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Guiding electric vehicles (EVs) in smart energy communities to participate in demand response can significantly benefit users and the load aggregator. However, when the user's travel plans are uncertain, the EV may end up charging before the planned time, leaving the user with an insufficient power supply. This can occur because the control program chooses to charge the EV during periods of low electricity prices to reduce user costs. To address this issue, a new strategy for charging pricing based on service-quality is proposed to help users deal with the uncertainty of travel plans and promote user participation in demand response. In this paper, the proposed charging pricing of EVs consists of three parts. Firstly, the model of charging packages with different service qualities is designed for users. The differences in charging packages are reflected in the percentage of power supply and price within each time slot. Charging packages provide users with at least a certain amount of power in each time slot, allowing them to better cope with the uncertainty of their travel plans. Secondly, a subscription model for charging packages is built to match the user's travel plans. Finally, the pricing model of the charging package is established, representing the master-slave game between the load aggregator and the user. The model is linearized by the KKT condition. The results show that the proposed strategy helps users cope with the uncertainty of their travel plans, reducing charging cost for users by 16.39% while increasing revenue for the load aggregator by 14.8%. • A novel charging package for electric vehicles based on service-quality is proposed. • A pricing model is established as a master-slave game formulation. • The uncertainty of the EV users' travel plan is effectively handled. • The user 's charging cost is reduced by 16.39% in the case. • The revenue of the load aggregator is increased by 14.8% in the case. [ABSTRACT FROM AUTHOR]

With the popularization of electric vehicles (EVs) and the development of home EV charging technology, the safety and economy of electricity distribution network operations are greatly challenged. This paper first establishes a simulation method for EV charging considering the usage habits of EV owners. Subsequently, the EV hosting capacity of the electricity distribution network is defined and determined based on an optimal power flow model, which is integrated with the three-phase power flow equation constraint, and inequality constraints including both rigid and flexible constraints. Furthermore, enhancement strategies based on multiple aspects of the electricity distribution network are also analyzed and proposed. Case tests are performed to verify the feasibility and advantages of the proposed models and strategies. According to the simulation results, with the increasing of EVs, the line carrying capacity and load peak-valley characteristics become the primary factors limiting the hosting capacity of the electricity distribution network for EVs, and the proposed strategies are analyzed for their strengthening effect on different segments of the distribution network. [ABSTRACT FROM AUTHOR]

The increasing penetration of intermittent renewable energy sources leads to a higher demand for flexible balancing service in the power system. Consequently, demand-side management techniques, such as utilizing flexible resources from residential electric-heating loads, provide a promising solution. However, due to various uncertainties such as stochastic weather and market prices, it is challenging to develop an economically efficient strategy. Hence, this paper proposes an economic operation optimization approach for residential electric-heating loads to develop optimal bidding and scheduling strategy in the multi-stage market. First, the dynamic characteristics of aggregated electric-heating loads are modeled as the virtual energy storage systems (VESS) to quantify the flexibility potential arising from the combination of building thermal inertia, user tolerance and thermal energy storage (TES). Subsequently, the joint energy and balancing service market is introduced to exploit the operating flexibility of VESS. A multi-agent deep reinforcement learning (MADRL) optimization framework is proposed to address the multi-objective problem at different decision frequency, which involves achieving lower economic costs while maintaining indoor comfort. Finally, we combine the DRL with imitation learning (IL) by introducing the behavior cloning mechanism to mitigate training process instability. It reduces the computational costs and eliminates the need for redundant additional structure. Comparative simulation experiments demonstrate the efficiency of data-driven optimization approach, which provides an adaptive, economic cost-effective and general solution for multi-type aggregators to activate their flexibility in dynamic markets. • The dynamic flexibility edge of electric-heating loads is quantified as VESS. • The MADRL is introduced to solve the optimization problem in multi-timescale market. • The IL is integrated to DRL to accelerate and smooth training process. • The proposed approach provides a general solution for multi-type LA strategy. [ABSTRACT FROM AUTHOR]

Normalization and weighting are two life cycle impact assessment (LCIA) steps that are often uncertain and subjective. To make them more reliable, this article aims to contribute to the development of an up-to-date normalization reference (NR) and a better set of weighting factors (WF) for China through distance-to-target (DTT) weighting. In the present work, eight environmental criteria were assessed by policy targets, and the NRs of the contribution inventories were calculated. They included global warming (GW), ozone layer depletion (OD), acidification (Ac), eutrophication (Eu), photochemical oxidation (PO), abiotic depletion (fossil fuels) (ADF), freshwater aquatic ecotoxicity (FAET), and human toxicity (HT). Two WF sets with the target year of 2020 and 2030 were obtained; in both sets, GW had the highest relative importance and ADF had the lowest. Comparing the new WF sets with the WF sets of Europe and Thailand revealed the different emphasis of countries/regions with different development levels on environmental control. In addition, 1-kWh electricity generation was used as a case study to verify the differences and effects on the NR and WF results. This policy-based DTT method showed both strengths and weaknesses in the construction of Chinese WF sets. A clear policy based on every five-year plan would facilitate setting targets and simplifying data collation. Further attention needs to be paid to toxicity indicators, including the characterization factors for emerging pollutants such as new pesticides and policies on marine aquatic eco-toxicity and terrestrial eco-toxicity. [Display omitted] • The normalization reference set in LCIA for China in 2015 is calculated. • Policy-based normalization reference sets in LCIA for China in 2020 and 2030 are forecasted. • The weighting factor sets in LCIA are calculated based on the distance-to-target method. • The critical environmental indicator is identified as ozone depletion in China during 2015–2020. • Global warming is the most important and alarming environmental indicator in the coming future (2020–2030). [ABSTRACT FROM AUTHOR]