• Energy Research
• 2021-2025close
• 7. Clean energy close
• 13. Climate action close
• 12. Responsible consumption close
• Southeast University close

The promotion of electric vehicles (EVs) is an important measure for dealing with climate change and reducing carbon emissions, which are widely agreed goals worldwide. Being an important operating mode for electric vehicle charging stations in the future, the integrated photovoltaic and energy storage charging station (PES-CS) is receiving a fair amount of attention and discussion. However, how to optimally configure photovoltaic and energy storage capacity to achieve the best economy is essential and a huge challenge to overcome. In this paper, based on the historical data-driven search algorithm, the photovoltaic and energy storage capacity allocation method for PES-CS is proposed, which determines the capacity ratio of photovoltaic and energy storage by analyzing the actual operation data, which is performed while considering the target of maximizing economic benefits. In order to achieve the proposed capacity allocation, the method is as follows: First, the economic benefit model of the charging stations is established, taking the net present value and investment payback period as evaluation indicators; then, by analyzing the operation data of the existing charging station with the target of maximizing economic benefits, the initial configuration capacity is obtained; finally, the capacity configuration is verified through a comprehensive case analysis for the actual operation data. The results show that the capacity configuration obtained through the data analysis features an optimized economic efficiency and photovoltaic utilization. The proposed method can provide a theoretical and practical basis for newly planned or improved large-scale charging stations.

Since the official launch of the Belt and Road Initiative (BRI) in 2013, China and the BRI countries have been working for the implementation of certain environmental measures to make the BRI project green and clean. For this purpose, China and the BRI countries have planned to implement certain environmental measures. Although China can efficiently implement these measures, most of the BRI countries face technological deficiencies and lack of proper environmental plannings. To tackle these deficiencies, the BRI countries can import environmental technology from China. Moreover, they can communicate their environmental protection policies with China for better policy guidance. The current study, therefore, aims to examine whether the BRI countries’ import of environmental technology from China can reduce carbon emissions in these countries. Moreover, it also examines that whether these countries should follow the environmental policy and the import policy of China or they should follow the six European countries (EU-6) with minimum carbon emissions intensity. This study considers a sample of 88 selected BRI countries (BRI-88) for the period 2001–2019. The results obtained with β convergence (based on the panel quantile regression model) suggest that not all the BRI countries but only the BRI countries with relatively higher carbon emissions intensity can significantly reduce their average carbon emissions intensity by importing environmental technology from China. Moreover, BRI countries can follow the environmental policy of China which is more feasible for them. However, regarding the environmental goods import policy, BRI countries can follow both China and the EU-6.

More and more renewable energy sources are integrated into power grids, leading to a power electronic-based low-inertia power system. The grid-forming (GFM) inverter is an effective method for improving the inertia of the system. However, with the increased GFM inverters in the system, how the multiple control parameters affect the frequency response is still not clear. In this study, first, the power-phase model of the power grid is established; then, a small-signal distributed frequency model of the GFM inverter-based power system is established associating with the power-phase model of the power grid and the power-frequency model of the GFM inverter. Based on the proposed model, the influence of the multiple parameters to the frequency response is analyzed. It is concluded that both the inertia and damping coefficient affect the settling time, overshoot, and oscillation of the frequency. Finally, the simulation results verify the proposed model and the conclusion.

With the roll-out of smart meters and the increasing prevalence of distributed energy resources (DERs) at the residential level, end-users rely on home energy management systems (HEMSs) that can harness real-time data and employ artificial intelligence techniques to optimally manage the operation of different DERs, which are targeted toward minimizing the end-user’s energy bill. In this respect, the performance of the conventional model-based demand response (DR) management approach may deteriorate due to the inaccuracy of the employed DER operating models and the probabilistic modeling of uncertain parameters. To overcome the above drawbacks, this paper develops a novel real-time DR management strategy for a residential household based on the twin delayed deep deterministic policy gradient (TD3) learning approach. This approach is model-free, and thus does not rely on knowledge of the distribution of uncertainties or the operating models and parameters of the DERs. It also enables learning of neural-network-based and fine-grained DR management policies in a multi-dimensional action space by exploiting high-dimensional sensory data that encapsulate the uncertainties associated with the renewable generation, appliances’ operating states, utility prices, and outdoor temperature. The proposed method is applied to the energy management problem for a household with a portfolio of the most prominent types of DERs. Case studies involving a real-world scenario are used to validate the superior performance of the proposed method in reducing the household’s energy costs while coping with the multi-source uncertainties through comprehensive comparisons with the state-of-the-art deep reinforcement learning (DRL) methods.

In light of the increasing number of electric vehicles (EV), disorderly charging in mountainous cities has implications for the stability and efficient utilization of the power grid. It is a roadblock to lowering carbon emissions. EV aggregators are a bridge between EV users and the grid, a platform to achieve energy and information interoperability, and a study of the orderly charging of EVs to reach carbon emission targets. As for the objective function, the EV aggregator considers the probability of EV charging access in mountainous cities, the SOC expectation of EV users, the transformer capacity constraint, the charging start time, and other constraints to maximize revenue. Considering the access probability of charging for users in mountainous cities, the optimized Lagrange relaxation method is used to solve the objective function. The disorderly charging, centralized optimized charging, and decentralized optimized charging modes are investigated using simulation calculations. Their load profiles, economic benefits, and computational efficiency are compared in three ways. Decentralized optimal charging using the Lagrange relaxation method is shown to be 50% more effective and to converge 279% faster than centralized optimal charging.

Along with the operating velocity and traction power increasing continuously, the issue related to the safety and reliability of the high-voltage traction power system for high-speed trains have gradually been exposed during the practical service process. When the train passes a neutral section settled roughly per 15 km between two neighboring substations, the on-board vacuum circuit breaker (VCB) must to be operated for changing the input powers with different phases. At the moment of switching on or off the VCB, the arc between the contacts in the arc-extinguishing chamber may be triggered, which tends to result in the operational overvoltage. Due to numerous inductances and capacitances existing in the traction power supply system, the overvoltage may lead to high-frequency electromagnetic oscillation spreading along the transmission routes. The on-board high-voltage equipments within the power supply system may suffer from the impulse brought from the VCB operational overvoltage frequently, which is possible to cause the insulation aging or breakdown issue. To investigate the characteristic of the operational overvoltage of VCBs, a ‘train-rail-catenary’ power supply model is built based on the measured impedance parameters, which has been verified by the experimental tests. The generating mechanism with the influence factors of the operational overvoltage when switching on or off the VCB has been explored, meanwhile the distribution of the train body overvoltage on each carriage has also been presented.

An incentive-compatible demand response (DR) strategy is proposed for engaging spatially-coupled Internet data center (IDC) and their spatial load regulation potentials in electricity markets. First, an optimal power flow (OPF) model which considers IDC (referred to as IOPF) is proposed to coordinate IDC DRs and power system operations, in which the formulated IDC load model is compatible with the conventional OPF model. Second, the IOPF-based locational marginal price (LMP) (referred to as ILMP) is derived, which is used to analyze the impact of spatially-coupled DR options on ILMPs and the IDC DR’s clearing price. Third, IDC DR activation strategy is proposed as an extension of Net Benefit Test (NBT), where the risk of negative benefit to IDCs is derived. An IDC DR’s activation strategy is proposed based on NBT to determine whether non-zero IDC DR dispatches in IOPF are cost-effective. Last, an extra benefit redistribution mechanism is proposed to achieve the incentive-compatibility between social welfare and IDC benefit. The proposed approach is based on the Vickrey–Clarke–Groves mechanism and the contribution factor theory, where the market’s revenue adequacy is maintained, and benefits to IDCs and other customers are guaranteed. Simulation results verify the efficiency of the proposed method, implying that the proposed spatially-coupled DRs are compatible with and can enhance existing DR mechanisms.

Abstract Distributed optimization algorithms contribute to an insight for the optimal coordination of distributed energy resources (DERs). To tackle the DER coordination problem, this paper studies a composite constrained optimization problem over multi-agent networks, where all agents independently maintain convex objective functions while satisfying the local and coupled constraints. Within the Peaceman-Rachford splitting (PRS) framework, a distributed algorithm based on the relaxed ADMM method is proposed for the considered problem, where agents in the network commonly share a fixed step-size instead of the diminishing one. Finally, the simulations on the optimal coordination of DERs including distributed generators (DGs) and energy storages (ESs) are conducted to validate the effectiveness of the proposed algorithm.

Interest in electricity-gas demand response (EGDR) programs has been growing, especially with respect to Energy Hub (EH) systems, which are typically large industrial multi-energy users involving electric, heating and cooling loads. A key component in modeling EGDR is the electricity-shifting curve (ESC), which is usually obtained empirically. However, rigorous and systematic approaches to obtaining and understanding the ESC are lacking. In this article, a quantitative modeling approach is proposed based on the aggregated utility curve of multi-energy demands such as electricity, heating and cooling loads. First, an ESC based on consumer psychology and behavior is adopted. Second, utility curves of different loads (electricity, heating and cooling) are aggregated to a single utility curve, which is then combined with the theory of consumer choice to develop the ESC with regard to the price ratio of different energy sources. Third, various factors affecting the shifting curves are identified and then analyzed. Based on the case studies, generalized guidelines for improving the ESC are provided. Additionally, a multi-energy user with a higher heating-to-electricity ratio (HER) is verified as having better performance in obtaining a broader shifting area, and a multi-energy user with lower HER has a larger shifting amount in common range of price ratio. In summary, this work presents a quantitative and systematic approach to modeling the ESC in an EH and to understanding EGDR behaviors which can be used to improve EH response flexibility.