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Conventional generation maintenance scheduling (GMS) is a solution to increase the reliability of power systems and minimize the operation and maintenance costs paid by generation companies (GenCos). Nonetheless, environmental aspects, such as zero carbon emissions, have attracted global attention, leading to emission costs being paid by electricity generators. Therefore, to obtain GMS plans that consider these factors, this paper proposes multi-objective GMS models to minimize operation, maintenance, and emission costs by using lexicographic optimization as a mathematical tool. A demand response program (DRP) is also adapted to decrease emission generation and operational expenditures. The probability that no generation unit (GU) fails unexpectedly and the average net reserve value, comprising the system reliability with and without considering the GU failure rate, are demonstrated. Numerical examples are implemented for the IEEE 24-bus reliability test system. A GMS algorithm presented in a published work is run and compared to verify the robustness of the proposed GMS models. Our results indicate that this paper provides comprehensive approaches to the multi-objective GMS problem focusing on operation, maintenance, carbon, and DRP costs in consideration of technical and environmental aspects. The use of lexicographic optimization allows for the systematic and hierarchical consideration of these objectives, leading to significant benefits for GenCos. [ABSTRACT FROM AUTHOR]

China implemented the China VI emission standard in 2020. The China VI emission standard has added requirements for the RDE (real-world driving emission) test. To evaluate vehicle CO2 emission for different vehicles, 10 conventional gasoline vehicles were tested under the RDE procedure using the PEMS (portable emission testing system) method. All vehicles tested meet the sixth emission regulation with a displacement of 1.4 L~2.0 L. Among the vehicles tested, the highest CO2 emission factor was 281 g/km and the lowest was 189 g/km, while the acceleration of RDE gets a wider distribution, varying from −2.5 m/s2 to 2.5 m/s2. The instantaneous mass emission rate could reach around 16 g/s. The amounts of total CO2 emission in the positive region and the negative region make up 82~89% and 11~18% of the overall CO2 emission during the entire RDE driving period, respectively. The same vehicle has a wide range of CO2 emission factors at different VSP (vehicle specific power) intervals. Different RDE test conditions can lead to large differences in CO2 emissions. [ABSTRACT FROM AUTHOR]

With the acceleration of urbanization, urban multi-energy systems (UMESs) generate more and more carbon emissions, causing severe environmental issues. The carbon generated by UMESs includes not only emissions from the consumption of fossil fuels for electricity generation during operation phases, but also those from the transportation, extraction, and recycling of materials during construction phases. Meanwhile, as carbon emissions are delivered with the energy flow among devices in the UMES, they are distributed differently across devices. Under this background, analyzing the carbon emissions of UMESs considering different life-cycle phases (i.e., operation and construction) and carbon flow characteristics is essential for carbon reduction and environmental protection. Considering that, a novel framework for tracing and evaluating life-cycle carbon emissions of UMESs is proposed in this paper. Firstly, the carbon emission models of different devices in UMESs, including energy sources and energy hub (EH), are established considering both the construction and operation phases. On this basis, the carbon flow matrixes of EHs coupled with the energy flow model are formulated to trace the distribution of life-cycle carbon emissions in UMESs. Moreover, different evaluation indices including the device carbon distribution factor (DCDF) and consumer carbon distribution factor (CCDF) are proposed to quantify the carbon emissions of devices and consumers in UMESs. The case study results based on a typical test UMES are presented to verify the effectiveness of the proposed framework. The analysis results of the test system show that about 60% of carbon emissions are delivered to electricity loads and the construction-produced carbon emissions of energy sources and EH devices account for nearly 35% of total carbon emissions at some periods. [ABSTRACT FROM AUTHOR]

In Generation Expansion Planning (GEP), the power plants lifetime is one of the most important factors which to the best knowledge of the authors, has not been investigated in the literature. In this article, the power plants lifetime effect on GEP is investigated. In addition, the deep learning-based approaches are widely used for time series forecasting. Therefore, a new version of Long short-term memory (LSTM) networks known as Bi-directional LSTM (BLSTM) networks are used in this paper to forecast annual peak load of the power system. For carbon emissions, the cost of carbon is considered as the penalty of pollution in the objective function. The proposed approach is evaluated by a test network and then applied to Iran power system as a large-scale grid. The simulations by GAMS (General Algebraic Modeling System, Washington, DC, USA) software show that due to consideration of lifetime as a constraint, the total cost of the GEP problem decreases by 5.28% and 7.9% for the test system and Iran power system, respectively. [ABSTRACT FROM AUTHOR]