems with finite widths, the disappeared magnons can be restored in the gap formed by the finite-size effect and the optical branch of the restored magnons are found to be topological nontrivial. • Verification of the SU(4) edge ferromagnetism in Dirac semimetals. • Certification of the fractionalized spin excitations of SU(4) edge ferromagnets. • Possible realization of ferromagnetic Luttinger liquid. [ABSTRACT FROM AUTHOR]

The leading-edge technology of Digital Twin (DT) presents potential solutions for challenges associated with renewable energy resources (RER), particularly solar energy, such as optimal management, random nature and unpredictability, maintenance, security, and energy efficiency. These issues are more elevated today due to the widespread adoption of solar energy in the power system. A DT leverages advanced technologies including the Internet of Things (IoT), artificial intelligence, and computing techniques, to observe and confirm the state of physical entities, analyze data and derive valid information to monitor and optimize the entity's operation. This Perspective strives to trace the growing body of advances in the field and proposes opportunity areas for DT of RER (RERDT) and DT of solar energy (DTSE) in various application domains including forecasting, reliability analysis, security, and resiliency. Barriers that hinder the adoption of RERDT and DTSE technologies to meet its continuous operation are discussed, and also possible views as future trends to deal with the challenges are presented. RERDT and DTSE technologies promise to be transformative in enabling flexible and sustainable energy sources as well as in active real-time management since it provides a full comprehension of the resources. [ABSTRACT FROM AUTHOR]

The low-carbon economic operation of integrated energy systems (IES) cannot be separated from the carbon trading and green certificate trading market. Therefore, a low-carbon market mechanism and optimization method for IES is proposed. First, we establish a "green-carbon" offset mechanism to realize the conversion from tradable green certificate (TGC) to carbon quotas to offset system's carbon emissions. Second, considering the impact of market incentives and users' consumption behavior on IES, an energy management method of IES is put forward based on Stackelberg game. IES operators as leaders decide energy prices and trading strategies of carbon, TGC and multi-energy. Energy users as followers participate in the integrated demand response based on energy price. The game is solved by an improved adaptive catastrophic genetic algorithm and CPLEX solver. Finally, we take an industrial park in China as an example to analyze. The results show that the proposed method can significantly reduce the system's carbon emissions while improving the benefits for both IES and consumers. • A mechanism is proposed to offset system carbon emissions with green certificates. • An intelligent pricing and optimal method is proposed based on Stackelberg game. • Carbon capture and power-to-gas technology are used to reduce carbon emissions. • Key parameters sensitivity of the offset mechanism is analyzed for specific cases. [ABSTRACT FROM AUTHOR]

• A novel T-DAD model is proposed for typhoon disaster scenario. • Besides line hardening, we incorporate DG UC into T-DAD's 1st level. • UC and reconfiguration strategies are extended to 24 h instead of load peak periods. • We simulate typhoon "Mangkhut" (2018) with considering more resilience elements. Extreme natural disasters such as typhoon often cause failures in distribution networks within a short time, and even lead to large area blackouts. We propose a tri-level robust optimization framework that combines pre-disaster, in-disaster and post-disaster strategy comprehensively to enhance distribution network resilience. The typhoons are regarded as attackers, and a tri-level Defender-Attacker-Defender model for typhoon disaster is used to integrate multiple resilience resources. In the first level, line hardening and distributed generation unit commitment are used to improve resilience in pre-disaster. The second level contains attack budget, attack time, hardening budget, repair time and load loss. It couples with the third level iteratively to generate the worst failure scenario under typhoon. In the third level, the Nested Column-and-Constraint Generation algorithm is used to solve distribution network reconfiguration and intentional islanding. Simulation time is spanned to the entire 24-hour disaster day. In the end, the proposed framework is tested through a case study using real data from super typhoon "Mangkhut" (2018) in Yangjiang, China. The result shows that it can effectively reduce load loss under the worst typhoon scenario and enhance distribution network resilience with limited resources. [ABSTRACT FROM AUTHOR]

• Static and dynamic image processing architectures are proposed for solar forecast; • Hybrid sky image-based forecasting models are effectively developed and verified; • Noticeable improvements for solar ramp event forecasting have been achieved. With the ever-increased penetration of solar energy in the power grid, solar photovoltaic forecasting has become an indispensable aspect in maintaining power system stability and economic operation. At the operating stage, the forecasting accuracy of renewables has a direct influence on energy scheduling and dispatching. In this paper, we propose a series of novel approaches based on deep whole-sky-image learning architectures for very short-term solar photovoltaic generation forecasting, of which the lookahead windows concern the scales from 4 to 20 min. In particular, multiple deep learning models with the integration of both static sky image units and dynamic sky image stream are explicitly investigated. Extensive numerical studies on various models are carried out, through which the experimental results show that the proposed hybrid static image forecaster provides superior performance as compared to the benchmarking methods (i.e. the ones without sky images), with up to 8.3% improvement in general, and up to 32.8% improvement in the cases of ramp events. In addition, case studies at multiple time scales reveal that sky-image-based models can be more robust to the ramp events in solar photovoltaic generation. [ABSTRACT FROM AUTHOR]

Most existing data-driven power system short-term voltage stability assessment (STVSA) approaches presume class-balanced input data. However, in practical applications, the occurrence of short-term voltage instability following a disturbance is minimal, leading to a significant class imbalance problem and a consequent decline in classifier performance. This work proposes a Transformer-based STVSA method to address this challenge. By utilizing the basic Transformer architecture, a stability assessment Transformer (StaaT) is developed as a classification model to reflect the correlation between the operational states of the system and the resulting stability outcomes. To combat the negative impact of imbalanced datasets, this work employs a conditional Wasserstein generative adversarial network with gradient penalty (CWGAN-GP) for synthetic data generation, aiding in the creation of a balanced, representative training set for the classifier. Semi-supervised clustering learning is implemented to enhance clustering quality, addressing the lack of a unified quantitative criterion for short-term voltage stability. Numerical tests on the IEEE 39-bus test system extensively demonstrate that the proposed method exhibits robust performance under class imbalances up to 100:1 and noisy environments, and maintains consistent effectiveness even with an increased penetration of renewable energy. Comparative results reveal that the CWGAN-GP generates more balanced datasets than traditional oversampling methods and that the StaaT outperforms other deep learning algorithms. This study presents a compelling solution for real-world STVSA applications that often face class imbalance and data noise challenges. [Display omitted] • The class imbalance issue is addressed using balanced samples generated by CWGAN-GP. • StaaT with multi-head self-attention mechanisms learns important features. • SFCM labels samples undeterminable directly by domain knowledge. • StaaT exceeds other methods, showing robustness amid class imbalances and noises. [ABSTRACT FROM AUTHOR]

Real-time industrial carbon emission estimation aims to estimate emissions more accurately to promote carbon reduction and mitigate climate change. Compared with input-output-based (IOA) analysis methods, the process-based analysis (PA) methods provide more specific information to decision-makers based on extensive detailed data. However, the required data is hard to obtain and normally contains missing data. To address these challenges, this paper proposes a novel deep learning-based carbon emission estimation framework to track the emissions of industrial customers in smart grids with smart meter data. The proposed framework encompasses three pivotal stages: data imputation, device recognition, and emission estimation—collectively referred to as DI-DR-EE. Specifically, the Data Imputation Network (DINet) based on super-resolution perception (SRP) is first introduced to recover the missing smart meter data. Then the recovered data is used to recognize the device states through the Device Recognition Network (DRNet), which thrives upon subspace blueprint separable convolutions (BSConv-S) to elevate the accuracy of device recognition with low-frequency data, all the while optimizing computational efficiency. Finally, the direct emission estimation is conducted based on the device states, and the indirect emission is estimated based on the power consumption. Case studies with five factories connected to the IEEE 57-bus system have verified the effectiveness of the proposed framework. The model training process was executed using Python with PyTorch version 1.8.1, coupled with Cuda 11.1 for accelerated computations. Results underscore that DINet and DRNet outperform established benchmarks, while DI-DR-EE remarkably maintains its capacity to attain estimations within a 10% margin of error, even when grappling with up to 90% missing meter data. • A novel deep learning-based carbon emission estimation framework is proposed to track the emissions of industrial customers. • A Data Imputation Network (DINet) is introduced to reconstruct missing data more efficiently. • A Device Recognition Network (DRNet) is proposed to identify the device states more accurately. • Real-time emission estimation is realized even with incomplete and low-frequency smart meter data. [ABSTRACT FROM AUTHOR]

This article investigates the load frequency control problem of interconnected power systems by proposing a distributed fault-tolerant proportional integral (PI) control strategy. Firstly, a unified actuator fault model is established, and a distributed fault-tolerant PI control strategy is proposed on the basis of the actuator fault model. Then, a stochastic event-triggered scheme (SETS) based on stochastic sampling period is developed to relieve the redundant occupation of network communication resources. Further, an asymptotical stability criterion with H ∞ performance is established by means of the Lyapunov method. Finally, illustrative examples are presented from two cases, isolated power system and interconnected power systems, to demonstrate the effectiveness of the designed control approach and the superiority of SETS compared with periodic event-triggered scheme. A unified model for interconnected power systems and an actuator fault model are established, and then a distributed fault-tolerant PI controller is designed. An SETS is proposed based on a novel defined stochastically updated sampling period to characterize the communication behaviour. [ABSTRACT FROM AUTHOR]

Highlights • Comprehensive model of different CHP technologies with various operation modes. • Two-stage dispatch considering day-ahead heat market and real-time power balancing. • Heat accumulator's contribution to flexibility is quantified. • Two fold flexibility of CHP is investigated and compared among four seasons. • We provide stakeholders with insights into CHP's flexibility for balancing services. Abstract Denmark has the ambitious goal of achieving 100% renewable electricity and heating sectors by 2035. Coupling these two energy sectors, combined heat and power (CHP) plants play an important role in providing flexibility in terms of economic dispatch of heat production and balancing power systems with high penetration of intermittent renewable like wind power. In this paper, a twofold flexibility potential of different CHP applications in the Danish district heating systems was investigated and compared based on a proposed two-stage optimal dispatch model. In the first stage, the heat production plan of a CHP plant was derived to minimize the system heat cost in a deregulated heat market by using its flexibility; in the second stage, the CHP plant was redispatched to provide real-time balancing service with the remaining flexibility. The diversified applications include different types of CHP plant, various operation modes, and the inclusion of heat accumulator (HA) or not. A case study using information collected from Denmark was presented to validate the proposed algorithm and to quantitatively illustrate the flexibility difference of various CHP applications in real time. The results provide a practical guide to activities aiming to take advantage of the flexibility potential of CHPs for both minimizing the heat cost and balancing a local energy portfolio. [ABSTRACT FROM AUTHOR]

Highlights • A two-layer collaborative planning for inverter interfaced DGs and BES units is established for enhanced voltage regulation functions in ADN. • The potential of DG inverter over-sizing design at planning stage, the fully utilization mode of reactive power output of DG inverters at operation stage are discussed. • A time-varying ZIP load model is adopted representing the dynamic load characteristics. • The uncertainties of the DG power output are modeled based on the Taguchi robust test design approach. Abstract Active Distribution Networks (ADNs) are featured by large-scale integration of distributed generation (DG) and energy storage. This paper proposes a novel two-layer co-planning method for optimal placement of inverter-interfaced DG and battery energy storage (BES) units towards enhanced voltage regulation functions in an ADN. The outer-later model determines the planning decision of DG units and the inverter sizing, location, and capacities of BES units, respectively; the inner-layer model corresponds to the operation decision which aims to optimally schedule the BES's charging/discharging and reactive power from inverters for voltage regulation support considering the conservation voltage reduction (CVR). The Taguchi's Orthogonal Array Testing (TOAT) is used to select a small number of scenarios to represent the DG power output uncertainties. The load is represented by a time-varying ZIP load model. The proposed model is tested on a modified IEEE 33-bus radial distribution system and the results to validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Steel production is one of the most energy-intensive industries on demand side. Highly distributed energy resource-penetrated multi-energy microgrids (MEMGs) with combined heat and power (CHP) units can supply both electricity and heat while the by-product coal gases during manufacturing can be reused for onsite power supply. However, there is a lack of coordination between steel production and MEMG operation, and the steelmaking process is not fully modelled. Thus, this paper proposes a three-stage coordinated operation method for steel plant-based MEMGs, aiming to minimize the total operating cost. In this method, the steel production is scheduled weekly-ahead to meet the production demand considering carbon emission reduction. Then, the CHP commitment and day-ahead energy transaction are optimized in a day-ahead stage, while the dispatchable device output and intraday energy transaction are determined hourly-ahead based on uncertainty realizations. Accordingly, the steel production is modelled as continuous and discontinuous processes in parallel or series. To tackle the uncertainty of renewable generation, a scenario-based stochastic optimization method is utilized. Moreover, different carbon prices are applied to investigate their effects on steel production. The results show that the proposed method can decrease the operating cost by 14.33% and 1.45% compared with the other two conventional methods. [Display omitted] • Three-stage steel plant MEMG low-carbon operation coordinated with process optimization. • Generalized continuous/discontinuous process modelling for steel production. • Uncertain intervals of renewable generation tackled with SO and SAA in different stage. [ABSTRACT FROM AUTHOR]

An energy system with desalination and hydrogen production and storage is a promising option for remote areas with shorelines, e.g., Middle East, to jointly manage electricity, desalinated water and hydrogen resources. Thus, a hybrid renewable energy system considering seawater reverse osmosis desalination, proton exchange membrane electrolyzer stacks, and also proton exchange membrane fuel cell, is proposed. This work focuses on the optimal operation problem of the system. It is established in a multi-objective optimization manner, with consideration of minimizing system total cost, power transmission and renewable energy curtailment. The problem is solved with Non-dominated Sorting Genetic Algorithm-III, a meta-heuristic method dedicated to multi-objective optimization. Results show through the solving of the optimization problem the optimal energy management strategy can be obtained, and in the studied scenario, the system can avoid 38–42% of carbon dioxide emission compared to conventional electricity generation and gray hydrogen production measures. The operational benefit of fuel cell is also verified. Compared to existing works, this work maintains the flexibility and cleanness of green hydrogen production components, consider more aspect in operation and can be solved with limited computational resources and obtain a satisfying result. [ABSTRACT FROM AUTHOR]

This paper presents the planning of solar photovoltaics (PV), battery energy storage system (BESS) and gas-fired micro turbine (MT) in a coupled micro gas and electricity grid. The proposed model is formulated as a two-stage stochastic optimization problem, including the optimal investment in the first stage and the optimal operation in the second stage. To better understand the mutual interactions between electric and heat energy, the gas network models are taken into account. As a result, the fuel availability and price of the gas-fired MT can be explicitly modeled and analyzed. Moreover, to enhance the computational efficiency of the formulated mixed-integer quadratic programming problem, the point estimation method is used as the scenario reduction technique. The effectiveness of the proposed model is verified on a 14-bus coupled micro energy grid. Based on the case studies, the proposed two-stage planning model can identify a planning solution with the objective value of $99.3104, which is comprised of the daily capital recovery cost of $20.5070, the daily operating cost of $78.8034 for the coupled micro gas and electricity grid. Comparative studies demonstrate that the proposed approach can help the microgrid operator identify feasible and optimal planning solutions, and provide valuable guidance for energy infrastructure expansion from an integrated perspective. [ABSTRACT FROM AUTHOR]

The wake effect is the major obstacle to reaching the maximum power generation for wind farms, since choosing the suitable wake model that satisfies both computational cost and accuracy is a difficult task. Deep Reinforcement Learning (DRL) is a powerful data-driven method that can learn the optimal control policy without modeling the environment. However, the "trial and error" mechanism of DRL may cause high costs during the learning process. To address this issue, we propose an ensemble-based DRL wind farm control framework. Under this framework, a new algorithm called Actor Bagging Deep Deterministic Policy Gradient (AB-DDPG) is proposed, which combines the actor-network bagging method with the Deep Deterministic Policy Gradient. The gradient of the proposed method is proved to be consistent with the DDPG method. The experiment results in WFSim show that AB-DDPG can learn the optimal control policy with lower learning cost and a more robust learning process. • This paper proposes an ensemble-based DRL framework for wind farm control. • An algorithm is proposed under the proposed framework to reduce the learning cost. • The gradient used in the proposed algorithm is proved to be consistent with the DDPG. [ABSTRACT FROM AUTHOR]

• A new renewable energy trading system is proposed for residential communities. • The proposed energy trading system is highly scalable. • Homomorphic Encryption technology is introduced to protect user privacy. • Extensive numerical simulations are conducted to validate the proposed system. This paper proposes a new trading framework for enabling energy trading between end energy customers and a small-capacity Renewable Energy Plant (REP) in urban environments. To relieve the communication burden incurred by a large number of end customers who submit bids to purchase energy from the REP, the energy trading process is designed on a community basis. The end customers are grouped into multiple communities. With this approach, the proposed energy trading system coordinates the REP, the community energy management system in each community, and the local energy management system of each customer. The homomorphic encryption technology is integrated into the energy trading framework to enable the energy trading to be performed and settled without exposing individual customer's bidding energy purchase price and amount values. Extensive numerical simulations are conducted to validate the effectiveness of the proposed system. [ABSTRACT FROM AUTHOR]

The increasing penetration of wind power significantly affects the reliability of power systems due to its intrinsic intermittency. Wind generators participating in electricity markets will encounter operational risk (i.e. imbalance cost) under current trading mechanism. The imbalance cost arises from the service for mitigating supply-demand imbalance caused by inaccurate wind forecasts. In this paper, an insurance strategy is proposed to cover the possible imbalance cost that wind power producers may incur. First of all, a novel method based on Monte Carlo simulations is proposed to estimate insurance premiums. The impacts of insurance excesses on premiums are analyzed as well. Energy storage system (ESS) is then discussed as an alternative approach to balancing small wind power forecasting errors, whose loss claims would be blocked by insurance excesses. Finally, the ESS and insurance policy are combined together to mitigate the imbalance risks of trading wind power in real-time markets. With the proposed approach, the most economic power capacity of ESS can be determined under different excess scenarios. Case studies prove that the proposed ESS plus insurance strategy is a promising risk aversion approach for trading wind power in real-time electricity markets. [ABSTRACT FROM AUTHOR]

The concerns on energy security and environment protection have driven the need to produce, transform and utilize energy in a more efficient, clean and diversified way. Under this background, we design a paradigm for energy hubs that combine distributed energy supply/combined cooling heating and power (DES/CCHP), renewable energy and energy storage. These energy hubs are comprised of heating, cooling and power systems, and natural gas, power generation and photovoltaic (PV) are the primary energy sources. Also, we propose a paradigm and its operation model for regional multi-energy prosumers (RMEP) whose energy demands are served by interconnected energy hubs. The energy exchange in energy hub is based on the structure of energy buses including power bus, heating bus and cooling bus. These energy buses are interconnected as a ring heating/cooling network and a radial power grid to implement mutually complementary reserves of energy hubs. Moreover, the bi-directional energy flows between prosumer and the main grid are also analyzed. In addition, an optimal scheduling model for RMEP is proposed. The formulated objective of this model is to minimize prosumer’s cost of purchasing electricity and natural gas plus the cost of GHG emission or to maximize the revenue of selling electricity back to the grid, while considering various electricity and gas prices and heating/cooling demands during different time periods. The decision variables for prosumer include the amount of purchased gas, the amounts of purchased and sold electricity at energy hubs. Case studies are undertaken on the 15-node multi-energy prosumer system, where the system comprises three energy hubs. Prosumer’s operational strategies under system normal and contingency conditions on typical summer and winter load days can be obtained. Comparative analyses between mutually independent energy hubs are also conducted. According to the simulation results, prosumer can play an important role in responding to the time-of-use electricity and gas tariffs, shaving the regional peak loads as a whole. Besides, the interconnected energy hubs can enhance the overall system operational flexibility and reliability. [ABSTRACT FROM AUTHOR]

• A risk-averse deep learning method for real-time emergency load shedding. • Deep learning algorithm to enhance prediction accuracy on load shedding strategy. • A shedding amount inference model to speculate the stability after load shedding. • A risk-averse loss function to minimize the risk of load under-cutting events. • Experiments on multiple power systems to verify the practical values of the method. Emergency load shedding is an effective and frequently used emergency control action for power system transient stability. Solving the full optimization models for load shedding is computational burdensome and thus slow react to the intense system variations from the increasing renewable energy sources and the more active demand-side behavior. Other sensitivity-based methods impair the control accuracy and may not guarantee global optimality. Artificial intelligence methods, as the data-driven approaches, have recently been well-recognized for its real-time decision-making capability to tackle the system variations. The existing artificial intelligence methods for emergency load shedding are based on shallow learning algorithms and can lead to both load under-cutting and over-cutting events. However, when the loads are under-cut, the power system will be exposed to a high risk of post-control instability that can propagate into cascading events, which incurs significantly higher cost than an over-cutting event. Being aware of such unbalanced control costs, this paper proposes a risk-averse deep learning method for real-time emergency load shedding, which trains deep neural network towards the reluctance to load under-cutting events, so as to avoid the huge control cost incurred by control failure. The case studies on two renewable power systems demonstrate that, compared to the state-of-the-art methods, the proposed risk-averse method can significantly improve the control success rate with negligible increase in prediction error, ending up with lower overall control cost. The results verify the enhanced control performance and the practical values of the proposed method. [ABSTRACT FROM AUTHOR]

• New security-constrained OPF model for a microgrid considering tie-line switching. • New Benders decomposition based solution algorithm to solve the OPF problem. • Stochastic optimization method with probabilistic modeling to address uncertainty. With the rapid development of microgrid, its tie-line switching from grid-connected to islanded mode is a topic worth discussing for considering both main grid resilience and microgrid security. In this paper, a stochastic security-constrained optimal power flow (OPF) method is proposed to deal with these conditions under high uncertainties. Firstly, a linear load flow model and a backward forward sweep algorithm are applied to present microgrid power flow with reduced computing burdens. Secondly, with consideration of tie-line switching from grid-connected to islanded operation mode, a security-constrained OPF problem for a microgrid is proposed to minimize operating cost and by optimizing microturbine setpoints and load shedding coefficient. To promise stable islanded operation after disconnection from the main grid, a Benders decomposition method is developed to decouple the OPF problem into a grid-connected master problem and an islanded sub-problem and then solve them iteratively with Benders cuts to guarantee microgrid security after tie-line switching. Last, a stochastic optimization method with probabilistic modelling is adopted to address the uncertainty issue caused by renewable energy sources and loads. The proposed stochastic security-constrained OPF method has been verified with high computing efficiency and robust security via comprehensive numerical simulations. [ABSTRACT FROM AUTHOR]

• Review the state-of-the-art research works of integrated energy systems of data centers and smart grids. • Propose future integration scenarios for data centers and smart grids. • Analyze the challenges of implementing integrated energy systems of data centers and smart grids. Cloud computing platforms are critical cyber infrastructures in modern society. As the backbone of cloud systems, data centers act as large energy consumers in today's power grids. The integration of on-site renewable energy sources and energy storage systems further transforms data centers to be energy prosumers (producers-and-consumers). As a result, optimizing data centers' energy production and consumption, and exploiting their potential of actively engaging in the external grid's planning, operation, and control has been drawing increasing attention in the last few years. This paper conducts a comprehensive review of the state-of-the-art research efforts on integrated energy systems of data centers and smart grids. A taxonomy of such integration scenarios is provided. Consequently, this paper identifies several future application scenarios of integrating data centers and smart grids, which serves as a roadmap towards future research. This article is expected to provide a useful reference for researchers and engineers in the areas of energy systems and cloud computing. [ABSTRACT FROM AUTHOR]

Based on extensive research work on the application of energy trading models, energy sharing strategies are applied to smart homes. This paper addresses an energy sharing platform as an energy storage solution for renewable distributed generation (RDG) integration in microgrids. An energy-sharing service platform for renewable distributed generation smart home applications is proposed. This platform aims to suit various kinds of energy users, energy generation and energy storage devices. It is based on a decentralized approach, which means a central operator is not required. In the proposed energy management model, the up-reserve (UR) is defined as the maximum state of charge (SOC) value in every participant. The conditional Value-at-Risk (CVaR) method is used to undertake risk analysis and UR calculation. In a shared strategy, the Conditional Value at Risk (CVaR) method is used for risk analysis and energy operations. Moreover, this energy sharing platform refers to a trading mechanism called the call-auction method, which is similar to the opening auction trading in the stock market. The objective of the trading principle is to achieve the maximum trading volume; thus the platform facilitates an increase in the utilization of RDG. This paper details the rigorous proof of the energy sharing strategy used by the algorithm in the energy trading model. A rigorous proof of the algorithm is also given. Furthermore, the proposed energy sharing strategy has been compared with existing offerings of the frequency control ancillary services (FCAS). The historical data in Australia's electricity market is used to verify the proposed approach. Moreover, the proposed energy sharing platform compares with the FCAS provision. Simulation results show that the surplus energy can be shared among participants who hold different quantities of demand and generation/storage. Therefore, the proposed approach is a cost-effective energy storage solution, especially when energy storage capital cost is high. • An energy sharing platform is presented for renewable distributed generation. • An independent energy management strategy can be customized for each participant. • The call auction model of stock exchange is cited in the energy sharing platform. • The maximum transaction volume is applied in the call auction model. • The award of energy sharing can be self-ruled by participants. [ABSTRACT FROM AUTHOR]

• A framework solution for multi-timescale coordinated operation of CHP and wind power. • Day-ahead and real-time uncertainties are handled by stochastic optimization. • The solution is applied to a real-life case in Denmark. • Comparative analysis between coordinated operation and independent operation. • Comparative analysis between stochastic and deterministic methods. This paper proposes a multi-timescale coordinated operation approach which coordinates the combined heat and power (CHP) plant and wind farm operation in a deregulated day-ahead heat market and a real-time balancing electricity market. This approach aims to optimize profits of the CHP-wind farm portfolio by considering energy sale in the day-ahead heat market and penalty in the real-time two-price balancing market. Multiple uncertainties of heat load, wind power generation, day-ahead electricity price, up- and down-regulation prices are taken into account for the optimal operation. A stochastic optimization method is applied to solve the proposed coordinated operation problem. The proposed multi-timescale coordinated operation approach is simulated on a real-life system in Copenhagen Denmark where an energy company owns both a CHP plant and a wind farm. The simulation results verify that the proposed method can achieve a profitable operation in the markets and a highly robust operation against the multiple uncertainties. [ABSTRACT FROM AUTHOR]

• A private reliability service is proposed and can be traded in the electricity market. • A novel group double side auction method for market clearing is designed and verified. • Dynamic elasticities considering real-time preference are applied in this model. • The model provides the advised price interval for customers. • Real historical data of customers in Australia are used to verify the proposed model. Due to the rapid development of distributed renewable generation, emerging prosumers are encouraged to participate in the energy market. Meanwhile, blackout events, planned and unplanned outages due to weather, component failures and other causes, can also impact users' experience. To maintain reliability and improve users' experience under outage conditions, it is possible to transform the service reliability from a public good (compulsory and uniform) to a private good (self-selection). In this paper, a new restore mechanism based on private reliability service is proposed, the reliability service is no longer uniform. Customers with a higher reliability requirement are willing to pay to maintain their desired consumption in outage conditions. The customers are classified by dynamic elasticity considering historical data and real-time customer preferences. Then the advised unit price, trading amount and reliability level are provided to customer. Once they submit this information to market operator, the price and transaction amount is matched in each group, and floated average clearing price is applied to obtain the bidding results. The trading mechanism is proved to satisfy incentive compatibility, and the transaction process for a specific area in Australia is analyzed in a case study to verify the proposed model. [ABSTRACT FROM AUTHOR]

• Proposing an energy-based transient stability constrained unit commitment model. • Considering sensitivity of generation level and inertia on stability margin. • Proposing cutset energy function methodology without loss of network structure. • Developing a state transition MILP formulation for the unit commitment model. Rapid growth of load demand and concurrently system inertia deterioration put power systems at risk of transient instability. Therefore, operation of power systems as bulk complex systems must be secured against transient instability not only in hourly optimal power flow studies, but also in daily unit scheduling. To address this challenge, in this paper, secure and economic operation of power system is ensured through a Transient Stability-Constrained Unit Commitment (TSCUC) model employing Benders Decomposition (BD) technique. The proposed TSCUC model consists of one master problem determining committed units and two distinct sub-problems verifying the steady state impacts of single outages, and transient stability criteria, respectively. This paper proposes a state transition formulation for master problem as a mixed integer linear programming optimization problem preserving both compactness and tightness of the problem. A structure-preserving transient stability assessment approach called cutset energy function method is developed to assess the transient stability of the system for each configuration of the committed units, under a set of probable contingencies. Several case studies on a dynamic test system are demonstrated to validate the efficacy of the proposed TSCUC algorithm. Finally, the proposed method performance is compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

• Integrated planning of Internet data centers and battery energy storage systems. • Coupled cyber-physical modelling of Internet data centers and a smart grid. • Multi-objective modelling of the planning task. Modern power grids have been becoming complex cyber-physical systems integrated with distributed energy sources and information and communication facilities. With prevalence of cloud computing, geo-distributed, networked data centers have become an integrated part of modern grids. The coupling impact between data centers and smart grids thus becomes an important consideration. This paper proposes an integrated planning scheme that optimally determines the locations and capacities of interconnected Internet data centers and battery energy storage systems in a smart grid. The model is formulated as a multi-objective optimization problem, in which both computational performance metrics of Internet data centers and operational criteria of the grid are coordinately considered as three inter-related but conflict objectives; the coupling impact between the cyber and energy resources are modelled. An advanced evolutionary algorithm – Multi-Objective Natural Aggregation Algorithm is used to solve the model. Extensive case studies are conducted to demonstrate the reasonability and effectiveness of the proposed integrated planning method. [ABSTRACT FROM AUTHOR]

• A sharing platform framework with energy and capacity sharing of hybrid energy storage system (HESS) is proposed. • Detailed technic-economic analysis has been conducted. • The characteristics of electrical and thermal demands are captured, in order to assist the energy management of HESS. • Flexibility from multi-energy systems studied is including electricity, heat and gas sector and energy storage. • Energy sharing and flexibility of end-users are also considered. There is already a large amount of energy storage system (ESS) and demand response potential in the power, heat and gas system, which can be used to promote a cost-effective transition to low-carbon and renewable energy. This paper proposes an energy sharing platform to effectively integrate power, thermal and gas systems of different sizes to balance the fluctuation of renewable energy and improve the system reliability. In a potential application in the hospitality industry, hotels can jointly share and rent ESS. This sharing platform uses a hybrid energy storage system (HESS), comprising BESS and thermal energy storage system (TESS). Unlike BESS, TESS is cost-effective and can be provided by hot water tanks as short-term energy storage. The capacity and energy sharing method of the hybrid BESS and TESS system is provided, including the detailed rental model of HESS in the operation economy. Moreover, the coupled energy supply chain is also considered, the combined utilization of thermal, gas and electricity sharing is achieved. The proposed model is evaluated on the IEEE 18-bus system with the real historical data in Australian energy market, and the cost-benefit analysis is also provided. [ABSTRACT FROM AUTHOR]