The response of a metapopulation to a changing environment

A species distributed across heterogeneous environments may adapt to local conditions. Szep et al. (2021, Evolution) modelled this process in the infinite island model, finding the stationary distribution of allele frequencies and deme sizes. We extend this to ask how a metapopulation responds to changes in carrying capacity, selection strength, or migration rate, restricting attention to fixed deme size ("soft selection"). We develop a "fixed-state" approximation (accurate when migration is rare) which assumes that the loci are near fixation. Under this approximation, polymorphism is only possible for a narrow range of habitat proportions when selection is weak compared to drift, but for a much wider range otherwise. When local conditions (Ns or Nm) change in a single deme, it takes a time of ~1/m to reach the new equilibrium. However, even withmany loci, there can be substantial fluctuations in net adaptation, due to the bimodal allele frequency distributions at each locus. Thus, in a finite metapopulation, variation may gradually be lost by chance, even if it would persist with infinitely many demes. When conditions change across the whole metapopulation, there can be rapid response, accurately predicted by the fixed-state approximation when Nm <<1.

Transportation-Mission-Based Optimization of Heterogeneous Heavy-Vehicle Fleet Including Electrified Propulsion

Commercial-vehicle manufacturers design vehicles to operate over a wide range of transportation tasks and driving cycles. However, certain possibilities of reducing emissions, manufacturing and operational costs from end vehicles are neglected if the target range of transportation tasks is narrow and known in advance, especially in case of electrified propulsion. Apart from real-time energy optimization, vehicle hardware can be meticulously tailored to best fit a known transportation task. As proposed in this study, a heterogeneous fleet of heavy-vehicles can be designed in a more cost- and energy-efficient manner, if the coupling between vehicle hardware, transportation mission, and infrastructure is considered during initial conceptual-design stages. To this end, a rather large optimization problem was defined and solved to minimize the total cost of fleet ownership in an integrated manner for a real-world case study. In the said case-study, design variables of optimization problem included mission, recharging infrastructure, loading–unloading scheme, number of vehicles of each type, number of trips, vehicle-loading capacity, selection between conventional, fully electric, and hybrid powertrains, size of internal-combustion engines and electric motors, number of axles being powered, and type and size of battery packs. This study demonstrated that by means of integrated fleet customization, battery-electric heavy-vehicles could strongly compete against their conventional combustion-powered counterparts. The primary focus has been put on optimizing vehicle propulsion, transport mission, infrastructure and fleet size rather than routing.

THE CURRENT SITUATION AND PROSPECTS FOR THE DEVELOPMENT OF GREEN ENERGY IN RUSSIA ON THE EXAMPLE OF THE LCOE INDICATOR

The development of renewable energy in Russia follows global trends with some delay. However, at the moment, a renewable energy production cluster has been formed and the commissioning of 5.6 GW of capacity is planned until 2024, marking a more than twofold drop in capital expenditures under the power supply contracts program. The article calculated the LCOE indicator for three types of generation of wind power plants, solar power plants and small hydro power plants in 2020 based on the results of capacity selection at power supply contracts auctions, and describes the main factors and drivers for reducing this indicator.

PENEMPATAN DAN PEMILIHAN KAPASITAS TRANSFORMATOR DISTRIBUSI SECARA OPTIMAL PADA PENYULANG PERUMNAS

Some distribution transformers in densely populated areas have a loading percentage of more than 100% and also less than 10%. This problem shows that the use of a distribution transformer is inappropriate. With descriptive, correlational and comparative research methods, it is hoped that it can optimize the placement and capacity selection of distribution transformers. Data were taken from 25 people living in the Pemecutan Kelod and Tegal Harum areas using questionnaires and interviews. The data were tested using the T and Pearson tests to determine the effect of placing the distribution transformer. The results of equalization of the load with the addition of a new distribution transformer and relocation of the distribution transformer capacity to the feeder are expected to get optimal results. The technical solution is obtained with a voltage of less than 10% and a distribution transformer capacity that is in accordance with the growth of the population electrical load. Socially and culturally, for the level of conformity there is an increase in the harmony of the population after the placement of a new distribution substation (t = -7.71 and p <0.05), for the comfort level there is an increase in the comfort of the population after the placement of a new distribution substation (t = -9.57 and p <0.05), and for the level of security there was an increase in the tranquility of the population after the placement of a new distribution substation (t = -8.42 and p <0.05). The relationship between suitability and comfort obtained a very strong relationship (r = 0.90), the relationship between suitability and safety obtained a very strong relationship (r = 0.85), and the relationship between comfort and safety obtained a very strong relationship (r = 0.86).

Time-Series PV Hosting Capacity Assessment with Storage Deployment

Europe aims to diversify energy sources and reduce greenhouse gas emissions. On this field, large PV power growth is observed that may cause problems in existing networks. This paper examines the impact of distributed PV systems on voltage quality in a low voltage feeder in terms of the European standard EN 50160. As the standard defines allowable percentage of violation during one week period, time-series analyses are done to assess PV hosting capacity. The simulations are conducted with 10-minute step and comprise variable load profiles based on Gaussian Mixture Model and PV profiles based on a distribution with experimentally obtained parameters. In addition, the outcomes are compared with “snapshot” simulations. Next, it is examined how energy storage utilization affects the hosting capacity. Several deployments of energy storages are presented with different number and capacity. In particular, a greedy algorithm is proposed to determine the sub-optimal energy storage deployment based on the voltage deviation minimization. The simulations show that time-series analyses in comparison with snapshot analyses give completely different results and change the level of PV hosting capacity. Moreover, incorrect energy storage capacity selection and location may cause even deterioration of power quality in electrical systems with high RES penetration.

Development of a Kernel Extreme Learning Machine Model for Capacity Selection of Distributed Generation Considering the Characteristics of Electric Vehicles

The large-scale access of distributed generation (DG) and the continuous increase in the demand of electric vehicle (EV) charging will result in fundamental changes in the planning and operating characteristics of the distribution network. Therefore, studying the capacity selection of the distributed generation, such as wind and photovoltaic (PV), and considering the charging characteristic of electric vehicles, is of great significance to the stability and economic operation of the distribution network. By using the network node voltage, the distributed generation output and the electric vehicles’ charging power as training data, we propose a capacity selection model based on the kernel extreme learning machine (KELM). The model accuracy is evaluated by using the root mean square error (RMSE). The stability of the network is evaluated by voltage stability evaluation index (Ivse). The IEEE33 node distributed system is used as simulation example, and gives results calculated by the kernel extreme learning machine that satisfy the minimum network loss and total investment cost. Finally, the results are compared with support vector machine (SVM), particle swarm optimization algorithm (PSO) and genetic algorithm (GA), to verify the feasibility and effectiveness of the proposed model and method.