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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.

Accurate monthly electricity consumption forecasting (ECF) can help retailers enhance the profitability in deregulated electricity markets. Most current methods use monthly load data to perform monthly ECF, which usually produces large errors due to insufficient training samples. A few methods try to use fine-grained smart-meter data (e.g., hourly data) to increase training samples. However, such methods still exhibit low accuracy due to the increase in forecasting steps.

To store energy from the grid into spiral torsion spring (STS) smoothly and efficiently via PMSM, a multi-objective control problem of flexible load's vibration, PMSM's torque ripple, and electrical loss is raised, where the current studies on vibration and torque ripple are mostly addressed separately, not to mention electrical loss. This research attempts to propose a multi-objective integrative control scenario that can simultaneously solve these problems satisfactorily in a unitary nonlinear control framework. Firstly, a dynamic mathematical model of PMSM is built under stator current vector orientation, and then the model of PMSM is combined with the vibration model of STS to establish the overall system model of STS driven by PMSM with considering motor's electrical loss. Then, a backstepping control principle-based multi-objective integrative control approach is proposed to realize the suppression of flexible load's vibration and the reduction of PMSM's torque ripple and electrical loss concurrently. Meanwhile, this research also designs a wide range speed identification method based on the least square algorithm with a forgetting factor. Simulation and experimental results have verified that the proposed integrative control method enables the state variables to track their respective references quickly and accurately, both torque ripple and load vibration are effectively suppressed, and the operating efficiency of the whole system is improved.

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.

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.

Due to the development of intelligent electric devices and advanced metering infrastructures, demand response will be widely utilized in the trading of the electricity market and maintain energy balance of the power system. In this paper, a two-stage nested bilevel model for the optimal bidding strategy of a load agent (LA) with incentive-based demand response in day-ahead and balancing markets is proposed. In the upper-level model, the optimal trading strategy of the LA is formulated to maximize the operating profit of the LA in the day-ahead energy and balancing markets. On the other hand, the lower-level proposes the clearing market model of the independent system operator, which aim to maximize social welfare. The LA acts as a price-maker in the first-stage of the bilevel model, which is bilevel nonlinear programming (BNLP) problem. Karush-Kuhn-Tucker conditions and dual theory are used to transform the BNLP into single-level programming. The LA acts as a price-taker accepts the day-ahead energy clearing-price in the second-stage of the bilevel model, which is a bilevel mixed-integer linear stochastic programming problem. Finally, implementing the two-stage nested bilevel model on modifying the 8-bus power system demonstrates the applicability of the proposed model and analysis the sensitivity of the LA' profit to unit price and the committed reserve capacity demand of the day-ahead reserve market.

Home energy management systems (HEMSs) enable residential customers to efficiently participate in demand response programs in order to obtain optimal benefits. Traditional HEMSs only manage household electric appliances to reduce the electricity consumption cost while the optimal scheduling of natural gas appliances has been overlooked. Due to the increasing popularity of natural gas appliances in modern smart homes, the electricity consumption of residential customers connected to the natural gas network is significantly affected by the use of natural gas appliances. To consider the interaction between electric and natural gas appliances in households, a day-ahead optimal joint scheduling model of electric and natural gas appliances for HEMS is proposed. Firstly, all household appliances are classified into several categories and the mathematical model of each appliance is presented. Then, a day-ahead optimal joint scheduling model of both electric and natural gas appliances for HEMS is formulated, in which the objective function is to minimize the household's energy cost and the dissatisfaction level caused by the shifting, reduction and replacement of loads in response to the time varying prices. Case studies using realistic data indicate that the proposed model can save the total energy costs up to 30% for customers whilst ensuring their satisfaction levels.

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.

With the development of smart grid and active distribution network, the flexible load recourse would play a key role in the electricity market. In this paper, we proposed a framework that the distributed storage energy systems, electric vehicles, and temperature control loads are aggregated in the flexible load aggregator, trading in day-ahead energy and reserve markets. The framework is modeled as a bilevel optimization model. In the propose model, the operation problem of the FLA is modeled in upper-level problem, which is to maximize the profit of the aggregator. The biding and offering strategic of Gencos and flexible load aggregator in the independent system operator are presented in lower-level problem, which aim at improving the social benefits. Karush–Kuhn–Tucker and dual theory are used to transform the nonlinear bilevel problem to a mixed-integer linear programming of single-level model. Finally, the numerical studies based on modifying PJM-5bus power system, showing the effectiveness of the proposed framework and bilevel model.

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.