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The uncertainties of the power load, wind power, and photovoltaic power lead to errors between point prediction values and real values, which challenges the safe operation of distribution networks. In this paper, a robust reactive power scheduling (RRPS) model based on a modified bootstrap technique is proposed to consider the uncertainties of power loads and renewable energy sources. Firstly, a deterministic reactive power scheduling (DRPS) model and an RRPS model are formulated. Secondly, a modified bootstrap technique is proposed to estimate prediction errors of power loads and renewable energy sources without artificially assuming the probability density function of prediction errors. To represent all possible scenarios, point prediction values and prediction errors are combined to construct two worst-case scenarios in the RRPS model. Finally, the RRPS model is solved to find a scheduling scheme, which ensures the security of distribution networks for all possible scenarios in theory. Simulation results show that the worstcase scenarios constructed by the modified bootstrap technique outperform popular baselines. Besides, the RRPS model based on the modified bootstrap technique balances economics and security well.

With the exacerbation of environmental issues and the ongoing energy crisis, the large-scale integration of renewable energy resources has significantly increased the demand for flexibility in distribution systems. Establishing a comprehensive flexibility evaluation index system is crucial for the development, promotion, and optimization of AC/DC distribution systems. This paper focuses on the flexibility evaluation of AC/DC distribution systems, proposing a detailed flexibility evaluation index system and a comprehensive evaluation method based on the ANP-Entropy weighting model. The proposed flexibility evaluation index system comprises 18 key indexes categorized into four essential dimensions: overall flexibility performance, flexible resources participation, operational performance, and economic performance. The ANP-Entropy weighting model is employed to perform a thorough evaluation of the flexibility of AC/DC distribution systems. To validate the effectiveness of the proposed evaluation method, a case study is conducted. Additionally, the study explores the impacts of controllable distributed generator (CDG) capacity, energy storage system (ESS) capacity, and voltage source converter (VSC) capacity on the flexibility of AC/DC distribution systems. The results provide valuable insights into optimizing the flexibility and performance of AC/DC distribution systems.

With the large-scale distributed generations (DGs) being connected to distribution network (DN), the traditional day-ahead reconfiguration methods based on physical models are challenged to maintain the robustness and avoid voltage off-limits. To address these problems, this paper develops a deep re-inforcement learning method for the sequential reconfiguration with soft open points (SOPs) based on real-time data. A state-based decision model is first proposed by constructing a Marko decision process-based reconfiguration and SOP joint optimization model so that the decisions can be achieved in milliseconds. Then, a deep reinforcement learning joint framework including branching double deep $Q$ network (BDDQN) and multi-policy soft actor-critic (MPSAC) is proposed, which has significantly improved the learning efficiency of the decision model in multi-dimensional mixed-integer action space. And the influence of DG and load uncertainty on control results has been minimized by using the real-time status of the DN to make control decisions. The numerical simulations on the IEEE 34-bus and 123-bus systems demonstrate that the proposed method can effectively reduce the operation cost and solve the overvoltage problem caused by high ratio of photovoltaic (PV) integration.

Reliable and accurate ultra-short-term prediction of wind power is vital for the operation and optimization of power systems. However, the volatility and intermittence of wind power pose uncertainties to traditional point prediction, resulting in an increased risk of power system operation. To represent the uncertainty of wind power, this paper proposes a new method for ultra-short-term interval prediction of wind power based on a graph neural network (GNN) and an improved Bootstrap technique. Specifically, adjacent wind farms and local meteorological factors are modeled as the new form of a graph from the graph-theoretic perspective. Then, the graph convolutional network (GCN) and bi-directional long short-term memory (Bi-LSTM) are proposed to capture spatio-temporal features between nodes in the graph. To obtain high-quality prediction intervals (PIs), an improved Bootstrap technique is designed to increase coverage percentage and narrow PIs effectively. Numerical simulations demonstrate that the proposed method can capture the spatiotemporal correlations from the graph, and the prediction results outperform popular baselines on two real-world datasets, which implies a high potential for practical applications in power systems.

With the increasing use of renewable resources and electric vehicles (EVs), the variability and uncertainty in their nature put forward a high requirement for flexibility in AC distribution system incorporating voltage source converter (VSC) based multi-terminal direct current (MTDC) grids. In order to improve the capability of distribution systems to cope with uncertainty, the flexibility enhancement of AC-MTDC distribution systems considering aggregated EVs is studied. Firstly, the charging and discharging model of one EV is proposed considering the users' demand difference and traveling needs. Based on this, a vehicle-to-grid (V2G) control strategy for aggregated EVs to participate in the flexibility promotion of distribution systems is provided. After that, an optimal flexible dispatching method is proposed to improve the flexibility of power systems through cooperation of VSCs, controllable distributed generations (CDGs), aggregated EVs, and energy storage systems (ESSs). Finally, a case study of an AC-MTDC distribution system is carried out. Simulation results show that the proposed dispatching method is capable of effectively enhancing the system flexibility, reducing renewable power curtailment, decreasing load abandonment, and cutting down system cost.

Taking into account the challenges of obtaining accurate physical parameters and uncertainties arising from the integration of a large number of sources and loads, this paper proposes a real-time voltage control method for AC/DC distribution networks. The method utilizes model-free generation and coordinated control of multiple converters, and employs a combination of agent modeling and multi-agent soft actor critic (MASAC) techniques for modeling and solving the problem. Firstly, a complex nonlinear mapping relationship between bus power and voltage is established by training an power-voltage model, to address the issue of obtaining physical parameters in AC/DC distribution networks. Next, a Markov decision process is established for the voltage control problem, with multiple intelligent agents distributed to control the active and reactive power at each converter, in response to the uncertainties of photovoltaic (PV) and load variations. Using the MASAC method, a centralized training strategy and decentralized execution policy are implemented to achieve distributed control of the converters, with each converter making optimal decisions based on its local observation state. Finally, the proposed method is verified by numerical simulations, demonstrating its sound effectiveness and generalization ability.

With the increasing penetration of photovoltaics in distribution networks, the adaptability of distribution network under uncertainties needs to be considered in the planning of distribution systems. In this paper, the interval arithmetic and affine arithmetic are applied to deal with uncertainties, and an affine arithmetic based bi-level multi-objective joint planning model is built, which can obtain the planning schemes with low constraint-violation risk, high reliability and strong adaptability. On this basis, a bi-level multi-objective solution methodology using affine arithmetic based non-dominated sorting genetic algorithm II is proposed, and the planning schemes that simulta-neously meet economy and adaptability goals under uncertainties can be obtained. To further eliminate bad solutions and improve the solution qualities, an affine arithmetic based dominance relation weakening criterion and a deviation distance based modification method are proposed. A 24-bus test system and a 10 kV distribution system of China are used for case studies. Different uncertainty levels are compared, and a sensitivity analysis of key parameters is conducted to explore their impacts on the final planning schemes. The simulation results verify the advantages of the proposed affine arithmetic based planning method.

Abstract Accurately predicting photovoltaic output power is one of the most important basic tasks for the rapid development of the smart grid. The factors that influence photovoltaic output power are not fully considered in the current forecast model. To this end, an all‐factor photovoltaic short‐term output power prediction model based on improved ensemble empirical mode decomposition (EEMD) and the seeker optimization algorithm (SOA) ‐BP neural network model is proposed here. First, different similarity algorithms are used in the selection of similar days of different weather types. Second, the factors that affect the PV output power are divided into deterministic parameters and stochastic parameters. The stochastic parameters of similar daily samples are used to constitute a time series, which is decomposed into several stationary signals by EEMD, and then, these signals are trained separately by the SOA‐BPNN model. Finally, the final forecast result can be synthesized with the deterministic parameters and the accumulation of the predicted data of individual signals. Compared with the results of different prediction models, the prediction accuracy of the proposed model is much higher.

Abstract An interval power flow calculation (PFC) algorithm for multi‐terminal DC distribution networks is proposed to handle the uncertainties of distributed generation output powers and loads. Firstly, an equivalent resistance is introduced to represent the effect of droop control in DC distribution networks. The power losses of the equivalent resistance are offset by adding the injection powers to droop nodes, which guarantees the precision of the equivalent resistance model. The Newton–Raphson method is used for deterministic PFC based on the equivalent resistance, and it is applicable to DC distribution networks with different kinds of network topologies and voltage source converter control modes. Secondly, based on the deterministic PFC algorithm and affine arithmetic, an interval PFC algorithm is presented to handle the uncertainties of distributed generation output powers and loads. The affine arithmetic based interval PFC algorithm not only can determine power flow intervals quickly and accurately, but also can analyse the influences of distributed generation outputs to nodal voltages. Finally, a series of test systems are used to validate the proposed algorithms. Simulation results illustrate the accuracy and efficiency of the deterministic PFC algorithm and the interval PFC algorithm for multi‐terminal DC distribution networks.

The fluctuation of output power of renewable energies and loads brings challenges to the scheduling and operation of the distribution network. In this paper, a robust voltage control model is proposed to cope with the uncertainties of renewable energies and loads based on an improved generative adversarial network (IGAN). Firstly, both real and predicted data are used to train the IGAN consisting of a discriminator and a generator. The noises sampled from the Gaussian distribution are fed to the generator to generate a large number of scenarios that are utilized for robust voltage control after scenario reduction. Then, a new improved wolf pack algorithm (IWPA) is presented to solve the formulated robust voltage control model, since the accuracy of the solutions obtained by traditional methods is limited. The simulation results show that the IGAN can accurately capture the probability distribution characteristics and dynamic nonlinear characteristics of renewable energies and loads, which makes the scenarios generated by IGAN more suitable for robust voltage control than those generated by traditional methods. Furthermore, IWPA has a better performance than traditional methods in terms of convergence speed, accuracy, and stability for robust voltage control.

The daily load profiles modeling is of great significance for the economic operation and stability analysis of the distribution network. In this paper, a flow-based generative network is proposed to model daily load profiles of the distribution network. Firstly, the real samples are used to train a series of reversible functions that map the probability distribution of real samples to the prior distribution. Then, the new daily load profiles are generated by taking the random number obeying the Gaussian distribution as the input data of these reversible functions. Compared with existing methods such as explicit density models, the proposed approach does not need to assume the probability distribution of real samples, and can be used to model different loads only by adjusting the structure and parameters. The simulation results show that the proposed approach not only fits the probability distribution of real samples well, but also accurately captures the spatial-temporal correlation of daily load profiles. The daily load profiles with specific characteristics can be obtained by simply classification.

The continuous access of new energy and distributed energy, as well as the random disturbance of load power, affect the security and stability of microgrids. A virtual generation ecosystem control (VGEC) strategy is proposed in this paper, which adopts the idea of time tunnel and the principle of a new win-loss criterion to achieve a fast automatic generation control power dispatch, optimal coordinated control of microgrids. A two-layer dynamic power dispatch structure is introduced in the proposed strategy, which combines hierarchical Q-learning with consensus theory to improve the adaptability of the consistency algorithm in complex random environments. Both the IEEE standard two-area load frequency control model and the interconnected microgrids model are used in simulation for comparison and verification. The results show that, by using the VGEC strategy, the control performance of microgrids can be improved, while can reduce power generation cost, and obtain faster convergence speed and stronger robustness compared with other algorithms.

Non-Intrusive Load Monitoring (NILM) makes it possible for users and energy providers to track the fine-grained energy consumption information of residential and commercial buildings. The load identification methods in NILM usually require labeling many samples for training and evaluation, which is always expensive and time-consuming. In order to reduce the labeling cost, this paper proposed a load identification method based on Active Deep Learning (ADL). In this method, Discrete Wavelet Transform (DWT) was applied to extract high-dimensional appliance features from original current signals. Then a pool-based or stream-based active deep learning model was built to learn the features and select high-value samples that worthy of labeling. A mixed dataset based on three public datasets was formed to evaluate the proposed method and three sampling approaches of active learning. The results showed that the proposed method could significantly reduce labeling cost on large datasets, and the number of samples required is 33% lower than the state-of-the-art method when the F1 score is equal. Compared with pool-based sampling approaches, the stream-based approach's benefits are that the classifier improved and the query frequency decreased with continuous input of samples.

Multienergy load forecasting (MELF) is the premise of regional integrated energy systems (RIES) production planning and energy dispatch. The key of MELF is the consideration of multienergy coupling and the improvement of prediction accuracy. Therefore, a MELF method considering the multienergy coupling of variation characteristic curves (MELF_MECVCC) for RIES is proposed. The novelty of MELF_MECVCC lies in the following three aspects. 1) For the trend stripping and volatility extraction of multienergy load time series, the extreme-point symmetric mode decomposition-sample entropy (ESMD-SE) method is introduced to decompose and reconstruct the variation characteristic curves of multienergy, including trend curve and fluctuation curve. 2) The multienergy coupling of the variation characteristic curves is considered to reflect the variation characteristics of the multienergy loads. 3) Different methods are applied according to different variation characteristics; i.e., the combined method based on multitask learning and long short-term memory network (MTL-LSTM) is applied to predict the trend curve with strong correlation and the least square support vector regression (LSSVR) method is applied to predict the fluctuation curve with strong volatility and high complexity. Based on the actual data set of the University of Texas in Austin, the MELF_MECVCC model is simulated and verified, which shows that the model reduces the mean absolute percentage error (MAPE) and the root mean square error (RMSE) and fits better with the original load and has higher prediction accuracy, compared with current advanced algorithms.

Scientific scale forecasting of multi-type electric vehicles (EVs) is critical to accurately analyze the planning and operation of battery-swapping stations (BSSs) and charging stations (CSs). This paper predicts the proportions of plug-in electric vehicles (PEVs), hybrid electric vehicles (HEVs), and battery-swapping electric vehicles (BSEVs) in the total EV fleet in multi-scenarios via a system dynamics (SD) method. Relying on the predicted evolution scale of the BSEVs and the service demand of BSSs calculated by the service radius (SR) method, an improved differential evolutional algorithm combing with Monte Carlo searching (IDEA-MCS) method is proposed to obtain the optimal location of BSSs in a certain region in Beijing, which achieves an economic optimum of BSSs under the battery-swapping mode (BSM) via centralized charging and unified distribution (CCAUD). The analytical results show that the proportion of the BSEVs in different scenarios is the major driver that impacts the location of BSSs. The distribution of BSSs' BS demand in the optimistic scenario is more inhomogeneous than that in the other scenarios. In addition, a cross-comparison of optimal profits in different scenarios is conducted to verify the optimality of BSS locations for a given scenario. Finally, the proposed IDEA-MCS method is compared with the DEA method and IDEA method to verify its optimality.

The scenarios of a half wavelength transmission line (HWTL) connected to the original DC asynchronous interconnection system are established based on the quasi-steady state model of HWTL, which include single or double loops of single channel and multi-channel scenarios with different starting and landing points. Firstly, the steady state power flow characters of multi-scenario transmission schemes are studied. The voltage distribution characters of HWTL are analyzed when its transmission power and power factor vary, and the influence of the DC power variation on voltage of HWTL is discussed. Then the transient characters of HWDHTS are explored. The DC commutation failure caused by the fault of HWTL is simulated and the power transfer capacity of HWTL while DC blocking is calculated in this paper. Keywords: UHV half-wavelength AC power transmission line, UHV DC system, Steady state characters, Transient stability

Abstract Smart electricity utilization (SEU) is one of the most important components in a smart grid. It is crucial to evaluate efficiency, safety, and demand response capability of electricity users to achieve the smart use of electricity. The analytic hierarchy process (AHP) uses subjective criteria to determine index weights in multi-criteria decision-making problems, while the entropy method provides objectivity in determining index weights. Taking into account the uncertainty of expert scoring and user data, a hybrid interval analytic hierarchy process (IAHP) and interval entropy (IE) method is proposed for electricity user evaluation (EUE). Specifically, in the proposed method, electricity users are evaluated in terms of energy efficiency, safety monitoring, and demand response. The weights of EUE indices are calculated under uncertainty. The proposed approach derives subjective weights of EUE indices by the IAHP with expert scoring as input data, and determines objective weights of EUE indices by the IE method with user data as inputs. In order to obtain the optimal combined index weights, the two weights are normalized by a selected weight factor. Numerical case studies illustrate the effectiveness and advantages of the proposed approach, which combines subjective and objective information to derive the optimal combined index weights.

As integrated electrical and natural-gas systems (IENGS) are popularized, the uncertainties brought by variation of electrical load, power generation, and gas load should not be ignored. The aim of this paper is to analyze the impact of those uncertain variables on the steady-state operation of the whole systems. In this paper, an interval energy flow model considering uncertainties was built based on the steady-state energy flow. Then, the Krawczyk−Moore interval iterative method was used to solve the proposed model. To obtain precise results of the interval model, interval addition and subtraction operations were performed by affine mathematics. The case study demonstrated the effectiveness of the proposed approach compared with Monte Carlo simulation. Impacts of uncertainties brought by the variation of electrical load, power generation, and gas load were analyzed, and the convergence of energy flow under different uncertainty levels of electrical load was studied. The results led to the conclusion that each kind of uncertainties would have an impact on the whole system. The proposed method could provide good insights into the operating of IENGS with those uncertainties.

The estimation of losses of distribution feeders plays a crucial guiding role for the planning, design, and operation of a distribution system. This paper proposes a novel estimation method of statistical line loss of distribution feeders using the feeder cluster technique and modified eXtreme Gradient Boosting (XGBoost) algorithm that is based on the characteristic data of feeders that are collected in the smart power distribution and utilization system. In order to enhance the applicability and accuracy of the estimation model, k-medoids algorithm with weighting distance for clustering distribution feeders is proposed. Meanwhile, a variable selection method for clustering distribution feeders is discussed, considering the correlation and validity of variables. This paper next modifies the XGBoost algorithm by adding a penalty function in consideration of the effect of the theoretical value to the loss function for the estimation of statistical line loss of distribution feeders. The validity of the proposed methodology is verified by 762 distribution feeders in the Shanghai distribution system. The results show that the XGBoost method has higher accuracy than decision tree, neural network, and random forests by comparison of Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Absolute Percentage Error (APE) indexes. In particular, the theoretical value can significantly improve the reasonability of estimated results.