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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]

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]

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]

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]

• 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]

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]

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 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]

• 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]

• 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]

• 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]

• The planning model reflects the interactions among multiple factors in reality. • Robust chance constraints are formulated to address the uncertain charging demands. • Moment-based information is utilized to construct ambiguity set for uncertainties. • The chance constraints are reformulated into mixed integer linear constraints. • The modeling results are validated by experiments through real world applications. In this paper, a bi-level programming model is established to address the planning issues of fast-charging stations in electrified transportation networks with the consideration of uncertain charging demands. The capacitated flow refueling location model is considered in the upper level to minimize the planning cost of fast-charging stations while the traffic assignment model is utilized in the lower level to determine the spatial and temporal distribution of plug-in electric vehicle flows over entire transportation networks. Such bi-level model unveils the inherent relationship among charging demands, electrical demands and the spatial and temporal distribution of plug-in electric vehicle flows. Robust chance constraints are formulated to characterize the service abilities of fast-charging stations under distribution-free uncertain charging demands, where the ambiguity set is constructed to estimate the potential values of the uncertainties based on their moment-based information, such that the robust chance constraints can exactly be reduced to mixed integer linear constraints. By introducing new variables, the bi-level model is then reformulated into a single-level mixed integer second-order cone programming model so as to be solved via off-the-shelf solvers, which guarantee the optimality of the solution. A case study is conducted to illustrate the effectiveness of the proposed planning model, which reveals three critical factors that significantly impact the planning outcomes. [ABSTRACT FROM AUTHOR]

• Review on combined heat and power plant (CHP) operation is presented. • Technical flexibility and characteristics of CHP technologies are summarized. • Applications of CHP flexibility in integrated energy system (IES) are discussed. • Cost- and price-based operation strategies of CHP in recent research are reviewed. • Future technical and operational flexibility of CHP and IES is summarized. With an increasing penetration of renewable energy, combined heat and power (CHP) plants play an important role in connecting different energy sectors of an integrated energy system (IES). Technical flexibility is an essential ability for CHPs to provide secure and economic operation of the IES, as well as to implement a profitable arbitrage in different markets. Operation strategies (OSs) are adopted to utilize the existing technical flexibility in an optimal way. The delivered flexibility is evaluated in terms of the system performance and operation profits. In this paper, a literature review on technical flexibility and characteristics of different CHP technologies is summarized. Moreover, the applications of CHP system flexibility in the IES are discussed from the perspectives of plant owners, system operators and demand side. Literature classified by cost-based and price-based OSs is studied for system operators and generation plant owners respectively. Characteristics of the OSs in terms of the CHP modeling, optimization objectives, constraints and algorithms, etc. are discussed and compared. The study is beneficial to achieve more economic operation of the IES and more profitable operation of the CHP system by system operators and plant owners. Future research work regarding this topic is investigated in the last part. [ABSTRACT FROM AUTHOR]