• Energy Research

Electric spring (ES) is a relatively new power electronics device for voltage regulation in a distribution system. Some existing ES control methods are system-independent; however, their performances generally can be significantly improved when accurate system models are properly utilized. Unfortunately, accurate model of the concerned distribution system is not always available in practice. This paper proposes a hybrid method for ES control, based on the integration of data-driven and analytical models. The inaccurate analytical model provides a basic policy to generate system data. Three data-driven models based on the extreme learning machine (ELM) are built using these data to replace the power flow, active power, and reactive power models respectively. An ELM-based control model is proposed to generate the final control strategies. The hybrid method is tested using a 15-node distribution network. Simulation results show that the data-driven model is more accurate than the analytical model in predicting system states, while the proposed control method outperforms the original analytical method.

Traditional experimental economics methods often consume enormous resources of qualified human participants, and the inconsistence of a participant's decisions among repeated trials prevents investigation from sensitivity analyses. The problem can be solved if computer agents are capable of generating similar behaviors as the given participants in experiments. An experimental economics based analysis method is presented to extract deep information from questionnaire data and emulate any number of participants. Taking the customers' willingness to purchase electric vehicles (EVs) as an example, multi-layer correlation information is extracted from a limited number of questionnaires. Multi-agents mimicking the inquired potential customers are modelled through matching the probabilistic distributions of their willingness embedded in the questionnaires. The authenticity of both the model and the algorithm is validated by comparing the agent-based Monte Carlo simulation results with the questionnaire-based deduction results. With the aid of agent models, the effects of minority agents with specific preferences on the results are also discussed.

Nowadays, power system resilience presents higher requirements for restorative self-healing strategies considering both restoring and operating performances. This paper sheds light on a resilience-enhancing restoration strategy considering voltage balance, which is formulated as a two-stage mixed integer linear programming (MILP) problem. An improved non-black-start (NBS) generator start-up model is presented in the first stage, and used to determine the restoration sequence of generators, power lines, and loads with maximum restored load capacity. Besides, a linear power flow model with transmission loss ignored is employed in the second stage, with an aim of attaining most balanced post-restoration voltage by readjusting the power outputs from generators. The IEEE 30-bus power system is used to demonstrate the correctness and effectiveness of the proposed two-stage strategy.

Modern power systems are continuously developing to large and interconnected ones. The power industry restructuring and the reduced investment in transmission system expansion make power systems operate closer and closer to their limits, and hence lead to larger possibility of fault outages than before. Therefore, the protection and control in power systems become more and more important as well as complicated. On the other hand, the continuous technological development in communication and measurement accelerates the occurrence and applications of wide-area protection, a kind of advanced protections based on wide-area measurements. The blackouts happened in North America as well as other countries in the past few years also provide more and more incentive for scientists and engineers in the power system circle to devote to the study on wide-area protection and control systems. In this paper, a comprehensive bibliographical survey is made on recent development in this field, and the survey is done from seven relevant aspects

HVDC transmission has significant advantages in high-power and long-distance transmission, and at the same time brings great challenges to the stability of the interconnected asynchronous power system with HVDC links. Given this background, this paper investigates frequency stability of the sending subsystem in an interconnected asynchronous power system with HVDC links, with special background associated with Yunnan power grid of China. First, the mathematical models of governor systems in hydropower and thermal power generation units are presented, with secondary frequency control and Frequency Limit Control (FLC) considered. Then, the root locus method is used to study the frequency stability of the power system, and coordinated optimization between the secondary frequency control parameters and the FLC parameters addressed. Finally, numerical simulations of some operating scenarios in Yunnan power grid are employed to demonstrate the proposed frequency stability analysis method.

Reasonably dividing restoration subsystems is an essential step for speeding up the restoration procedure with the widely employed bottom-up restoration strategy. A new method based on the community structure of the complex network theory is presented for dividing restoration subsystems in this work. The locations of black-start units and the characteristics of power systems are taken into account in the developed method. The restoration subsystems are determined by removing the lines with the maximal ‘betweenness’ repeatedly, and the rationality of the division results is evaluated by using a modularity index. An interconnection strategy for the restoration subsystems thus obtained is proposed by following the sequence that each restoration subsystem is separated from the power network concerned in the divisive process. The essential features of the proposed method are illustrated by case studies of the well-known New England test system and the Guangzhou actual power system in China.

Based on the dynamic time warping (DTW) matching method, a novel appliance identification algorithm for low frequency sampling load data is proposed. First, residential load sequences are segmented into subsequences composed of single appliance load profiles and multi-appliance load profiles. Then, reference load sequences of all candidate appliances, which have identical lengths, are generated before conducting DTW matching. Next, the reference sequence that has the minimum DTW distance to the query subsequence is assigned as the identification result. For a multi-appliance subsequence, the best-matched reference sequence is subtracted from it after each DTW matching, and this process is repeated until all the appliances are identified. The proposed algorithm is implemented in R language and tested with a load dataset, which contains 1000 residential load profiles which all have a length of 24h and are sampled every minute. The result shows that the proposed algorithm is fast and accurate. It only takes 12.67 minutes to identify 1000 load profiles and could identify single appliance load subsequence at an accuracy of 95%. While for multi-appliance subsequences which is much more difficult to identify, it could also achieve an accuracy of about 75%.

The uncertain output of a photovoltaic (PV) system imposes a problem for the power system concerned to accommodate the PV generation. The energy-stored capability of electric vehicles (EVs) can be utilized to enhance the capability of accommodating PV generation locally. Given this background, an optimal scheduling strategy for EVs and PVs in dynamic time-of-use (TOU) tariff environment is proposed in this paper. The charging/discharging schedules are optimized by EV users/agents with the objective to minimize the charging cost and maximize the discharging profit. Then, the regional operator adjusts the schedule in order to enhance the usage of the PV power output locally. TOU tariffs are updated at set intervals, and can guide charging/discharging schedules of EVs so as to implement valley filling and peak shaving. Finally, a distribution system with 3 residential communities is employed to demonstrate the effectiveness of the proposed method.

Driven by the accelerating deployment of distributed energy resources (DERs), advanced information and communications technologies (ICTs), and demand-side response (DR), modern power systems have been transforming from a one-way energy supply chain to a two-way energy system. The continuous integration of DERs into a low-voltage distribution network brings profound challenges to its secure and economic operation, including technical issues with protection placement and coordination, voltage and power violation, power quality, and energy losses. To overcome these challenges, tremendous endeavours have been devoted to enhancing the accommodation capability for renewables in distribution networks. In addition, DERs, including both generation and storage resources, are gaining increasing attention to support the operation of bulk power grids by providing ancillary services, particularly in frequency regulation. To help researchers and engineers have a better overview of the state-of-the-art on the accommodation of renewables in power distribution networks, this special issue solicits original and novel research on enhancing hosting capability for renewable energy generation in active distribution networks. After undergoing a thorough peer review process, eleven papers were finally accepted on topic areas of operation and planning of distribution networks, modelling of DERs, virtual power plants, electricity market design, and demand-side management. A brief discussion of the authors' contributions is presented as follows.