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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]

The continuous growth of renewable energy sources (RESs) has increased the demand for flexibility in managing uncertainties of RES generation. Energy storage systems (ESSs) are recognized as one of the promising methods to address this challenge. For multi‐area power system planning problems, capacity allocations of RESs can vary considerably among areas accounting for the geographic diversities in RES generation and load patterns. This paper presents a hierarchical coordinated planning model considering the interaction of local‐area (LA) planning entities and the system‐wide (SW) planner. A novel multi‐objective LA planning model is proposed to compute optimal capacity configuration ratios of RESs and ESSs based on regional resource characteristics. The SW planner acts as a coordinator and further optimizes specific capacities of generation assets. Optimal configuration ratios produced by LA planning entities are important indices reflecting LA planning preferences and serve as a link between the two planning layers. Numerical results on a modified NREL‐118 test system show that the proposed planning model can reduce carbon emission by an average of 10.5% and improve the RES utilization rate by an average of 1.8% compared with planning models without considering configuration ratios and ESS installations. [ABSTRACT FROM AUTHOR]

This paper proposes a distributed generator (DG) optimization framework based on an improved northern goshawk optimization (INGO) algorithm to install multiple DGs into the distributed system (DS) simultaneously. Various case studies are conducted in constant or varying load and generation models. The framework considers the power system's critical constraints while optimizing each DG's location, capacity, and power factor to minimize energy losses and voltage deviations and improve DG penetration levels and system voltage distribution. The effectiveness and stability of the INGO algorithm were first verified in two constant test systems, and the effect of the optimal power factor of the DG on the system was considered. The results of all case studies show that the INGO algorithm performs better than other existing methods in reducing active power losses and voltage deviations. When the three parameters of generator location, capacity, and power factor were optimized simultaneously, the active power losses of the two test systems were reduced by 93.96% and 98.11%, respectively. On this basis, photovoltaics (PVs) and wind turbines (WTs) as renewable energy DGs (REDGs) were also introduced in two changing test systems. The performance gap between two REDGs and conventional generators (CGs) is compared. The all-day active energy losses in the two tested systems were reduced by 88.27% and 94.74% after WTs installation. Much higher than 51.23% and 53.64% after installing PVs, almost as good as installing CGs. Therefore, WTs can replace CGs to reduce carbon emissions because the difference in energy loss and voltage deviation between the two is tiny. [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]

A region-based low-carbon operation analysis method for integrated electricity‑hydrogen-gas systems (IEHGSs) is proposed in this paper. The committed carbon emission operation region (CCE_OR) and the economic CCE_OR (ECCE_OR) are proposed to evaluate the low-carbon feasible space (LCFS) from the low-carbon, secure, and economic perspectives, which provides powerful tools for the low-carbon operation analyses of IEHGSs. Firstly, a coordinated mechanism of carbon flow and hydrogen flow is developed to excavate the low-carbon operation potential of IEHGSs. Then, based on the distributionally robust chance-constrained approach, the concepts and models of CCE_OR and ECCE_OR are defined and formulated considering uncertainties and hydrogen merits. Furthermore, a region model reformulation method is proposed to reformulate the uncertain and non-convex CCE_OR and ECCE_OR models into the deterministic and mixed-integer linear region models. The geometric properties of CCE_OR and ECCE_OR are both theoretically proven, which are the union set of finite convex and bounded polytopes. The united region solution methodology considering the geometric properties of CCE_OR and ECCE_OR is presented to effectively characterize CCE_OR and ECCE_OR. Finally, the numerical results on two test systems verify the effectiveness and applicability of the proposed methods. Simulation results indicate that the proposed methods can improve the LCFS, characterize visual coupling relationships between LCFS and observation variables, and provide comprehensive low-carbon operation information of operation points. • The coordinated mechanism of carbon flow and hydrogen flow is developed to excavate the low-carbon operation potential. • The committed carbon emission operation region (CCE_OR) and the economic CCE_OR (ECCE_OR) are defined and formulated. • The unified region solution methodology considering the geometric properties of CCE_OR and ECCE_OR is established. • The low-carbon feasible space (LCFS) capturing comprehensive operation information is characterized. • The merits and restrictions of zero-carbon characteristics of hydrogen on LCFS are revealed. [ABSTRACT FROM AUTHOR]

Operational flexibility is to depict the ability of a power system in guaranteeing a secure and high-performance system operation, which is essential for distribution systems (DSs) with high renewable energy generation (REG) penetration. However, due to the dispersed integration of energy storage systems and uncertain REGs, the available regulation capability exhibits spatio-temporal coupling. It poses a challenge to quantify flexibility considering multiple locations and time periods. This paper proposes a novel analytical region-based method to characterize the flexibility considering spatio-temporal coupling, where the total dispatch budget and carbon emission constraints are considered to guarantee economic and environmental benefits. Leveraging the basic feasible solution theory, we derive an analytical formulation of the flexibility region, expressed as a set of linear inequalities. Note that no inner or outer approximation techniques are employed in the deduction procedure. The effectiveness and scalability of the proposed method are validated on test systems. Case studies demonstrate that the proposed method enables precise quantification and visualization of operational flexibility for DSs. The derived flexibility region is a closed polyhedron in a high-dimensional space, signifying the maximum ranges of uncertainties that can be accommodated. • A region-based method is developed for quantification and visualization of operational flexibility in distribution systems. • An analytical expression of the flexibility region under spatio-temporal coupling is derived from the dispatch model. • Total dispatch budget and carbon emission constraints are considered in flexibility quantification. [ABSTRACT FROM AUTHOR]

Carbon emissions and carbon flow within a specific area have been studied extensively, but little is known about the effects of inter-regional material and energy exchanges on carbon emissions. In this study, we examined an urban agglomeration in Northeastern China. A carbon emission model was established based on input-output analysis, and robustness analysis was used to test the system's stability. A new factor for indirect emission dominance intensity was developed to distinguish the significances of indirect emissions and direct emissions. Sectoral control and dependence relationships were explored by ecological network analysis. Results showed that carbon emissions from primary energy consumption were 2.34 times higher than those from secondary energy consumption. The indirect carbon emissions were more than double the direct emissions. The largest direct and indirect carbon emissions come from the advanced manufacturing sector, and the materials and energy conversion sector, respectively. In urban agglomeration, the exploitation relationship between sectors was dominant, suggesting that the structure of the system needs to be improved. Our results provide sector-specific information to help each sector to adjust industrial planning for emission reductions. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are widely recognized as promising solutions to reduce carbon emissions from the transportation sector. The rapid proliferation of EVs intensifies the interdependency between the power distribution system (PDS) and transportation system (TS). This paper proposes a distributed low-carbon economic dispatch scheme for the integrated power and transportation system (IPTS). Carbon emissions from the TS are quantified by a carbon dioxide calculation model, and the emissions from the PDS are determined by the electricity generation of fossil-fueled generators and the carbon reduction of carbon capture and storage (CCS) devices. An integrated model reformulation method is employed to transform the original non-convex model into a tractable one. Then a distributed solution method based on the spectral penalty parameter based adaptive alternating direction method of multipliers (SPPA-ADMM) is developed to protect the privacy of the PDS and TS. The reformulated model is solved iteratively, and the penalty parameter of the SPPA-ADMM is adjusted adaptively to improve the convergence performance of the method. Numerical results on three test systems demonstrate the effectiveness of the proposed scheme in achieving low-carbon economic dispatch of IPTS. Moreover, case studies validate the superiority of the SPPA-ADMM based distributed solution method in computation performance. • A low-carbon economic dispatch scheme for integrated power and transportation system is proposed. • An integrated model reformulation method is designed to transform the non-convex model into a tractable one. • The SPPA-ADMM based distributed solution method is used to protect the privacy and improve computation efficiency. • Effects of the proliferation of EVs on costs and carbon emissions are discussed in sensitivity analysis. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a compromise programming (CP) framework for solving a multi-objective two-stage stochastic unit commitment problem characterized by high penetration of wind power. The proposed framework aims at finding best-compromise Pareto efficient on/off schedules, accounting for wind and power demand uncertainties: such solutions must trade off the three objectives of operating cost, CO 2 emissions, and wind power curtailment in accordance to the decision maker preferences. To achieve this, we introduce a practical procedure to compute the ideal and Nadir points associated to the multi-objective two-stage stochastic unit commitment problem and propose a linearized ℓ 1 norm-based compromise program to design best-compromise on/off schedules that correspondingly minimize and maximize their weighted distances to the ideal point and to the Nadir point, considering the preference weights assigned by the decision maker to each of the three objective functions. The proposed CP framework is applied to a case study related to the New England IEEE-39 bus test system. The results show that, compared to the schedule obtained through the traditional minimization of operating cost, the designed best-compromised schedules considerably improve CO 2 emissions reduction and wind power curtailment performance by conservatively sacrificing operating cost performance. • A linear multi-objective two-stage stochastic unit commitment model is presented. • The model accounts for wind and power demand uncertainties. • The solutions must trade off operating cost, CO 2 emissions and wind power curtailment. • A compromise programming framework is proposed to solve the problem. • The obtained best-compromise Pareto efficient solutions are compared and analyzed. [ABSTRACT FROM AUTHOR]

Plug-in electric vehicles (PEVs) are one of the most promising technologies for decarbonizing the transportation sector towards the global Net-zero target. However, charging/discharging of PEVs impacts the electricity network's stability, increases the operating costs, and affects the voltage profile. This paper proposes a flexible multi-objective optimization approach to evaluate and deploy vehicle-to-grid and grid-to-vehicle technologies considering techno-economical and environmental factors. Furthermore, life cycle of PEV batteries, charging/discharging pattern, and driving behaviours of the PEV owners are considered. The simulations are run over a modified IEEE 69-bus radial distribution test system to minimize two objective functions including the operating costs and CO 2 emissions using the heuristic-based Firefly Algorithm in a stochastic optimization framework considering renewable generations, load consumption, and charging/discharging timing of PEVs as the uncertain parameters. The results demonstrate significant reductions in the operating costs and CO 2 emissions, and the voltage profile of the network is improved properly. Besides, by implementing the discharging facility of PEVs in the network, the PEV owners save a considerable amount in operating costs. • Minimizing operating costs and CO 2 emissions, and regulating voltage profile. • Proposing a multi-objective stochastic heuristic-based firefly algorithm. • Integrating renewable units, micro turbine, fuel cell, plug-in electric vehicles. • Proposing a charging method including drivers' behaviours and driving patterns. • Investigating the uncertainties of renewables, load, and parking time of vehicles. [ABSTRACT FROM AUTHOR]

• A day-ahead generation and HVDC transmission joint scheduling method is presented. • Operation cost, carbon and pollutant emission are considered in objective function. • A distributed scheduling model is established based on SADMM technique. • The normal boundary intersection method is used to handle multiple objectives. • Case studies of a two-area HVDC-linked power grid show the method's effectiveness. In order to improve the overall operation economy and efficiency of two-area HVDC-linked power grids, a distributed scheduling approach is presented in this paper for the multi-objective day-ahead generation and HVDC transmission joint scheduling (GHT-JS) problem. At first, a multi-objective day-ahead GHT-JS model is formulated aiming at minimizing the operation cost, the carbon emission and the pollutant emission, in which HVDC tie-line operation constraints are considered. Secondly, the normal boundary intersection method is implemented to transform the multi-objective GHT-JS problem into a series of single-objective GHT-JS problems. Then, a distributed scheduling model is established based on the synchronous alternating direction method of multipliers for each single-objective problem. The original problem is decomposed into an upper-level master sub-problem of coordinating HVDC transmission power and two parallel lower-level generation scheduling sub-problems. Finally, case studies of a two-area interconnected test system via two HVDC tie-lines show the effectiveness of the proposed algorithm. [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]