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This paper proposes a novel evaluation method to address the challenge of evaluating insulation degradation in power transformer windings based on incomplete online Internet of Things (IoT) sensing data. The method leverages the Wasserstein Slim Generative Adversarial Imputation Network with Gradient Penalty algorithm to fill the irregularly missing power transformer IoT perception data, including voltage, current, temperature, and partial discharge. Subsequently, electrical, thermal, and mechanical performance degradation damage indicators for transformer winding insulation are constructed using the filled and complete IoT perception data. By applying the tensor fusion algorithm, the characteristics of these degradation damage indicators are fused, leading to the development of a comprehensive degradation evaluation index for the winding insulation. The evaluation of the winding insulation degradation state is achieved through the minimum quantization error method. The proposed method is validated using the real‐world transformer IoT perception data, and the experimental results demonstrate its ability to accurately assess the degree of winding insulation degradation, regardless of the presence of random or continuous irregularities in IoT sensing data. [ABSTRACT FROM AUTHOR]

As renewable power generation increases in distribution networks, the real‐time power balance is becoming a tough challenge. Unlike simple peak‐load shedding or demand turn‐down scenarios, generation following (GF) requires persistent and precise control due to the temporal response performance of controlled resources. This motivates a comprehensive control design considering the temporal response limits and execution performance of air conditioner clusters (ACCs) when providing GF. Accordingly, this paper proposes a self‐constraint model predictive control (SMPC) that properly allocates the generation following task among different ACCs, consisting of three main parts: response rehearsal, distributed consistency‐based power allocation, and real‐time task execution. Specifically, the rehearsal knowledge of ACCs is evaluated by introducing model predictive control (MPC) to track power signals with different values and thus obtain prior factors, including the upward/downward limits and control cost function. On this basis, the coherence of the incremental response costs of different clusters is achieved to allocate the GF signals. Once the optimised following signals (OFS) are obtained, a real‐time MPC for generation following task execution is employed, where the OFS are used as reference and the upward/downward limits are used as constraints. Simulations are conducted to verify the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

With the growing popularity of charging pile infrastructure and the development of smart electronic devices and 5G communication technologies, the electric vehicle aggregator (EVA) as a bidding entity can aggregate numerous electric vehicle (EV) resources to participate in the electricity market. Moreover, as the number of grid-connected EVs increases, EVA will have an impact on the nodal marginal prices of electricity market clearing. Aiming at the bidding and offering problem of EVA participation in the day-ahead and intra-day electricity markets, based on the Stackelberg game theory, this paper establishes a bilevel optimization model for EVA participation in the two-stage electricity market as a price-maker. In the proposed bilevel model, the upper-level and lower-level models are constructed as an operational problem for EVA and a market-clearing problem for independent system operator (ISO), respectively. In the day-ahead stage, EVA is optimized to maximize its own expected benefits, and ISO aims to improve the social benefits. In the intra-day stage, EVA is optimized to maximize its self-interest, and the ISO aims to make it possible to minimize the cost of expenditures to maintain the system's supply–demand balance. Karush–Kuhn–Tucker (KKT) conditions and dual theory are used to transform the nonlinear bilevel programming model into a mixed-integer single-level linear programming model. In order to verify the validity of the proposed bilevel model as well as to comparatively analyze the impact of EVA's participation in the electricity market on the market clearing results. Two scenarios are set up where EVA is seen as the price-taker in Scenario 1 and EVA is seen as the price-maker in Scenario 2. ISO's revenue under Scenario 2 increased by USD 2262.66 compared to Scenario 1. In addition, the EVA acts as an energy consumer in Scenario 1 with a charging cost of USD 26,432.95, whereas in Scenario 2, the EVA can profit by participating in the electricity market with a revenue of USD 26,432.95, at which point the EVA acts like a virtual power plant. The simulation examples verify that the proposed bilevel optimization model can improve the benefits of ISO and EVA at the same time, achieving mutual benefits for both parties. In addition, the simulation analyzes the impact of abandonment penalty price on ISO and EVA intra-day revenues. Comparing the scenarios where the abandonment penalty price is 0 with USD 10/MW, the ISO's revenue in the intra-day market decreases by USD 197.5. Correspondingly, EVA's reserve capacity is dispatched to consume wind power in the intra-day market, and its revenue increases by USD 197.5. The proposed two-stage bilevel optimization model can provide a reference for EVA to develop scheduling strategies in the day-ahead and intra-day electricity markets. [ABSTRACT FROM AUTHOR]

Accurately evaluating the response capability of electric vehicles (EVs) is very important for an EV aggregator (EVA) to formulate reasonable bidding strategies in ancillary service markets. Most of the methods proposed in the existing literature only evaluate EVs' temporal response capability while ignoring their spatial distribution. In addition, the evaluation results provided by the existing methods are typically deterministic, which fails to characterize the uncertainty of EV trip. The above two issues pose high risk of economic loss for the EVA. To this end, a probabilistic evaluation method of spatial‐temporal response capability for EVA is proposed in this paper. The gravity model is adopted to calculate a spatial transfer probability matrix describing EV owners' trip characteristics between different areas at each time in a region which is divided into several different areas according to the evaluation requirements. Then, the trip chains of EVs are modelled based on the spatial transfer probability matrix, and the states of charge (SOCs) of EVs are tracked in the process. The spatial‐temporal response capability of EVA is evaluated based on charging–discharging states and states of charge of EVs, and the probabilistic evaluation results of response capability are obtained by multiple Monte Carlo simulations. The effectiveness of the proposed method is verified on a real dataset from San Francisco, CA, USA. [ABSTRACT FROM AUTHOR]

In allusion to the issues of high loss of lifetime and neglecting scheduling potential, a control scheme to extend BESS lifetime for assisting wind farm to track power generation plan based on wind power feature extraction through improved whale optimization algorithm (IWOA) optimized swing door trending (IWOA‐SDT) is proposed. Firstly, an IWOA is designed to optimize the compression offset of the swing door trending algorithm. The preliminary wind power feature is extracted by IWOA‐SDT and is further corrected with actual output of wind power to acquire the corrected wind power feature. Secondly, BESS is divided into two groups and the second battery group is subdivided into three battery clusters, and their capacities are configured by the deviation between preliminary feature and forecasted power. The grouping state of battery units is dynamically adjusted based on devised dynamic grouping method at the corresponding time of corrected wind power feature. The power coordinative allocation approach of the two battery groups is designed. A power coordinative allocation strategy for battery groups is devised to obtain the power regulation signals of two battery groups. The power allocation in battery units of the first group is calculated by the number of the units, and the double‐layer power allocation in battery units of the second group coordinated with multiple rules is devised. Finally, the proposed control scheme is implemented in a wind farm to track the power generation plan, and the results show that the wind farm with BESS can accurately track the power generation plan, the service life of BESS is significantly extended and the scheduling potential is sufficient for the next period. [ABSTRACT FROM AUTHOR]

This study is for the case where the available data of power transformer oil–paper insulation is limited to a small amount furfural data, to solve the problems in oil–paper insulation degradation modelling, such as few samples available, unknown function form of the degradation process, differences of individual transformers among degradation processes, and commonality of degradation trends. A power transformer oil–paper insulation degradation modelling and prediction method based on functional principal component analysis (FPCA) is proposed. First, discrete furfural data of oil–paper insulation degradation are converted into continuous functional data, and the common degradation information of transformers is extracted based on functional time warping technology. Second, the principal components of insulation degradation are extracted based on FPCA method, and the difference of degradation information of individual transformers is obtained by analysing the differential of principal component scores. Subsequently, power transformer oil–paper insulation degradation model is constructed, and finally, the degradation model is updated based on Bayesian theory and the oil–paper insulation degradation is predicted. The example results show that compared with traditional transformer oil–paper insulation degradation modelling method, the proposed method has obvious superiority in model accuracy. [ABSTRACT FROM AUTHOR]

With the wide deployment of charging piles and the development of vehicle-to-grid technology, electric vehicles (EVs) will have more opportunities to participate in the operation and scheduling of electric power system through the EV aggregator (EVA), an intermediate agent between the power grid and EV users. Quantitatively evaluating the response capability (RC) of EVA, i.e., charging and discharging power ranges, is of fundamental importance for its trading with the operator of power grid. This article proposes a user-aware method to obtain a more accurate range of multitimescale RC considering the response willingness of EV users. First, a temporal RC evaluation model of a single EV considering charge-discharge state and state of charge (SOC) is established. Second, based on the user psychology model, which reflects the relationship between users' responsivity and incentive price, a multitimescale RC evaluation model of EVA considering users' willingness is built. The day-ahead RC of EVA is evaluated by the state prediction data of EVs. According to the control strategy which considers response time and SOC indicators comprehensively, the RC evaluation is updated intraday. Finally, using the statistical data from the EU MERGE project, the effectiveness of the proposed evaluation model is verified, and the impact of incentive price and scheduling time scale on the RC of EVA are analyzed. The results indicate that the proposed model can effectively track the scheduling goals of the system and realize the dynamic update of the RC of EVA. [ABSTRACT FROM AUTHOR]

Distributed energy resources especially wind and photovoltaic power, and demand response are highly valued in recent years for their advantages in environmental protection, sustainable development, and so on. However, their volatility poses double risks to the DER aggregator when formulating a profitable bidding strategy and schedule scheme. To this end, first, this article proposes an information gap decision theory-based optimal bidding strategy to model the dual uncertainties confronted by the DER aggregator without knowing the specific distribution pattern of uncertainties. Second, the DER aggregator is assumed to be risk-averse or opportunity-seeking, and the corresponding strategies could be obtained. The former comes up with a robust strategy under severe uncertain circumstances, and the latter presents a profit-maximization scheme while enduring more risks. The validity of the proposed method is examined using the dataset from the Thames valley vision project; the obtained results demonstrate that proper adjustment on aggregator's bidding strategy could be achieved based on its preference for high-profit or stability, which is also applicable for other market entities. [ABSTRACT FROM AUTHOR]

This article proposes a meta-heuristic optimization-based two-stage residential load pattern clustering (LPC) approach to address two main issues that exist in the most current LPC methods: 1) unreasonable typical load pattern (TLP) extraction; 2) a good clustering should achieve a good balance between the compactness and separation of the formed clusters. However, few clustering algorithms integrate both of these two aspects into the objective function of clustering for consideration. In the first stage, an adaptive density-based spatial clustering of applications with noise (DBSCAN) is proposed to automatically detect the uncommon load curves and obtain the TLP of each individual customer. In the second stage, LPC is formulated as an optimization problem in which clustering validity index (CVI) considering both compactness and separation is used as the objective function. Gravitational search algorithm (GSA) is adopted to solve this optimization problem. Four different CVIs are investigated to find the most appropriate one for LPC. A comparative case study using the real load data from 208 households from the U.K. verified the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Household characteristics play an important role in helping utilities carry out efficient and personalized services. Current methods to obtain such information, e.g., surveys, are usually costly and time-consuming. The widespread installation of smart meters enables the collection of fine-grained residential electricity consumption data and thus, making the identification of household characteristics from smart meter data possible. This article proposes a time–frequency feature combination based household characteristic identification approach using smart meter data. First, in addition to conventional time-domain statistical features, several frequency-domain features are extracted using discrete wavelet transform. Second, the random forest algorithm is used to select a subset of important features and remove redundant information contained in the original feature set. Third, a support vector machine is used as a classifier with the input of the selected features to infer the household characteristics. Finally, case study using the realistic data from Ireland indicates that the proposed approach shows better performance after incorporating the frequency-domain features. [ABSTRACT FROM AUTHOR]

In a typical electricity market, the load aggregator (LA) bids in the wholesale market to purchase electricity and meet the expected demand of its customers in the retail market. However, considering the uncertainty of the wholesale market prices, the LA has to undertake all the risks arising from the price volatility in the wholesale market, which may make the LA suffer from financial loss under some scenarios such as price spikes. To this end, first, this article proposes an optimal bidding strategy model for the LA that implements the demand response program (DRP), which enables the LA to reduce the risk of financial loss caused by price volatility. The bidding model is a mixed integer linear programming problem, which can be solved efficiently via a commercial solver. Second, making a rational and quantitative compensation mechanism is significant for the LA to induce its customers to participate in the DRP while there are few studies investigating it, hence, this article designs a quantitative compensation mechanism for the LA. Case studies using a dataset from the Thames valley vision verify the effectiveness of the proposed bidding model. The results confirm that the implementation of DRP not only brings great profits to LA but also benefits the other entities in the electricity market. [ABSTRACT FROM AUTHOR]

This article presents a new decision‐making framework for strategic trading of demand response aggregator (DRA). The proposed framework consists of three layers. At the first layer, the wholesale energy market operated by independent system operator (ISO), which seeks to maximise the social welfare in day‐ahead (DA) market and to minimise the cost of minimise the cost for maintaining energy balance in real‐time (RT) markets. At the second layer, the DRA act as an intermediate operator between ISO and flexible customers. Profit‐maximising DRA affects the clearing results of market prices by strategic trading in DA energy market, providing reserve capacity in DA reserve market and offsetting energy deviation in RT. At the third layer, customers seek to optimise the trading strategy between earnings incentive reward from the DRA and considering uncomfortable cost of customers. The tri‐layer framework is modelled as two bilevel optimisation models, which capture interaction between different entities. The bilevel model can be transformed to single level linear model through Karush‐Kuhn‐Tucker conditions and dual theory to solve the couple non‐linear problem. The numerical studies using the IEEE 9‐bus and 118‐bus test systems are presented to illustrate the application of the proposed tri‐layer decision‐making framework. [ABSTRACT FROM AUTHOR]

Torque ripple of permanent magnet synchronous motor (PMSM) will result in the distortion of current waveform and the load flexibility of spiral torsion spring (STS) will affect the control performance of driving system. This study focuses on a new control problem of simultaneous suppression of PMSM's torque ripple and flexible load's vibration. First, the Lagrange equation is used to establish the dynamic model of STS to describe its vibration mode. The overall model of STS directly derived by PMSM under stator current vector orientation is built. Then, based on the magnetic co‐energy model of electromagnetic torque of PMSM, the constraint relation of optimal stator harmonic current under the condition of torque ripple minimisation is derived, and the controller based on stator current oriented closed‐loop I/f control to suppress load vibration and torque ripple simultaneously is presented through backstepping control. A wide speed range identification method based on the least‐square algorithm with a forgetting factor is also designed. The simulation and experiment show that the speed identification algorithm can estimate the speed accurately in a wide range. The proposed control scenario improves control performance, smoothes current waveform and inhibits torque ripple and load vibration effectively. [ABSTRACT FROM AUTHOR]

For the defects of speed oscillation, fixed current amplitude and inclined to lose synchronism in open-loop $I\,\text{--}\, f$ control approach, a new stator current vector oriented closed-loop $I\,\text{--}\, f$ control scenario is proposed for surface-mounted permanent magnet synchronous motor (SPMSM) with fully unknown parameters. In a stator current vector oriented coordinate system $d^{{\ast }}q^{{\ast }}o$ , the dynamic model of SPMSM is established. Based on analysis of control performance for conventional open-loop $I\,\text{--}\, f$ control, the control framework of closed-loop $I\,\text{--}\, f$ control is presented and closed-loop regulators of angle, speed, and current are derived. Additionally, the adaptation law estimating totally unknown parameters of SPMSM is simultaneously accomplished in the design of controller and avoids construction of estimation algorithm separately. The stability of the controller is proved in the sense of Lyapunov theory. The validity of the proposed algorithm is verified through both simulations and hardware experiments. The results indicate that the improved control scheme ensures the speed to track the expected value rapidly and stably in different load conditions, and the current amplitude is regulated to enlarge with the increasing of load torque automatically. Furthermore, fully unknown parameters are estimated precisely and reverse rotation of motor at startup enables to be prevented effectively. [ABSTRACT FROM AUTHOR]

A nonlinear and robust adaptive backstepping based maximum torque per ampere speed sensorless control scheme with fully uncertain parameters is proposed for a permanent magnet-assisted synchronous reluctance motor. In the design of the controller, the relation to d-q-axis currents constrained by maximum torque per ampere control is firstly derived. Then, a fully adaptive backstepping control method is employed to design control scenario and the stability of the proposed control scenario is proven through a proper Lyapunov function candidate. The derived controller guarantees tracking the reference signals of change asymptotically and has good robustness against the uncertainties of motor parameters and the perturbation of load torque. Moreover, in allusion to the strong nonlinearity of permanent magnet-assisted synchronous reluctance motor, an active flux based improved reduced-order Luenberger speed observer is presented to estimate the speed. Digital simulations testify the feasibility and applicability of the presented control scheme. [ABSTRACT FROM AUTHOR]

Accurate solar photovoltaic (PV) power forecasting is an essential tool for mitigating the negative effects caused by the uncertainty of PV output power in systems with high penetration levels of solar PV generation. Weather classification based modeling is an effective way to increase the accuracy of day-ahead short-term (DAST) solar PV power forecasting because PV output power is strongly dependent on the specific weather conditions in a given time period. However, the accuracy of daily weather classification relies on both the applied classifiers and the training data. This paper aims to reveal how these two factors impact the classification performance and to delineate the relation between classification accuracy and sample dataset scale. Two commonly used classification methods, K-nearest neighbors (KNN) and support vector machines (SVM) are applied to classify the daily local weather types for DAST solar PV power forecasting using the operation data from a grid-connected PV plant in Hohhot, Inner Mongolia, China. We assessed the performance of SVM and KNN approaches, and then investigated the influences of sample scale, the number of categories, and the data distribution in different categories on the daily weather classification results. The simulation results illustrate that SVM performs well with small sample scale, while KNN is more sensitive to the length of the training dataset and can achieve higher accuracy than SVM with sufficient samples. [ABSTRACT FROM AUTHOR]

The chaotic behavior of permanent magnet synchronous motor is directly related to the parameters of chaotic system. The parameters of permanent magnet synchronous motor chaotic system are frequently unknown. Hence, chaotic control of permanent magnet synchronous motor with unknown parameters is of great significance. In order to make the subject more general and feasible, an adaptive robust backstepping control algorithm is proposed to address the issues of fully unknown parameters estimation and external disturbances inhibition on the basis of associating backstepping control with adaptive control. Firstly, the mathematical model of permanent magnet synchronous motor chaotic system with fully unknown parameters is constructed, and the external disturbances are introduced into the model. Secondly, an adaptive robust backstepping control technology is employed to design controller. In contrast with traditional backstepping control, the proposed controller is more concise in structure and avoids many restricted problems. The stability of the control approach is proved by Lyapunov stability theory. Finally, the effectiveness and correctness of the presented algorithm are verified through multiple simulation experiments, and the results show that the proposed scheme enables making permanent magnet synchronous motor operate away from chaotic state rapidly and ensures the tracking errors to converge to a small neighborhood within the origin rapidly under the full parameters uncertainties and external disturbances. [ABSTRACT FROM AUTHOR]

A novel control strategy based on the optimization of transfer trajectory at operation points for DFIG is proposed. Aim of this control strategy is to reduce the mechanical fatigue of DFIG caused by the frequent adjustment of rotating speed and pitch angle when operating in the islanded power system. Firstly, the stability of DFIG at different operation points is analyzed. Then an optimization model of transfer trajectory at operation points is established, with the minimum synthetic adjustment amount of rotating speed and pitch angle as the objective function and with the balance of active power and the stability of operation points as the constraint conditions. Secondly, the wind speed estimator is designed, and the control strategy of pitch system is improved to cooperate with the indirect stator flux orientation control technology for rotor-side inverter control. Then by the coordination control of its rotating speed and pitch angle, an operation trajectory controller is established to ensure the islanded operation of DFIG along the optimal transfer trajectory. Finally, the simulation results show that the proposed control strategy is technical feasibility with good performance. [ABSTRACT FROM AUTHOR]

The spiral torsion spring‐based mechanical elastic energy storage (MEES) device presented previously with inherent characteristic of simultaneous variations of inertia and torque is disadvantage to be actuated by conventional control method. This study proposes an improved non‐linear backstepping control scheme based on combination of recursive least squares (RLS) and differential evolution (DE) optimisation to regulate permanent magnet synchronous motor (PMSM) at a very low speed and control MEES device effectively. For this presented control scheme, first, a single RLS method with forgetting factor is presented to simultaneously estimate the time‐varying inertia and torque of MEES device. Second, on the basis of estimation results, an improved non‐linear backstepping control algorithm considering parameter variations in derivation process is proposed to drive PMSM to operate at a very low speed. Finally, a self‐adaptive parameter DE method is designed to optimise the several control parameters. The performance of the proposed methodology is verified through simulations and experiments. [ABSTRACT FROM AUTHOR]

Biogeography based optimization (BBO) is a new competitive population-based algorithm inspired by biogeography. It simulates the migration of species in nature to share information. A new hybrid BBO (HBBO) is presented in the paper for constrained optimization. By combining differential evolution (DE) mutation operator with simulated binary crosser (SBX) of genetic algorithms (GAs) reasonably, a new mutation operator is proposed to generate promising solution instead of the random mutation in basic BBO. In addition, DE mutation is still integrated to update one half of population to further lead the evolution towards the global optimum and the chaotic search is introduced to improve the diversity of population. HBBO is tested on twelve benchmark functions and four engineering optimization problems. Experimental results demonstrate that HBBO is effective and efficient for constrained optimization and in contrast with other state-of-the-art evolutionary algorithms (EAs), the performance of HBBO is better, or at least comparable in terms of the quality of the final solutions and computational cost. Furthermore, the influence of the maximum mutation rate is also investigated. [ABSTRACT FROM AUTHOR]

In response to air pollution problems caused by carbon emissions, electric vehicles are widely promoted in China. Since thermal power generation is the main form of power generation, the large-scale development of electric vehicles is equivalent to replacing oil with coal, which will accordingly result in carbon emissions increasing if the scale of electric vehicles exceeds a certain limit. A relationship model between regional energy mix structure and electric vehicles holdings under the constraint of carbon emission reduction is established to perform a quantitative analysis of the limitation mechanism. In order to measure the scale of the future electric vehicle market under the constraint of carbon emissions reduction, a method called Extreme Learning Machine optimized by Improved Particle Swarm Optimization (IPSO-ELM) with higher precision than Extreme Learning Machine (ELM) is proposed to predict the power structure and the trend of electric vehicle development in the Beijing-Tianjin-Hebei region from 2019–2030. The calculation results show that the maximum number of electric vehicles must not exceed 19,340,000 and 26,867,171 based on emissions reduction aims and also the predicted energy mix structure in the Beijing-Tianjin-Hebei region in 2020 and 2030. At this time, the ratio of electric vehicles to traditional car ownership is 75.6% and 78.3%. The proportion of clean energy generation should reach 0.314 and 0.323 to match a complete replacement of traditional fuel vehicles for electric vehicles. A substantial increase in clean energy generation is needed so that the large-scale promotion of electric vehicles can still achieve the goal of carbon reduction. Therefore, this article will be helpful for policy-making on electric vehicle development scale and energy mix structure in the Beijing-Tianjin-Hebei region. [ABSTRACT FROM AUTHOR]

An active distribution network (ADN) differs from a traditional distribution network in many aspects, one of which is the integration of a large amount of distributed generation (DG), especially intermittent photovoltaics (PVs). The integration of intermittent PVs has both pros and cons for the distribution system. As the platform on which new techniques work and the main body of a greener future energy system, the development of an ADN has to be sustainable, need-oriented, and environmentally friendly, and the traditional technical–economic evaluation method cannot meet the requirements and provide advice in the decision-making process. Based on the concept of sustainable development, we used an ADN with the integration of a large number of distributed PVs (DGPVs) as an example and established a multi-dimensional index system to evaluate the sustainable development level (SDI) of the ADN. The analysis was based on a platform we built with consideration of the investment feasibility of the DGPVs' investors, state and industrial policies, and their interactions with the distribution system. We first compared the development of DGPVs and the SDI of the ADN as the carrier of DGPVs under different state policies, and second, we compared the SDIs of three city ADNs with different solar resources and demand levels, but under the same state policy. The analysis results showed that different integration levels of DGPVs can be set for a city/area ADN with different solar resources and demand to achieve a comparable SDI, and a comprehensive incentive mechanism could be adopted for the development of DGPVs. In this way, the benefits of different parties can be considered at the same time and finally, the coordination of the sustainable development of multi-parties can be achieved. [ABSTRACT FROM AUTHOR]

Most distributed photovoltaic systems (DPVSs) are normally located behind the meter and are thus invisible to utilities and retailers. The accurate information of the DPVS capacity is very helpful in many aspects. Unfortunately, the capacity information obtained by the existing methods is usually inaccurate due to various reasons, e.g., the existence of unauthorized installations. A two-stage DPVS capacity estimation approach based on support vector machine with customer net load curve features is proposed in this paper. First, several features describing the discrepancy of net load curves between customers with DPVSs and those without are extracted based on the weather status driven characteristic of DPVS output power. A one-class support vector classification (SVC) based DPVS detection (DPVSD) model with the input features extracted above is then established to determine whether a customer has a DPVS or not. Second, a bootstrap-support vector regression (SVR) based DPVS capacity estimation (DPVSCE) model with the input features describing the difference of daily total PV power generation between DPVSs with different capacities is proposed to further estimate the specific capacity of the detected DPVS. A case study using a realistic dataset consisting of 183 residential customers in Austin (TX, U.S.A.) verifies the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Sharing the benefits of distribution systems from the integration of distributed photovoltaic systems (DGPVs) with investors is vital to the coordinated development of DGPVs and active distribution systems (ADN). The investment and development of DGPVs, incentive policies, and the development of distribution system interact, and the interactions vary with the changes in the on-grid capacity of DGPVs. In this paper, an event-driven co-simulation platform is built to simulate the abovementioned interaction among DGPVs, ADN, and incentive policy under a long-term time frame. The platform includes an investment model of DGPV investors and an ADN model with consideration of the growth of the ADN. On this platform, we study how multiple factors, including incentive system, global horizontal radiance (GHR), and cost, affect the investment and integration of DGPVs in the future 10 years. Simulation and analysis showed that investors' decisions are more sensitive to variation in GHR and cost, followed by variation in tariff system, subsidy, and self-use ratio. Distribution subsidies have certain impact on the development of DGPV and could partially replace the national and provincial capacity and generation subsidies. When the on-grid capacity reaches a certain level, the distribution subsidy reaches a dynamic equilibrium. [ABSTRACT FROM AUTHOR]

With continuous technological improvement and economic development of energy storage, distributed energy storage (DES) will be widely connected to the distribution network. If fragmented DES systems are aggregated to form a distributed energy storage aggregator (DESA), the DESA will have great potential to participate in the day-ahead energy and reserve market and the balancing market. The DESA could act as a mediator between the market and DES consumers, enabling beneficial coordination for DES owners and power systems. This paper presents a bilevel optimization model for DESAs in the energy and reserve market under wind power uncertainties. In the lower-level problem, generating companies, wind power plants (WPP), and DESAs are optimized for scheduling day-ahead (DA) energy and the reserve market. In the upper-level problem, operational strategies for DES systems and DESAs are designed to deal with wind power uncertainties in the balancing market. The DESA splits its resources between the energy and reserve markets so that it can reduce total power system consumption, and mutual profit for the system and end customers is achieved. This model is formulated as a mixed-integer linear programming (MILP) program, which can be solved with commercial software. The validity of the bilevel optimization model is verified by the eight-node test transmission system and IEEE-33 bus distribution system. [ABSTRACT FROM AUTHOR]