Energy storage systems (ESS) are pivotal component in the energy market, serving as both energy suppliers and consumers. ESS operators can reap benefits from energy arbitrage by optimizing operations of storage equipment. To further enhance ESS flexibility within the energy market and improve renewable energy utilization, a heterogeneous photovoltaic-ESS (PV-ESS) is proposed, which leverages the unique characteristics of battery energy storage (BES) and hydrogen energy storage (HES). For scheduling tasks of the heterogeneous PV-ESS, a practical cost function plays a crucial role in guiding operator’s strategies to maximize benefits. We develop a comprehensive cost function that takes into account degradation, capital, and operation/maintenance costs to reflect real-world scenarios. Moreover, while numerous methods excel in optimizing ESS energy arbitrage, they often rely on black-box models with opaque decision-making processes, limiting practical applicability. To overcome this limitation and enable explainable scheduling strategies, a prototype-based policy network with inherent interpretability is introduced. This network employs human-designed prototypes to guide decision-making by comparing similarities between prototypical situations and encountered situations, which allows for naturally explained scheduling strategies. Comparative results across four distinct cases demonstrate the effectiveness and practicality of our proposed pre-hoc interpretable optimization method when contrasted with black-box models.

Considering the popularity of electric vehicles and the flexibility of household appliances, it is feasible to dispatch energy in home energy systems under dynamic electricity prices to optimize electricity cost and comfort residents. In this paper, a novel home energy management (HEM) approach is proposed based on a data-driven deep reinforcement learning method. First, to reveal the multiple uncertain factors affecting the charging behavior of electric vehicles (EVs), an improved mathematical model integrating driver’s experience, unexpected events, and traffic conditions is introduced to describe the dynamic energy demand of EVs in home energy systems. Second, a decoupled advantage actor-critic (DA2C) algorithm is presented to enhance the energy optimization performance by alleviating the overfitting problem caused by the shared policy and value networks. Furthermore, separate networks for the policy and value functions ensure the generalization of the proposed method in unseen scenarios. Finally, comprehensive experiments are carried out to compare the proposed approach with existing methods, and the results show that the proposed method can optimize electricity cost and consider the residential comfort level in different scenarios.

In Home Energy Management System (HEMS), the scheduling of energy storage equipment and shiftable loads has been widely studied to reduce home energy costs. However, existing data-driven methods can hardly ensure the transferability amongst different tasks, such as customers with diverse preferences, appliances, and fluctuations of renewable energy in different seasons. This paper designs a transferable scheduling strategy for HEMS with different tasks utilizing a Meta-Reinforcement Learning (Meta-RL) framework, which can alleviate data dependence and massive training time for other data-driven methods. Specifically, a more practical and complete demand response scenario of HEMS is considered in the proposed Meta-RL framework, where customers with distinct electricity preferences, as well as fluctuating renewable energy in different seasons are taken into consideration. An inner level and an outer level are integrated in the proposed Meta-RL-based transferable scheduling strategy, where the inner and the outer level ensure the learning speed and appropriate initial model parameters, respectively. Moreover, Long Short-Term Memory (LSTM) is presented to extract the features from historical actions and rewards, which can overcome the challenges brought by the uncertainties of renewable energy and the customers’ loads, and enhance the robustness of scheduling strategies. A set of experiments conducted on practical data of Australia’s electricity network verify the performance of the transferable scheduling strategy.

Wind energy utilization is essential for realizing global energy transformation. To capture steady and powerful wind resources, wind turbines tend to be more remotely located with increasingly larger scales. This requires more sophisticated wind farm (WF) planning techniques to increase power production, reduce investment cost, and enhance power supply reliability, or pursue a balance among them. This article establishes a research framework for large-scale WF planning, with a technical review of the optimization methodologies involved, aiming to provide an updated, broader, and forward-looking vision for WF planners. Beyond the state-of-the-art summary of WF planning in the realm of wind turbines’ micro-siting and collector system design techniques, the related considerations on various practical factors are also well assessed including reliability, equipment rating, environment, and landscape. Further, viewing WF as an integrated system with scale expansion, the new research areas including WF joint-planning and redevelopment are explored and discussed to provide strong technical references for the foreseeable future.

Setting retail electricity prices is one of the significant strategies for energy management of multi-microgrid (MMG) systems integrated with renewable energy. Nevertheless, the need of privacy preservation, the uncertainties of renewable energy and loads, as well as the time-varying scenarios, bring challenges for pricing problems. In this paper, a two-level pricing framework is proposed based on interval predictions and model-free reinforcement learning to address these challenges. In particular, at the higher level, the distribution system operator (DSO) is viewed as an agent, which sets retail electricity prices without detailed user information for privacy protection to maximize the total revenue from selling energy with reinforcement learning. For time-varying scenarios with intermittent photovoltaic power generation and diverse loads, a differentiable trust region layer is considered in reinforcement learning to improve the robustness of the policy updating process. While at the lower level, operators in microgrids solve three-phase unbalanced optimal power flow (OPF) problems to minimize generation cost and network power loss. Additionally, to deal with the challenges from the uncertainties of renewable power generation and user loads, interval predictions are chosen to quantify prediction errors and improve the flexibility of pricing policies. Finally, a set of experiments are conducted to validate the effectiveness of the proposed method for pricing problems in MMG systems.

Modern electrical power systems have an increasing penetration of distributed generation (DG). This increase yields the requirement for integration of renewable energy and distributed energy generation into the educational curriculum of power engineering. However, the integration of associated knowledge in the curriculum framework is challenging because power system industry is experiencing a fast and continuing transition and associated teaching and learning materials are becoming quickly outdated in higher education institutions. This paper demonstrates the use of the backward design model in the power engineering discipline, where a Year 3 course is redesigned. The course is entirely implemented in an online mode, and the feedback and perception from the students are presented and analyzed. The results show the necessity of the curriculum framework development for power engineering education in a qualitative manner and provide guidance for course instructors to integrate distributed energy generation in undergraduate coursework programs.

The economic dispatch problem (EDP) plays a fundamental and significant role in smart grids. Its purpose is to decide the output power of every generator in smart grids for achieving the minimal generation cost. With advantages in flexibility, robustness, and scalability, it is desirable to apply distributed optimization methods to solve EDPs. In most existing distributed optimization approaches, all generators explicitly exchange their states with neighbors to obtain the optimal solution, which may result in disclosing the privacy information of generators. This problem becomes worse if there are some adversaries aimed at inferring privacy information from the communication network for nefarious purposes. For privacy preservation, a privacy preserving distributed optimization algorithm over time-varying directed communication networks is proposed in this article by adding conditional noises to the exchanged states. It is proved that this proposed algorithm is able to solve the EDP. Moreover, the convergence rate and privacy analysis of the proposed algorithm are also shown in this article. An example is provided to confirm the effectiveness of this proposed algorithm.

Constructing large-scale offshore wind farms (OWFs) has become the main direction of utilizing wind power to help realize the energy transformation. Traditionally, the planning of the OWF collector system would rely on either heuristic or deterministic optimization algorithms, which, respectively, suffer from unstable outputs and a lack of freedom in searching for a globally optimal solution. This article innovatively designs a hybrid optimization approach combining algorithms in these two categories to achieve a balance between improved economic efficiency and stable outputs. The whole design consists of two layers of hybrid optimizations. The outer layer is to partition wind turbines (WTs) into groups, where each group is allocated with an offshore substation with the optimized location for power collection and transmission. This partitioning and locating optimization is solved through a combination of the deterministic fuzzy C-means clustering method and the genetic algorithm (GA). The inner layer is to arrange optimal connections using proper cable ratings among WTs within each group, and GA is properly integrated into the deterministic two-phase Clark and Wright's saving algorithm to solve the problem. The collector system planning, in this article, concerns both the investment cost and the long-term power-loss cost. The former consists of the networks of internal medium voltage and the external high voltage, which collect the power from WTs and transmit it to the onshore grid. The proposed design is tested on a benchmark OWF collector system, and the test result verifies its achievements in higher economic efficiency with stable outputs.

This article proposes an economic model predictive algorithm for optimal load frequency control, in which both the frequency regulation and economic load dispatch (ELD) are considered, in interconnected power systems. Two-layer hierarchical control can be achieved through one level by EMPC. An economic stage cost function, including ELD and frequency regulation, which can be written in general convex form, is optimized by the controller. The distributed way is utilized to realize the control of large-scale power systems. Each subsystem-based controller works cooperatively with neighboring subsystems to achieve system-wide control performance. Asymptotic stability of the system is guaranteed by the proper terminal cost function. The efficiency and advantages of the proposed method are manifested by the simulation.

The economic dispatch problem (EDP) is one of the fundamental and important problems in power systems. The objective of EDP is to determine the output generation of generators to minimize the total generation cost under various constraints. In this article, a finite-time consensus-based distributed optimization algorithm is proposed to solve EDP. It is only required that each device in the communication network has access to its own local generation cost function, designed virtual local demand and its neighbors' local optimization variables. The proposed finite-time algorithm can solve EDP, if the gain parameters in the algorithm satisfy some conditions under undirected and connected time-varying graphs. Moreover, the bounded or linear increasing assumption on the gradient and subgradient of objecive functions is relaxed in this algorithm. Examples under several cases are provided to verify the effectiveness of the proposed distributed optimization algorithm.

Data-driven condition monitoring is an essential function for power plant because of its potential to enhance asset longevity and reduce the operation and maintenance costs. This article explains the complicated relationship in multiplex power plant data as a mixture of temporal dependency and cross-variable association and proposes a composite anomaly detection system that incorporates the two data relationships on a probabilistic basis for more reliable power plant condition monitoring. It is able to dynamically capture the most significant relationship to develop more reliable normal condition interval, based on which the potential faults can be timely detected and the abnormal variable can be accurately identified. The proposed system was tested on a realistic thermal power plant. The testing results demonstrate its reliable condition monitoring and accurate anomaly detection performance, which necessitates the composite modeling of temporal dependency and cross-variable association in data-driven power plant condition monitoring.

The increasing popularity of electric vehicles (EVs) has made electric transportation a popular research topic. The demand for EV charging resources has significantly reshaped the net demand profile of power distribution systems. This paper proposes an online optimal charging strategy for multiple EV charging stations in distribution systems with power flow and bus voltage constraints satisfied. First, we formulate the online optimal charging problem as an optimal power flow problem that minimizes the total system energy cost based on short-term predictive models and operates in a time-receding manner with the latest system information. Then, the problem is convexified by a modified convex relaxation technique based on the bus injection model, so that the globally optimal solution can be obtained with high efficiency. Moreover, a distributed model predictive control based scheme is designed to solve the optimization problem per concerns regarding data privacy, individual economic interests, and EV uncertainties. The obtained optimal schedules are dispatched to the EVs parked at each charging station according to a fuzzy rule, which guarantees full charging at the departure time for each vehicle. The effectiveness of the proposed method is demonstrated via simulations on a modified IEEE 15-bus distribution system with charging stations located in both residential and commercial areas.

Owning to electricity market deregulation, residential customers now enjoy the freedom to choose their preferred electricity retailers. This paper investigates the application of recommender system, a fast-developing technique in machine learning, into the task of recommending electricity plans for the individual residential customer. Based on a collaborative filtering strategy, an electricity plan recommender system (EPRS) is developed. By providing easily obtainable data of some household appliances, residential customers of the EPRS are recommended with predicted ratings of different plans, which can provide effective guidance to customers in the selection of suitable plans and proper tariffs. Different numerical tests are carried out to evaluate the performance of the EPRS. The EPRS outperforms other strategies in the accuracy of recommendation result and is verified to be a promising solution to electricity plan recommendation task.

The deregulation of the electricity market enables residential customers to select suitable electricity retailing plans. This paper proposes a Bayesian hybrid collaborative filtering-based electricity plan recommender system (BHCF-EPRS), which is constructed in a two-stage model integrated with model-based and memory-based collaborative filtering methods. Bayesian inference is developed for missing feature estimation and user classification. Free from the requirements on total electricity use data and historical plan transaction data, the BHCF-EPRS can recommend suitable retailers and plans based on some easily obtainable features quantifying home appliance usage patterns. The BHCF-EPRS is verified to be a reliable recommender system with low error in full-ranking recommendation and high precision in top-N recommendation, which can improve the competitive operation of the electricity market.

The growth in residential air-conditioning is a primary contributor to electric utility critical peak load causing millions of dollars spent on extra network infrastructure to cater for these peak times. This paper aims to provide an attempt to coordinate multiple groups of aggregated air-conditioners for distribution network loading management. Through limited communication to exchange information among neighboring aggregators, the proposed dispatch strategy shares the required active power curtailment among aggregators, maintaining room temperatures to keep occupants comfort in the meanwhile. Three case studies and sensitivity analysis are conducted to show the performance of the proposed scheme. The results show that it can provide technical and economic benefits to both participating residents and network operators.

The increasing prevalence of electric vehicles (EVs) calls for the effective planning of the charging infrastructure. In this study, a multi-objective, multistage collaborative planning model is proposed for the coupled EV charging station infrastructure and power distribution network. The planning model aims to minimize the investment and operation costs of the distribution system while maximize the annually captured traffic flow. The uncertainties of EV charging loads are modeled for three different types of charging stations. The FISK's stochastic traffic assignment model is utilized to model realistic traffic flows. And a new class of volume-delay functions, conical congestion functions, is employed to overcome the shortcomings of the conventional Bureau of Public Roads function. The multi-objective evolutionary algorithm based on decomposition (MOEA/D) algorithm is applied to find the nondominated solutions of the proposed collaborative planning model. Finally, simulations based on a 54-node distribution system are conducted to validate the effectiveness of the proposed method.

The growth in installed solar photovoltaic (PV) capacity and the ever-increasing power demand due to the use of energy-hungry appliances have caused voltage issues. In this paper, a hierarchical dispatch strategy is proposed for coordinating multiple groups of virtual energy storage systems (VESSs), i.e., residential houses with air conditioners, to regulate voltage in low-voltage (LV) grids with high solar PV penetration. Specifically, the two levels of the proposed model are: 1) in the lower level, VESSs within each intelligent residential district are controlled locally by individual aggregator; 2) in the upper level, multiple aggregators are coordinated to achieve voltage regulation through a consensus control strategy. By exchanging information through sparse communication links, each aggregator shares the required active power adjustment among all participating groups, without compromising users' thermal comfort. Simulation result demonstrates that the proposed control scheme can effectively regulate voltage in LV grids with greater robustness and scalability.

This paper proposes an operational planning framework for large-scale thermostatically controlled load (TCL) dispatch. The proposed framework consists of a day-ahead scheduling stage and a real-time operation stage. A thermal comfort model is employed to estimate the occupants' thermal comfort degree. A self-adaptive TCL grouping method is proposed to group the TCLs based on the similarity of the TCL model parameters. Then, a hierarchical day-ahead scheduling model is proposed to make the optimal dispatch plan for the TCL aggregators based on the day-ahead forecasted information. In the real-time operation stage, a predictive control model is proposed for the TCL aggregators to make the real-time TCL dispatch decision based on the updated real-time information. The simulation results prove the efficiency of the proposed framework.

Renewable share in the global total energy mix is predicted to grow, and this leads to an increase in the required capacity for frequency regulation. While an electric vehicle (EV) is gaining more popularity, a collection of retired EV battery packs provides an economic option for meeting the additional frequency regulation needs. In this paper, a battery market operation model is proposed to maximize financial return, and a battery operation cost estimator is built to evaluate the potential impacts of market operations on the battery lifespan. Specifically, the model is designed for retired EV lithium batteries under the Australian national electricity market framework. It predicts the automatic-generation-control energy due to the frequency regulation service offers. Battery cycle life cost and battery capacity degradation are considered in the model. It can be used to determine multimarket offers based on the expected profit. Nonetheless, the model can be generalized for other electricity market frameworks and battery types.

Dynamic security assessment (DSA) is an important issue in modern power system security analysis. This paper proposes a novel pattern discovery (PD)-based fuzzy classification scheme for the DSA. First, the PD algorithm is improved by integrating the proposed centroid deviation analysis technique and the prior knowledge of the training data set. This improvement can enhance the performance when it is applied to extract the patterns of data from a training data set. Secondly, based on the results of the improved PD algorithm, a fuzzy logic-based classification method is developed to predict the security index of a given power system operating point. In addition, the proposed scheme is tested on the IEEE 50-machine system and is compared with other state-of-the-art classification techniques. The comparison demonstrates that the proposed model is more effective in the DSA of a power system.

The increasing penetration of wind power can alter the dynamic security characteristic of a power system. To accommodate rapid and volatile wind power variations, dynamic security assessment (DSA) against foreseeable disturbances is required to be carried out online and provide security monitoring results within sufficiently small time frame. Based on soft computing (SC) technologies, this paper develops an intelligent framework for real-time DSA of power systems with large penetration of wind power. It consists of a DSA engine whose role is to perform real-time DSA of the power system, a wind power and load demand (W&LF) forecasting engine for offline and online predicting wind power generation and electricity load demand, a database generation (DBG) engine for generating instances to train the DSA engine, and a model updating (MU) engine for online updating the DSA engine. Case studies are conducted on two benchmark systems where high DSA efficiency and accuracy are obtained. This framework can be an ideal candidate for advanced security monitoring in the future SmartGrid control centres.

In this paper, a computational framework for integrating wind power uncertainty and carbon tax in economic dispatch (ED) model is developed. The probability of stochastic wind power based on nonlinear wind power curve and Weibull distribution is included in the model. In order to solve the revised dispatch strategy, quantum-inspired particle swarm optimization (QPSO) is also adopted, which shows stronger search ability and quicker convergence speed. The dispatch model is tested on a modified IEEE benchmark system involving six thermal units and two wind farms using the real wind speed data obtained from two meteorological stations in Australia.

Power system simulator for engineering (PSS_E) has gained great success in power energy industry for its powerful simulation and analysis functions. Along with market deregulation, power system planning and stability analysis warrants more effective and fast techniques due to the ever expanding large-scale interconnection of power networks. Running large-scale system multiple case studies on PSS_E will cost intensive time and efforts. In this paper, we accelerate PSS_E dynamic simulations with EnFuzion based distributed computing technique. This approach is proved to be effective by testing with 39-bus New England power system ¿n-1¿ and ¿n-1-1¿ contingency analysis. The results show that the simulation process can be speeded dramatically and the total elapsed time can be reduced proportionally with the increase of computer nodes.

A new hybrid training method for Radial Basis Function (RBF) neural network is presented in this paper. The proposed methodology produces RBF neural network models based on specially designed Fuzzy C-means (FCM) and Fuzzy Immune Algorithm (FIA), which are used to auto-configure the structure of networks and obtain the model parameters. With the proposed method, the number of hidden layer neurons and cluster centers are automatically determined according to the given data; both the output weight values and cluster radii are calculated by fuzzy immune algorithm. Meanwhile, the wavelet de-noising technique is introduced to ensure the neural network performance. This learning approach is proved to be effective by applying the optimized RBF neural network in predicting of Mackey-Glass chaos time series and forecasting of Queensland electricity reference price from Australian National Electricity Market.