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Abstract Smart grids represent a paradigm shift in the electricity industry, moving from traditional one‐way systems to more dynamic, interconnected networks. These grids are characterised by their intelligent automation, robust structure, and enhanced interaction with customers, backed by comprehensive monitoring and data analytics. The key of this transformation is the integration of data‐driven methods into smart grids. Compared to previous big data solutions, large language models (LLMs), with their advanced generalisation abilities and multi‐modal competencies, are crucial in effectively managing and integrating diverse data sources. They address challenges such as data inconsistency, inadequate quality, and heterogeneity, thereby enhancing the operational efficiency and reliability of smart grids. Furthermore, at the system level, LLMs improve human–system interactions, making smart grids more user‐friendly and intuitive. Last but not the least, the structure of LLMs performs inherent advantages in bolstering system security and privacy, alongside in resolving issues related to system compatibility and integration. The paper reviews the data‐empowered smart grids and for the first time finds and proposes opportunities and future directions for adopting LLMs to accelerate the mass‐scale application of Smart Grids.

Abstract Compared to load forecasting at the national level, two challenges arise in providing accurate forecasting for LV and MV networks: (1) customers within LV and MV networks are much less, implying greater volatility within those load profiles; (2) not all customers have smart metres. Particularly, the two challenges would exacerbate forecasting performance under unexpected events, such as extreme weather and COVID‐19 outbreaks. To secure accurate short‐term load forecasting for LV and MV networks, this paper customised a Spatio‐Temporal Edge‐Cloud‐coordinated (STEC) approach on a loop training structure—LV networks to MV networks to LV networks. For each LV network, this approach utilises XGboost to learn the relationship between weather data, substation‐side loads, and a few accessible customer‐side load data to deliver rough forecasting. Then, it adopts the rough forecasting results and accessible data for all LV networks within an MV network to train the convolutional neural networks and gated recurrent unit (CNN‐GRU) network. This step provides load forecasting for MV networks and simultaneously refines load forecasting for LV networks by generating the interacting relationship between LV substations of different locations. Case studies reveal that the STEC approach successfully extrapolates the demand‐varying information from long‐term datasets and improves short‐term forecasting performance under both normal scenarios and newly occurring unexpected scenarios for LV and MV networks. The loop training structure halves the forecasting error, compared to classic methodology by utilising the local data only for MV networks.

This review studies the cascading impacts of extreme weather events on the Water-Energy-Carbon (WEC) Nexus, with a focus on their combined and sequential effects. It synthesizes research on how droughts, floods, heatwaves, hurricanes, and wildfires each initiate a chain reaction within the interconnected domains of water, energy, and carbon. Key insights include the analysis of drought impacts, like in California, where hydroelectric power's share dropped from 18% to 7%, leading to a 34%increase in emissions from natural gas plants. In Europe, flooding led to operational challenges for power plants, with a projected loss of 0.6–4.6 TWh in energy generation by 2030 due to water temperature rises. The 2023 European heatwave saw Spain's energy demand spike by 20%, driven by increased use of air conditioning, and a corresponding 15–20% rise in carbon emissions in affected countries due to greater reliance on fossil fuels. The review emphasizes the need for integrated resilience strategies, leveraging the provided quantitative data to argue for policies that address these interdependent challenges. It urges for a nuanced understanding of the WEC Nexus's dynamics to inform more effective responses to the rising tide of climate change-induced extreme weather events. Furthermore, this review expands its examination to include cases from developing countries, showcasing how their unique challenges and responses within the WEC Nexus contribute to a more comprehensive understanding of global resilience strategies against extreme weather. This review brings to the forefront the ripple effects of alterations in energy production on water resources and carbon dynamics, underscoring the critical need for a nuanced understanding and integrated approaches in managing the WEC Nexus in the face of extreme weather events.

Edge data centers (EDCs) have been widely developed recently to supply delay-sensitive computing services, which impose prohibitively increasing electricity costs for EDC operators. This paper presents a new spatiotemporal reallocation (STR) method for energy management in EDCs. This method uses spare resources, including servers and energy storage systems (ESSs) within EDCs to reduce energy costs based on both spatial and temporal features of spare resources. This solution: 1) reallocates flexible workload between EDCs within one cluster; and 2) coordinates the electricity load of data processing, ESSs and distributed energy resources (DERs) within one EDC cluster to gain benefits from flexible electricity tariffs. In addition, this paper for the first time develops a Bit-Watt transformation to simplify the STR method and represent the relationship between data workload and electricity consumption of EDCs. Case studies justifying the developed STR method delivers satisfying cost reductions with robustness. The STR method fully utilized both spatial and temporal features of spare resources in EDCs to gain benefits from 1) varying electricity tariffs, and 2) maximumly consuming DER generation.

Abstract The sustainable development of urban agglomerations plays a pivotal role in national and global efforts to reduce emissions. By focusing on the efficient exchange and optimization of energy consumption across various sectors, the sustainable development of energy systems within urban agglomerations can be achieved. However, the overall impact of the cross-sector energy optimization and complementarity has not been quantitatively analyzed. Here, we focused on the Yangtze River Delta (YRD) urban agglomeration in China and proposed an optimization framework for energy, environment, and economy. The framework considered four sectors: transportation sector, power sector, industry sector, and building sector, in order to determine the most sustainable development pathway for the urban agglomeration. The optimization model considers total costs and greenhouse gas emissions reduction as the objectives and utilizes technologies as constraints to optimize the pathway. We found that this optimization strategy resulted in a 53.1 billion tons increase in CO2 emissions reduction in the region. The results of emission reduction varied across sectors, ranging from 4.5 to 22.2 billion tons CO2 equivalent, and across cities, ranging from 7.1 to 4688.1 Mt. The results suggest that the core cities in the urban agglomeration can take on a leadership role. By promoting cross-sector collaboration and implementing energy recycling, the energy efficiency of surrounding cities can be greatly improved, leading to the sustainable development of the urban agglomeration.

Abstract Background Data on persistent candidemia (PC), a recognized complication of candidemia, are lacking in China. This study aimed to investigate the clinical characteristics and risk factors for the mortality of PC among adults in China. Methods This 6-year retrospective study analyzed the prevalence, species distribution, antifungal susceptibility, risk factors, and patient mortality of PC among adults in three regional tertiary teaching hospitals in China from 2016 to 2021. We collected electronic laboratory records data of PC and non-PC patients and used the Student test or Mann–Whitney U test for a retrospective study. Logistic regression was used to identify risk factors associated with persistent candidemia. Results The definition of PC was fulfilled by 36 patients (13.7%, 36/263). The mean age of the patients was 59.9 years (60 years for patients with PC; 59.8 years for those with non-PC; P > 0.05) and 131 (60.1%) were men [16 with PC (44.4%), 115 with non-PC (63.2%), P 0.05). The 30-day mortality rate was 20.2% (16.7% with PC vs. 20.9% with non-PC, P > 0.05). Multivariable regression analysis showed that use of broad-spectrum antibiotics (odds ratio (OR), 5.925; 95% confidence interval (CI), 1.886–18.616, P = 0.002), fluconazole (OR, 3.389; 95% CI, 1.302–8.820, P = 0.012) and C. parapsilosis infection (OR, 6.143; 95% CI, 2.093–18.031, P = 0.001) were independent predictors of PC, sex (male) (OR, 0.199; 95% CI, 0.077–0.518, P = 0.001) was the protective factor for PC. Respiratory dysfunction (OR, 5.763; 95% CI, 1.592–20.864, P = 0.008) and length of hospital stay(OR, 0.925; 95% CI, 0.880–0.973, P = 0.002) were independent predictors of 30-day mortality in patients with non-PC. C. tropicalis bloodstream infection (OR, 12.642; 95% CI, 1.059–150.951; P = 0.045) was an independent predictor of 30-day mortality in patients with PC. Conclusions The epidemiological data of patients with PC and non-PC were different in the distribution of Candida species, the mean annual incidence and independent predictors of 30-day mortality. Flucytosine and amphotericin B could be used as first-choice drugs in the presence of PC infections.

Abstract With the launch of global decarbonisation, the infiltration of low‐carbon distributed energy resources (DERs) is increasing. The future transaction platforms for those flexibilities are considered to be peer‐to‐peer (P2P) ones conforming to resources' characteristics of small‐scale and independence. This research reviews current flexibility‐related topics and proposes one P2P flexibility market filling in the gaps. The contribution of this research includes: (1) it constructs a flexibility market combining pricing strategy and matching strategy of the mature and successful real‐world P2P business models, accommodating the penetration of DERs. The proposed automatic trading market could mobilise the enthusiasm of market participants and satisfy individuals' needs for a convenient market. (2) it proposes a dynamic pricing strategy where prices fluctuate with the features and portfolios of market participants. More adequate market signals are provided with the promotion of market equity. (3) it discusses the segmentation tendency of the flexibility market when considering energy products as pure commodities following the disintegration from the transmission system operator to the distribution system operator. The feasibility of the proposed market under future energy scenarios is thus explored.

The load demand and distributed generation (DG) integration capacity in distribution networks (DNs) increase constantly, and it means that the violation of security constraints may occur in the future. This can be further worsened by short-term power fluctuations. In this paper, a scheduling method based on a multi-objective chance-constrained information-gap decision (IGD) model is proposed to obtain the active management schemes for distribution system operators (DSOs) to address these problems. The maximum robust adaptability of multiple uncertainties, including the deviations of growth prediction and their relevant power fluctuations, can be obtained based on the limited budget of active management. The systematic solution of the proposed model is developed. The max term constraint in the IGD model is converted into a group of normal constraints corresponding to extreme points of the max term. Considering the stochastic characteristics and correlations of power fluctuations, the original model is equivalently reformulated by using the properties of multivariate Gaussian distribution. The effectiveness of the proposed model is verified by a modified IEEE 33-bus distribution network. The simulation result delineates a robust accommodation space to represent the adaptability of multiple uncertainties, which corresponds to an optional active management strategy set for future selection.

With the increasing penetration of local renewable energy and flexible demand, the system demand is more unpredictable and causes network overloading, resulting in costly system investment. Although the energy storage (ES) helps reduce the system peak power flow, the incentive for ES operation is not sufficient to reflect its value on the system investment deferral resulting from its operation. This paper designs a dynamic pricing signal for ES based on the truncated strategy under robust operation corresponding to the network charge reduction. Firstly, the operation strategy is designed for ES to reduce the total network investment cost considering the uncertainties of flexible load and renewable energy. These nodal uncertainties are converted into branch power flow uncertainties by the cumulant and Gram-Charlier expansion strategy. Then, a time of use (ToU) pricing scheme is designed to guide the ES operation reflecting its impact on network investment based on the long-run investment cost (LRIC) pricing scheme. The proposed ToU LRIC method allocates the investment costs averagely to network users over the potential curtailment periods, which connects the ES operation with network investment. The curtailment amount and the distribution of power flow are assessed by the truncated strategy considering the impact of uncertainties. As demonstrated in a Grid Supply Point (GSP) distribution network in the UK, the network charges at the peak time reduce more than 20% with ES operation. The proposed method is cost-reflective and ensures the fairness and efficiency of the pricing signal for ES.

Abstract This paper proposes a novel real option (RO)‐based network investment assessment method to quantify the flexibility value of battery energy storage systems (BESS) in distribution network planning (DNP). It applied geometric Brownian motion (GBM) to simulate the long‐term load growth uncertainty. Compared with commonly used stochastic models (e.g. normal probability model) that assume a constant variance, it reflects the fact that from the point of prediction, uncertainty would increase as time elapses. Hence, it avoids the bias of traditional net present value (NPV) frameworks towards lumpy investments that cannot provide strategic flexibility relative to more flexible alternatives. It is for the first time to adopt the option pricing method to evaluate the flexibility value of distribution network planning strategies. To optimize the planning scheme, this paper compares the static NPVs and flexibility values of different investment strategies. A 33‐bus system is used to verify the effectiveness of the formulated model. Results indicate that flexibility values of BESS are of utmost importance to DNP under demand growth uncertainties. It provides an analytical tool to quantify the flexibility of planning measures and evaluate the well‐timed investment of BESS, thus supporting network operators to facilitate flexibility services and hedge risks from the negative impact of long‐term uncertainty.

Abstract With the increasing penetration of renewable energy, a new type of energy system, transactive energy systems (TES), has emerged. This study investigates the challenges of optimally operating a TES distribution system with demand response (DR) from the cyber‐physical‐social system (CPSS) perspective. A TES optimization framework that integrates artificial systems, computational experiments, and parallel energy theory for modelling DR, via parallel system theory, is introduced. A data‐driven artificial DR system is created and modelled using limited data. In the computational experiment, a complete information Stackelberg game model for the distribution network operator and the artificial DR system is built. This simulates the response relationship between the distribution network operator and the electricity consumer under different price conditions. In the parallel energy optimization model, a multi‐time scale energy optimization method which considers day‐ahead and intraday scenarios, the interaction between the actual TES and the artificial DR system is shown. Finally, empirical data from the Henan province in China is used as a case study to verify the effectiveness of the optimization method proposed in this study.

The quantity and heterogeneity of intelligent energy generation and consumption terminals in the smart grid are increasing drastically over the years. These edge devices have created significant pressures on cloud computing (CC) system and centralised control for data storage and processing in real-time operation and control. The integration of edge computing (EC) can effectively alleviate the pressure and conduct real-time processing while ensuring data security. This paper conducts an extensive review of the EC-CC computing system and its Application to the smart grid, which will integrate a vast number of dispersed devices. It first comprehensively describes the relationship among CC, fog computing (FC), and EC to provide a theoretical basis for the differentiation. It then introduces the architecture of the EC-CC computing system in the smart grid, where the architecture consists of both hardware structure and software platforms, and key technologies are introduced to support functionalities. Thereafter, the application to the smart grid is discussed across the whole supply chain, including energy generation, transportation (transmission and distribution networks)., and consumption. Finally, future research opportunities and challenges of EC-CC while being applied to the smart grid are outlined. This paper can inform future research and industrial exploitations of these new technologies to enable a highly efficient smart grid under decarbonisation, digitalisation, and decentralisation transitions.

Electric vehicle charging stations (EVCSs) are important infrastructures to support sustainable development of electric vehicles (EVs), by providing convenient, rapid charging services. Therefore, the planning of electric vehicle charging network (EVCN) has attracted wide interest from both industry and academia. In this paper, a multiobjective planning model for EVCN is developed, where a fixed number of EVCSs are planned in the traffic network (TN) to achieve two objectives, i.e., minimizing both average travel distance for charging (TDfC) of EVs and investment costs of EVCN. According to the random characteristics of EVs’ TDfC, its constraint is presented as a chance constraint in the developed EVCN planning model. The nondominated sorting genetic Algorithm II with the constraint domination principle (NSGA-II-CDP) is customized to solve the developed multiobjective EVCN planning model, by designing a special coding scheme, a crossover operator, and a mutation operator. Then, a maximum gradient principle of investment revenue is designed to select the optimal planning strategy from the Pareto-optimal solution set, when taking the investment return ratio as primary consideration. A 25-node TN is used to justify the effectiveness of the developed methodology.

This paper investigates voltage regulation in low voltage (LV) networks under different loading conditions of a supply network, with increased levels of distributed generation, and in particular with a diverse range of locational solar photovoltaic (PV) penetration. This topic has been researched extensively, with beneficial impacts expected up to a certain point when reverse power flows begin to negatively impact customers connected to the distribution system. In this paper, a voltage-based approach that utilizes novel voltage-based reliability indices is proposed to analyse the risk and reliability of the LV supply feeder, as well as its PV hosting capacity. The proposed indices are directly comparable to results from a probabilistic reliability assessment. The operation of the network is simulated for different PV scenarios to investigate the impacts of increased PV penetration, the location of PV on the feeder, and loading conditions of the MV supply network on the reliability results. It can be seen that all reliability indices improve with increased PV penetration levels when the supply network is heavily loaded and conversely deteriorate when the supply network is lightly loaded. Moreover, bus voltages improve when an on-load tap changer is fitted at the secondary trans-former which leads to better reliability performance as the occurrence and duration of low voltage violations are reduced in all PV scenarios. The approach in this paper is opposed to the conventional reliability assessment, which considers sustained interruptions to customers caused by failure of network components, and thus contributes to a comprehensive analysis of quality of service by considering transient events (i.e., voltage related) in the LV distribution network.

Abstract The modeling and multi-energy flow calculation of an integrated energy system (IES) are the bases of its operation and planning. This paper establishes the models of various energy sub-systems and the coupling equipment for an electricity-gas-thermal IES, and an integrated multi-energy flow calculation model of the IES is constructed. A simplified calculation method for the compressor model in a natural gas network, one which is not included in a loop and works in constant compression ratio mode, is also proposed based on the concept of model reduction. In addition, a numerical conversion method for dealing with the conflict between nominal value and per unit value in the multi-energy flow calculation of IES is described. A case study is given to verify the correctness and speed of the proposed method, and the electricity-gas-thermal coupling interaction characteristics among sub-systems are studied.

The European power grid is one of the largest regional interconnected power grids in the world. It realizes a multinational grid operation, which is rare. The total installed capacity of the European power grid is the largest throughout the world. In addition, the integration and utilization of renewable energy in this grid is a great benchmark for other countries and can help promote energy transformation and achieve a high proportion of renewable energy consumption. Based on the analysis of the existing status of the European interconnected power grid and the development history of this power grid, this paper summarizes four key development stages of the European power grid. In addition, the characteristics of each stage and the development prospect of the European power grid are analyzed. On this basis, this paper gives suggestions for the development and construction of China’s energy internet; this can provide valuable reference for further studies on China’s energy internet.

Developing a reasonable, efficient distributed market transaction mechanism is an important issue in distribution systems. The gaming relation between distributed transaction market entities has yet to be fully elucidated in various trading links, and the impact of distributed transactions on distribution network operations has yet to be comprehensively analyzed. This paper proposes a novel distributed Peer-to-Peer (P2P) day-ahead trading method under multi-microgrid congestion management in active distribution networks. First, a flexible load model for price-based demand response load and an autonomous microgrid economic scheduling model are constructed. Second, under normal operation of the distribution network, a non-cooperative game model and Stackelberg game model are employed to separately and comprehensively analyze gaming relationship among sellers, and between sellers and buyers. Thereafter, a congestion management method based on market capacity is established from the perspective of distribution network control centers. Finally, the impact of end energy consumption characteristics on microgrid economic scheduling and P2P trading is analyzed through a modified IEEE 33-node power distribution system. The economic and technical benefits such as congestion mitigation and network loss reduction that produced by P2P trading to the operation of microgrid systems are analysed with specific indicators.

It is of significant economic value to use an electric boiler to coordinate the heating network and power grid to reduce wind curtailment during heating supply seasons in the north area. This paper presents an optimal economic dispatching model of combined heat and power energy systems to minimize the total operation cost and wind curtailment by using electric boilers. The proposed model comprehensively considers the thermal characteristics of buildings and the constraints of electric and thermal power balancing, combined heat and power (CHP) units, heat storage tanks, electric boiler power, and wind power. To address this model, a linearization optimization model is built, where different dispatch strategies under CHP only, CHP + HST (heat storage tanks), and CHP + HST + EB (electric boiler) different conditions are conducted. Thereafter, the relationship between indoor temperature and thermal power is analyzed with consideration of the temperature time-delay effect and the heat loss on heating balance. A 6-node combined heat and power energy system is used to validate the effectiveness of the proposed model and solution method. Furthermore, the case studies illustrate the impact of heat storage tanks and the electric boiler on wind power curtailment and coal consumption.

Combined borehole (BH) heat storage systems, batteries and power-to-gas system have the potential to shift load, reduce carbon emissions, provide hydrogen for fuel cell cars and save energy costs for end customers on an extended scale. This study proposes an optimal operation strategy for a local multi-vector energy storage system, which includes batteries, BH thermal storage, the power to the gas system and the fuel cell cars system. These storage systems can be divided into the short-term storage system and inter-seasonal storage system or low capacity storage system and high capacity storage system. The optimisation problem is divided into a two-stage framework, (i) the first stage optimisation is seasonal optimisation, which gives an approximate optimal operation plan for BH heat storage systems in the following year; (ii) the second stage develops a day-ahead robust optimal plan for all storage systems. Finally, the algorithm will return to seasonal optimisation to update the operation plan for BH heat storage systems to make results more accurate. The test case of eight nodes illustrates that the combined energy system of photovoltaic, heat pump power to gas, BH and batteries can provide hydrogen to fuel cell cars and significantly save power costs for customers with the optimal operation.

The influence of parameters on system states for parametric problems in power systems is to be evaluated. These parameters could be renewable generation outputs, load factor, etc. Polynomial approximation has been applied to express the nonlinear relationship between system states and parameters, governed by the nonlinear and implicit equations. Usually, sampling-based methods are applied, e.g., data fitting methods and sensitivity methods, etc. However, the accuracy and stability of these methods are not guaranteed. This paper proposes an innovative method based on Galerkin method, providing global optimal approximation. Compared to traditional methods, this method enjoys high accuracy and stability. IEEE 9-bus system is used to illustrate its effectiveness, and two additional studies including a 1648-bus system are performed to show its applications to power system analysis.

The change of customer behaviors and the fluctuation of spot prices can affect the benefits of electricity retailers. To address this issue, an incentive-based demand response (DR) model involving the utility and elasticity of customers is proposed for maximizing the benefits of retailers. The benefits will increase by triggering an incentive price to influence customer behaviors to change their demand consumptions. The optimal reduction of customers is obtained by their own profit optimization model with a certain incentive price. Then, the sensitivity of incentive price on retailers' benefits is analyzed and the optimal incentive price is obtained according to the DR model. The case study verifies the effectiveness of the proposed model.

A day-ahead economic scheduling method based on chance-constrained programming and probabilistic sequence operation is proposed in this paper for an electric vehicle (EV) battery swapping station (BSS), considering the dual uncertainties of swapping demand and photovoltaic (PV) generation. First of all, a BSS day-ahead scheduling model that can deal with the uncertainties is established by using the chance-constrained programming. The optimization objective is to minimize the cost of electricity purchased from the utility grid with the chance constraints of swapping demand satisfaction and the confidence level of the minimum cost. Then, the deterministic transformation of chance constraints is implemented based on probabilistic sequences of stochastic variables. Thereafter, the feasible solution space of the proposed model is determined based on the battery controllable load margin, and then the fast optimization method for the BSS day-ahead scheduling model is developed by combining the feasible solution space and genetic algorithm (GA). In order to evaluate the solution quality, a risk assessment method based on the probabilistic sequence for day-ahead scheduling solutions is proposed. Finally, the efficiency and applicability of the proposed method is verified through the comparative analysis on a PV-based BSS system. Results illustrate that the model can provides a more reasonable charging strategy for the BSS operators with different risk appetite.

In order to understand energy consumption and ensure precise load prediction, it is essential to identify the variation of gas consumption in response to ambient temperature change outdoor. In this paper, the relationship is identified by using Empirical Mode Decomposition (EMD) and linear regression analysis together with outlier detection. EMD is a data processing tool that can divide original data into several Intrinsic Mode Functions (IMFs) with a lower frequency residue. By applying the data mining technique-Mahalanobis distance measurement, some outliers from real-time gas consumption and temperature data points are detected, which are excluded from the data sets to ensure accuracy. Correlation coefficients between the gas load and ambient temperature are calculated and denoted as an important index to quantify their relationship through regression analysis. By comparing such indices on realtime data and EMD processed data, the weather-sensitive part of gas demand is identified. The methods are implemented on a local energy system and the results reveal that the outcome after EMD presents a higher level of correlation between the gas load and ambient temperature, compared to the results from directly using the real-time gas load and temperature data.

High temperature Superconducting Magnetic Energy Storage (SMES) systems can exchange energy with substantial renewable power grids in a small period of time with very high efficiency. Because of this distinctive feature, they store the abundant wind power when the power network is congested and release the energy back to the system when there is no congestion. However, considering the cost and lifespan of SMES systems, there is an urgent demand to conduct a cost-benefit analysis to justify its role in smart grid development. This study explores the application and performs economic analysis of a 5 MJ SMES in a practical renewable power system in China based on the PSCAD/ EMTDC software. An optimal location of SMES in Zhangbei wind farm is presented using real power transmission parameters. The stabilities of the renewable power grid with and without SMES are discussed. In addition, a financial feasibility study is conducted by comparing the cost and the savings from wind power curtailment of deploying SMES and battery. The economic analysis tries to find the balance between SMES investment cost and wind farm operation cost by using real data over a calendar year. The technical analysis can help guide the optimal allocation of SMES for compensating power system instability with substantial wind power. Further, the economic analysis provides a useful indication of its practical application feasibility to fight the balance between cost and benefit. Keywords: High temperature Superconducting Magnetic Energy Storage (SMES) system, Renewable energy, Smart grid, Allocation planning, Economic planning, Large-scale energy storage

Abstract This paper presents a novel integrated framework for simulating the charging load of an electric vehicle charging station. The framework is built based on the cellular automaton model, including five modules: vehicle generation, charging station, lane change, speed update and boundary clear. The proposed framework can effectively stimulate the system dynamics of the traffic system and charging power process in a charging station. Case studies have verified its feasibility and effectiveness.

Domestic heating is the major demand of energy systems, which can bring significant uncertainties to system operation and shrink the security margin. From this aspect, the borehole system, as an interseasonal heating storage, can effectively utilize renewable energy to provide heating to ease the adverse impact from domestic heating. This paper proposes an optimal charging strategy for borehole thermal storage by harvesting energy from photovoltaic (PV) generation in a low-carbon space heating system. The system optimizes the heat injection generated by air source heat pump in the charging seasons to charge the borehole, which provides high inlet temperature for ground source heat pump to meet space heating demand in discharging seasons. The borehole is modeled by partial differential equations, solved by the finite-element method at both 2D and 3D for volume simulation. The pattern search optimization is used to resolve the model. The case study illustrates that with the optimal charging strategies, less heat flux injection can help the borehole to reach a higher temperature so that the heating system is more efficient compared with boilers. This paper can benefit communities with seasonable borehole storage to provide clean but low-cost heating and also maximize PV penetration.

The combined heat and power (CHP) systems can provide heat and electricity simultaneously. They are promising in the future energy landscape because of high efficiency and low emissions. This paper proposes a new operation optimization model of CHPs in deregulated energy markets. Both CHPs' overall efficiency and heat to electricity ratio are closely linked with the loading level, which are dynamically determined in this paper. A discrete optimization model is then proposed to determine the optimal real-time operation strategies for the CHPs. The optimization problem is solved by the interior point method with discrete time intervals, in which the discrete optimal operation points can be identified effectively. This step projects the potential operation strategies that could produce maximum benefits. Finally, a dynamic programming algorithm is developed to maximize the profits of CHPs through dynamically modifying the operation strategies projected in the previous step considering transient constraints. The proposed new methodology is demonstrated on a 1-MW CHP system with real-time data.

Three-phase imbalance causes uneven voltage drops along low-voltage feeders. Under long-term load growth, the phase with the lowest terminal voltage will trigger network reinforcements, which are earlier than if the three phases were balanced. This leads to a higher voltage-driven reinforcement cost (VRC) than the balanced case. Three-phase power flow analyses are not suitable for VRC estimations under serious data deficiency (without customers' phase connectivity and smart metering data), and are not scalable due to the iterative nature, which brings a prohibitively high computation burden on a utility level with millions of feeders. To overcome the challenges, this paper proposes a novel scalable methodology for VRC estimations that is applicable from an individual feeder to millions of feeders, where the level of information is insufficient to support accurate three-phase power flow studies. The key is to use five types of load current distributions to represent customers' phase allocations and individual demands, and to incorporate these distributions into an equivalent impedance matrix, which allows a straightforward VRC estimation without iterations. This paper applies this methodology to an individual feeder, showing that: 1) the VRC decreases (increases) with the increase of the K (beta) factor of the trapezoid (triangular-rectangular) distribution, given that other conditions remain the same; 2) the VRC is more sensitive to voltage imbalance than to current imbalance; and 3) if the three phases are balanced, the change of any single variable results in an increase of the VRC, given that all other input variables remain constant.

This paper develops a discrete operation optimization model for combined heat and powers (CHPs) in deregulated energy markets to maximize owners’ profits, where energy price forecasting is included. First, a single input and multi-output (SIMO) model for typical CHPs is established, considering the varying ratio between heat and electricity outputs at different loading levels. Then, the energy prices are forecasted with a gray forecasting model and revised in real-time based on the actual prices by using the least squares method. At last, a discrete optimization model and corresponding dynamic programming algorithm are developed to design the optimal operation strategies for CHPs in real-time. Based on the forecasted prices, the potential operating strategy which may produce the maximum profits is pre-developed. Dynamic modification is then conducted to adjust the pre-developed operating strategy after the actual prices are known. The proposed method is implemented on a 1 MW CHP on a typical day. Results show the optimized profits comply well with those derived from real-time prices after considering dynamic modification process.

The increasing number of electric vehicles (EVs) connected to existing distribution networks as time-variant loads cause significant distortions in line current and voltage. A novel EV’s intelligent integrated station (IIS) making full use of retired batteries is introduced in this paper to offer a potential solution for accommodating the charging demand of EVs. It proposes the concept of generalized energy in IIS, based on the energy/power flow between IIS and EVs, and between IIS and the power grid, to systematically evaluate the energy capacity of IIS. In order to derive a unique and satisfactory operation mode, information from both the grid (in terms of load level) and IIS (in terms of its energy capacity and EVs battery charging/exchanging requests) is merged. Then, based on the generalized energy of different systems, a novel charging/discharging control strategy is presented and whereby the operating status of the grid and energy capacity of IIS are monitored to make reasonable operation plans for IIS. Simulation results suggest that the proposed IIS offers peak load shifting when EV battery charging/exchanging requests are satisfied compared to existing charging stations.

With the accelerated development of urbanization, waste disposal has become a tough problem. If waste cannot be disposed properly, it will lead to environment pollution and waste of resources. Since the energy utilization of the Waste Processing Base (WPB) has not been considered thoroughly, optimal planning of the Multi-Energy Circular System (MECS) coupled with the WPB is studied in this paper. Based on a typical WPB, this paper adds Power to Gas (P2G) and energy storage equipment, and applies a bi-level optimization model to optimize energy utilization. The minimum of total annual cost is the objective of the upper model, whose decision variables are the capacity of each equipment. The minimum annual operating cost is the lower model’s objective whose decision variables are the control parameters of certain energy equipment. Finally, a practical WPB is used for the demonstration and simulation of the proposed planning scheme. The analysis of the simulation results indicates that the collaborative optimization of the MECS coupled with WPB is effective, and improves the benefits of energy, economy, and environment enormously.

The grid-connected micro gas turbine (MGT) generation system is playing an important role in power systems because of its demand response capability and application in combined heat and power (CHP) systems. When applied to promote demand response, the generation system is expected to respond to follow instructions quickly, but a rapid response harms the safety and is not conducive to the benefits of customers, which leads to a contradiction. In this paper, a closed-loop power control is introduced for the MGT to improve demand response capability. The rate of fuel valve opening is limited so as to protect the equipment from thermal fatigue threats. An optimization method is developed for identifying the control parameters, balancing the response time and unrealized energy in the regulation process. An improved whale optimization algorithm (IWOA) is proposed to implement the optimization. Results of the algorithm performance verify that WOA is competitive with other heuristic algorithms, and IWOA is more suitable for parameter optimization problems than WOA because of better efficiency and exploitation capability. Results of power response further indicate that the proposed control strategy can achieve expected aims and is suitable for the MGT generation system.

With the development of smart meters, a realtime pricing (RTP) demand response is becoming possible for households in distribution networks. The power flow can be bidirectional in distribution networks which become smarter with distributed generators (DGs). It is expensive to import electricity from the generation far from load centers because of the cost of power loss and network use, so that it is more economical to use electricity generated by local distributed generators. Therefore, in order to curtail operating costs of distribution networks, this paper proposes a model of economic optimization conducted by distribution network operators. The electricity purchasing costs for distribution network operators are minimized by optimizing electric power from transmission systems and local distributed generators. Further, based on price elasticity, the formulations of load demand considering RTP are proposed with economic optimization of distribution networks. The economic optimization problems are resolved by an interior point method. The case study shows that peak load demand can be reduced about 3.5% because the household RTP and electricity purchasing costs of distribution network operators can save 28.86 £ every hour.

Potential machine-grid interactions caused by large-scale wind farms have drawn much attention in recent years. Previous work has been done by analyzing the small–signal modeling of doubly-fed induction generators (DFIGs) to obtain the oscillation modes. This paper, by making use of the metered power data of wind generating sets, studies the correlation between oscillation modes of the DFIG system and influence factors which includes wind speed and grid voltage. After the metered data is segmented, the Prony algorithm is used to analyze the oscillation modes contained in the active power. Then, the relevant oscillation modes are extracted in accordance with the small-signal analysis results. Meanwhile, data segments are clustered according to wind speed and grid voltage. The Apriori algorithm is finally used to discuss the association rules. By training the mass of data of wind generating sets, the inevitable association rules between oscillation modes and influence factors can be mined. Therefore, the prediction of oscillation modes can be achieved based on the rules. The results show that the clustering number quite affects the association rules. When the optimal cluster number is adopted, part of the wind speed/voltage clusters can analyze the certain oscillation modes. The predicted results are quite consistent with the practical data.

Micro phasor measurement units (μPMU) installed in active distribution networks are very useful for improving observability by acquiring system real-time data. However, three-phase imbalance and harmonic power flows adversely impact the accuracy of synchronous measurements, which implies the importance of phasor estimation errors. This paper proposes a new phasor estimation algorithm for μPMU in active distribution networks that uses a conditional maximum likelihood (CML) estimation method. Firstly, the signal model of three-phase, three-wire and four-wire imbalance systems is established. Then, the probability distributions of the magnitude and phase angles are derived from the geometric characteristics of the CML method by solving the geometric equation. Simulation results show that the proposed CML based method is effective for estimating phasor and impedance models of active distribution networks by using μPMU measurement data.

Network-connected combined heat and powers (CHPs), owned by a community, can export surplus heat and electricity to corresponding heat and electric networks after community loads are satisfied. This paper proposes a new optimization model for network-connected CHP operation. Both CHPs’ overall efficiency and heat to electricity ratio (HTER) are assumed to vary with loading levels. Based on different energy flow scenarios where heat and electricity are exported to the network from the community or imported, four profit models are established accordingly. They reflect the different relationships between CHP energy supply and community load demand across time. A discrete optimization model is then developed to maximize the profit for the community. The models are derived from the intervals determined by the daily operation modes of CHP and real-time buying and selling prices of heat, electricity and natural gas. By demonstrating the proposed models on a 1 MW network-connected CHP, results show that the community profits are maximized in energy markets. Thus, the proposed optimization approach can help customers to devise optimal CHP operating strategies for maximizing benefits.