To effectively reduce carbon emissions of the integrated energy system (IES) and solve safety problems in the echelon utilization of retired power batteries, this study proposes a low-carbon economic dispatch method for the application of retired power batteries in IES. First, a reliability evaluation method based on confidence interval estimation for retired power battery reconfiguration energy storage system (RESS) is proposed. A reasonable charge and discharge safety threshold improves the operation safety of retired power batteries. Second, the carbon consumption characteristics of retired power batteries are tracked. In addition, the carbon emission cost model for the retired power batteries in the whole process of echelon utilization to IES is established. Finally, the stepped carbon trading model is introduced, and the IES carbon emission cost and operating cost model is established. The battery safety threshold of the echelon utilization is periodically adjusted by considering the time-of-use electricity price, which realizes economical low-carbon dispatch of the IES. Through multi-scenario simulation analysis, the scheduling method for echelon utilization of retired power batteries in IES can effectively reduce the carbon emissions and costs of the verified system.
Isolated DC microgrids should be equipped with energy storage systems consisting of distributed energy storage units. To extend the service life of the energy storage system, the latter should keep the charge state of distributed energy storage units (DESUs) in a balanced state; however, in practice, there may be differences in the capacity of DESUs and mismatch in line resistance, which seriously affect the balancing effect of state of charge (SOC). To address this issue, this study proposes an improved sag control strategy, which enhances the discrimination of the sag coefficient to SOC variations by nesting exponential functions with power functions, thus improving the equalization effect of the SOC. Next, by defining and equalizing the power state factor, the output power of DESUs is proportionally distributed according to their capacity. To reduce the communication pressure, this study adopts a sparse communication network to convey the required information of each DESUs, i.e., the global average value is estimated by applying the dynamic consistency algorithm. Finally, a simulation model built on MATLAB/Simulink verifies the effectiveness of the proposed strategy.
The iron and steel industry in China is under pressure to reduce carbon emissions; however, the carbon emissions from the traditional blast furnace long-process metallurgy remain high, and the hydrogen metallurgy technology is still in infancy. Therefore, the development of electric arc furnace short-process steelmaking is the low-carbon development approach in the medium term. First, the energy supply and consumption structure of an integrated energy system of the steel industrial community is constructed, with new energy sources, energy storage equipment, blast furnace gas generation technology, and two metallurgical processes: the blast furnace long-process and electric arc furnace short-process. Then, a model of the equipment and metallurgical processes is presented. Second, an optimal dispatch model that considers carbon emissions and equipment operating costs is developed for the community. Finally, the impact of introducing new energy sources, energy storage equipment, and blast furnace gas generation technology on the proportion of electric arc furnace steel production, as well as the impact of different proportions of electric arc furnace steel on the economics of the community, is investigated in different scenarios. The results show that the introduction of new energy, energy storage equipment, and blast furnace gas power generation technology will increase the proportion of electric arc furnace steel in the community, reduce the community's production and operating costs, and reduce carbon emissions.
“Carbon peaking” and “carbon neutrality” are important strategic decisions implemented by countries, and the development of electric vehicles is one measure of achieving low carbon emissions. This study proposes a regional-level method for assessing carbon emissions of electric vehicles from a full lifecycle perspective. This method addresses the one-sidedness and inaccuracies present in existing carbon emission assessments for electric vehicles. To evaluate the carbon emissions of electric vehicles throughout their life cycle, an evaluation index and a quantitative model, beginning from the material and fuel cycles, were constructed. Subsequently, a three-stage data envelopment analysis was employed to analyze the carbon emissions and obtain the carbon emission assessment results of all regional electric vehicles. The method was further applied at the provincial level using actual vehicle and development data from each province to evaluate the carbon emissions of electric vehicles in various provinces of China. The simulation results indicated significant differences in the carbon emission efficiency of provincial electric vehicles in China and demonstrated that the proposed evaluation method can eliminate random factor disturbances and comprehensively evaluate the carbon emissions of regional electric vehicles.
To control carbon emissions due to energy consumption and to achieve the objectives of carbon peaking and carbon neutrality, a step-by-step carbon-trading mechanism is proposed that considers carbon offsets. Additionally, the economic and environmental benefits of using natural gas pipelines to generate electricity were considered based on the complementary characteristics of multiple energy sources, such as electricity, heat, and gas, and the principle of energy cascade utilization. A low-carbon economic optimization scheduling model was developed for an integrated energy system of electricity-heat-gas cogeneration, and a case study was performed. First, a carbon-trading mechanism that included carbon offset was established, with the environmental cost incorporated into the system economic optimization target. Subsequently, a power generation model utilizing the pressure of natural gas pipelines was developed using the exergy analysis method, while relevant constraints were determined by combining other unit output and energy transmission network models within the system. The aim of minimizing the operating cost of the system and reducing carbon emissions was achieved by developing an overall framework for the integrated energy system, and low-carbon economic optimization scheduling was conducted based on the case study. This approach reduces the operating cost of the system, energy consumption, and carbon emissions. Furthermore, it prevents the adverse effects on the upstream power grid caused by the fluctuations in power demand.
The emergence of 5G networks will drive social change. However, 5G networks require construction of numerous base stations, leading to greater carbon emissions. Scientifically analyzing the carbon reduction potential of 5G base stations and proposing an effective carbon reduction route are urgent issues for the information and communications technology (ICT) industry to address. First, in the context of green power trading, a 5G base station carbon emission accounting model based on the Kaya identity is constructed by considering factors such as base station scale, single-station energy consumption, green power ratio, and emission coefficient. A carbon emission reduction contribution analysis model is established using the logarithmic mean Divisia index (LMDI) decomposition method, and the carbon emission reduction potential and carbon emission reduction factor contribution of 5G base stations in China are analyzed using the scenario analysis method. The results show that, for carbon growth, the base station scale is always the greatest driver; for carbon reduction, single-station energy consumption contributes the most to the baseline and established policy scenarios, and the proportion of green power trading contributes the most to the low-carbon-policy scenario. Therefore, for the 5G base station carbon reduction path, participating in the common construction and sharing of communication infrastructure to reduce the base station size is a fundamental way to upgrade green energy-saving technologies to reduce single-station energy consumption. Expanding the scale of green power trading to increase the proportion of green power trading is the key initiative to achieve the target ahead of schedule. The findings and conclusions of this study can provide a reference for the ICT industry to conduct double-carbon work.
In the background of new power systems, advanced information and communication technology have been effectively applied in the field of power distribution systems. The distribution system has developed into a typical cyber-physical one. The traditional distribution network planning method can not meet the development needs of interdependent systems. In this study, the planning technology of cyber-physical distribution systems is summarized. Firstly, the basic structure, technical characteristics, and application scenarios of the cyber-physical distribution system are discussed, and the relationship between the cyber-physical distribution system and distribution Internet of Things is analyzed. Subsequently, the core elements of cyber-physical distribution system planning are analyzed from the perspective of double-layer coupling characteristics, multiple uncertainties, and multiple types of constraints. Thereafter, the current status of cyber-physical distribution system planning is reviewed. Finally, the framework of cyber-physical distribution system planning technology is established, and the key technologies for planning the distribution information physical systems are prospected.
Security and stability control systems (SSCSs) are the second type of defense system for power grids. Their controllable power demonstrates their capability of maintaining frequency stability. However, control path station availability and controllable power fluctuations increase the difficulty of assessing the controllable power of SSCSs. In this study, a controllable capacity uncertainty analysis method for SSCS is proposed. Based on the fluctuation characteristics of the controllable capacity at the executive station (EX), a basic reliability allocation model of the EX is established using evidence theory. A station control probability model is established by improving the probability-weighted adjacency matrix, and the basic confidence assignment of EX is modified. By considering the correlation between the station control capacity and the control path, a joint credibility allocation model for the controllable capacity of SSCSs is derived. The likelihood cumulative probability distribution (LCPD) and belief cumulative probability distribution (BCPD) of controllable capacity are defined. Numerical results show that the proposed model quantifies the preservation ability of the SSCS against a power shortage. The effectiveness of the proposed method is verified by comparing it with a traditional probabilistic method.
By considering the impact of high-permeability distributed generation access on distribution networks, a data-driven robust planning method that accounts for the resilience of a distribution network is proposed. First, a comprehensive resilience evaluation system with high-permeability distributed generation is proposed. The three-stage planning model is then transformed into a two-stage data-driven robust planning model to complete the decision-making of the line-reinforcement scheme and the optimal allocation of distributed generation location capacity. An improved column and constraint generation algorithm based on the limit scenario method is used to solve the robust model. Finally, an example is provided to analyze the effects of the proposed resilience improvement measures. The results show that the proposed method can effectively quantify the resilience of a distribution network and provide a reference for resilient distribution network planning with high-permeability distributed generation.
With the continuous advancement in the renewable power system construction process, the uncertainty of system operating conditions has significantly increased, resulting in a lower antidisturbance ability, higher cascading failure occurrence rate, and shorter evolution time. The traditional power-flow calculation method relies on the enumeration of scenarios to consider random factors, making it difficult to cope with the complex operating conditions experienced with a high percentage of new energy grids in terms of timeliness and accuracy. Therefore, a multi-scale feature-set-based data-driven method for identifying cascading faults under conditions of having a high percentage of new energy grids is proposed by mining historical data patterns and fully considering the stochastic information contained in historical operation data. Indexes that can describe the topological characteristics of a complex network and the operation state of the system are extracted from the macro-, meso-, and micro-levels to form the index set. Based on a bidirectional long-term and short-term memory neural network, the mapping relationship between the index set and the system operation state is studied using the historical cascading failure dataset. This enables a cascading fault prediction model of a high-proportion renewable power system to be constructed. The effectiveness of the proposed model is verified using the topology of the Xinjiang power-grid system.
Under the connection of high-penetration distributed photovoltaic generation, new challenges face the economic and safe operation of distribution networks. In this study, a distributionally robust optimization model considering switch reconfiguration and demand response was established to optimize the operation of a distribution network. First, considering switch reconfiguration and price-based demand response, topology optimization and operation schedules of the distribution network were obtained with the optimization objective of minimizing daily operating cost. Second, using the historical photovoltaic (PV) output data, the typical scenarios of PV output and its probability distribution are obtained through clustering, and the 1-norm and ∞-norm are used to constrain the confidence interval of the uncertainty probability distribution, and a data-driven distributionally robust two-stage optimization model is established. Finally, with the modified IEEE 33-node system, the Cplex solver was used based on the column-and-constraint generation algorithm, and the feasibility of the model was verified.
DC microgrid systems have multiple controllers, and the combination of different controller parameters has different effects on the stability of the entire system. To improve system stability, a multi-controller parameter global optimization method based on small-signal stability is proposed. First, the small-signal model of the system is deduced, and eigenvalue and participation factor analyses of the system are performed to determine the stability domain of key control parameters such as PI parameters, droop coefficients, and virtual inertia coefficients. The objective function, including the maximum real part of the eigenvalue, damping ratio, and maximum output power of the energy storage, is established. The sample data are obtained by using an orthogonal experiment, and the multi-objective is weighted by using a comprehensive weighting method. The key parameters of the system are then optimized using improved particle swarm optimization based on the grey wolf algorithm, thereby yielding the optimization results. The results show that the eigenvalues of the system after parameter optimization are farther away from the imaginary axis, the damping ratio increases, and the maximum output power of the energy storage increases, which improves system stability. Finally, the effectiveness and superiority of the proposed method are verified by building a model on the MATLAB Simulink simulation platform.
The current criterion for the configuration of operating reserve capacity is based on the load percentage and maximum single-unit capacity, which can result in insufficient or excessively abundant reserve capacity in cases with a high proportion of renewable energy access. To address the challenge of accurately assessing the reserve capacity demand for high proportions of renewable energy grid operation during current dispatching operations, this study proposes a probabilistic dynamic assessment method for operating reserve requirements based on kernel density estimation. Furthermore, a dynamic confidence selection strategy considering the renewable energy penetration rate and evaluation period is proposed, which can facilitate the rolling differential evaluation of the up- and down-regulation of reserve capacity demand. Based on this proposal, a strategy for formulating conventional power supply startup capacity that considers dynamic reserve capacity demands was developed. This strategy maximizes the space for renewable energy consumption while ensuring sufficient operation reserve capacity. An application test was conducted using a provincial power grid with a high proportion of renewable energy sources, and the effectiveness of the proposed method was verified.
The development of shared electric vehicles does not only provide users with a convenient way to travel but also provides efficient and flexible adjustment for urban power grid resources, large-scale shared electric vehicle (EV) travel and charge-discharge behavior, strong randomness, complex charging and discharging control model online rolling together to consider multiple stakeholders, large amount of calculation, and high real-time requirements. First, this study uses the aggregation modeling method based on the SOC interval to determine the energy transition state in time or space of shared EV; it also reduces the dimension of the optimal charging and discharging schedule of large-scale EVs. Thereafter, we consider the operator profit and utility of the limited rational users' cumulative prospect as well as the power demand response on multiple subject gains. Moreover, the optimization model of charge and discharge depth is constructed based on reinforcement learning. Next, the deep Q net is applied to solve the network method, and real-time online sharing of EV charging and discharging aggregation optimization strategy in the different regions is achieved. In addition, the model effectively copes with the effects of randomness. Finally, combined with the actual operation data of 5000 shared EVs in 9 regions of a city, numerical example analyses verify that shared EVs within the city have the characteristics of time-space transfer of energy. The modeling method and solving strategy proposed in this study are effective in solving the optimization scheduling problem of large-scale shared EV charging and discharging while aiming to ensure the interests of multiple subjects.
In October 2021, during a no-load unlocking test on an offshore converter station with single- and double-connected transformers, an AC system voltage resonance of 2500 Hz or 2000 Hz was detected at the Rudong Offshore Wind Power Flexible Direct Project. This paper provides a detailed analysis of the impedance of flexible DC and AC systems and their interaction characteristics. The simulation results confirmed that the system was at risk of resonance in this frequency band, and the resonance mechanism was revealed. To address this resonance issue, an active resonance suppression method based on proportional resonance control of the voltage feedforward link was proposed in this study, which can significantly reduce the high-frequency negative damping of flexible straight systems. Simultaneously, a passive resonance suppression method based on a correction device for amplitude and phase angle was also proposed to improve the full-band impedance characteristics of the flexible direct system and the feeding system. The simulation results obtained using the Rudong engineering parameters show that this method can suppress system resonance in the full frequency band and is expected to completely solve the resonance problem of the follow-up flexible DC system. Thus, the simulation results and on-site implementation of the Rudong project proved the effectiveness of the method.
