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Wire icing seriously threatens the safety and reliability of power systems. Strengthening the simulation study of icing thickness is crucial for disaster prevention and mitigation, as well as the adjustment of power system operation strategies. The meteorological elements, including air temperature, precipitation and wind speed, are the key factors affecting wire icing. In this study, the meteorological elements related to icing thickness are obtained by numerical models, and the relationship between these elements and icing thickness observations is established to develop a model for simulating icing thickness. The model is applied to study typical icing in Yunnan Province, China. The results indicate that the deviation of the simulated icing thickness is about 2 mm, smaller than that from the traditional model. Batch experiments demonstrate that the new model developed in this research is applicable to the vast majority of 238 observation stations in Yunnan Province for icing thickness simulation, and the method can yield lower simulation deviations. [ABSTRACT FROM AUTHOR]

The aggregation of end-users who produce, consume, and exchange surplus energy within the borders of a shared geographical area manifests a new way of using renewable energy as represented by Energy Communities (EC). At that, both industrial and "residential" community microgrids can be such consumers, which act as a single controlled object in relation to the external power grid. The reasons and objectives of prosumers joining their forces to form an EC differ depending on their predominant type and nature (industrial enterprises, residential buildings, farms, etc.). These include consumers' need for energy autonomy, lowering of otherwise high electricity tariffs, improvement of energy supply resilience and reliability, minimization of urban pollutant emissions, more efficient use of renewable energy sources, etc. This study relies on a general methodology developed by the authors to assess key defining features of the establishing and operating ECs of industrial and community microgrids serving residential loads. We demonstrate how methods of multi-criteria decisionmaking and artificial intelligence can take into account these features in an efficient way so as to maximize the local and system-wide effects of different types of ECs.

The strong dependence of the accuracy of empirical solar radiation models on natural and climatic conditions causes their diversity and ambiguity of choice. The paper proposes an approach to choosing two most accurate models from a variety of empirical models designed to estimate global and diffuse solar radiation over the entire territory based on data from several locations. The approach focuses on the improvement of the accuracy and adaptability of models to a variety of natural and climatic conditions. To improve the adaptability of models, their coefficients are determined using a genetic algorithm. In contrast to the least-squares method, the objective function is aimed at minimizing the maximum mean absolute error (MAE) in estimating solar radiation at locations. A new procedure for extended validation of empirical models is proposed to improve their accuracy. In addition to the standard validation based on estimates of global and diffuse radiation, the accuracy of direct solar radiation estimates obtained as the difference between models' estimates is checked. A numerical example is given for the Baikal natural territory. Based on the proposed approach, 18 empirical models are calibrated and the 2 most accurate models are selected. The maximum MAE in estimates of global, diffuse, and direct radiation for nine locations of the territory was 0.51, 0.57, and 0.6 MJ/m2day, respectively. The least-squares method of calibration yielded higher estimates of the maximum MAE: 0.64, 0.64, 0.88 MJ/m2day. The estimates of the developed models are more accurate compared to those provided by ERA5 and NASA POWER databases. [ABSTRACT FROM AUTHOR]

The problem of power supply to remote consumers in the “Baikal-Khövsgöl” Cross-Border Recreation Area, associated with the high length and low reliability of power lines is discussed. The assessment of the modes of the power distribution grid showed that the introduction of new consumers in this territory will lead to unacceptable voltage deviations, even taking into account the installation of reactive power compensating devices. Since the area under consideration has a high solar energy potential, it is advisable to use distributed solar generation. The choice of locations and capacities of solar power plants is a multi-criteria optimization problem. Four criteria are proposed: total voltage deviation, total active power losses, reliability and capital costs for construction. An algorithm for multi-criteria optimizationis developed and implemented as a program in the MATLAB, which consists in sequential verification of the feasibility of installing additional power of solar power plants at the consumers of each of the substations under consideration. For each variant, the electric grid mode is assessed using the Power system analysis toolbox program. Solutions for the choice of locations and capacities of solar power plants are obtained, providing high scores by criteria in accordance with the given criteria importance coefficients.

The article deals with the problem of the negative impact of wind farms stochastic generation on power grid. One of the ways to reduce the stochasticity of the wind farms generation is their geographically distributed siting. A technique for sizing and distributed siting of wind farms from the standpoint of the influence on the variability of the total generated power is proposed. Modeling of wind power generation with hourly detailing is carried out using the developed Wind-MCA software based on data from archives of long-term observations of ground-based weather stations. The optimal distribution of wind turbines in potential locations is based on a genetic algorithm. The objective function is the coefficient of variation of the power generated by all wind farms in the sites under consideration, depending on the number of wind turbines in their composition. The genetic algorithm is implemented using the built-in MATLAB function. The proposed technique is applied to assess the capacity options and sites for wind farms in the Zabaykalsky Krai. The solution providing the minimum value of the coefficient of variation of the wind farms generated power and high value of the wind farms capacity utilization factor has been obtained.

This paper considers the problem of multi-criteria option selection for the purpose of developing generating capacities for local power supply systems. The authors suggest a 2-stage method of multi-criteria analysis. At the 1st stage one conducts a multi-criteria selection of the most prospective power generation technology. At the 2nd stage one conducts a multi-criteria assessment of options for power station capacity ratio on the basis of the selected efficient technology of power generation. At the first stage the authors suggest a modified method of hierarchy analysis allowing for the decrease in the number of requests to a decision-maker (DM). At the second stage the authors use the modified method TOPSIS with the application of value functions for calculating non-linear change of the DM preferences in relation to the assessment of options by the criteria.

The paper dwells upon the problem of multi-criteria choice of ways to develop generating capacities to supply power to remote consumers. We herein propose a two-step multi-criteria analysis method: choosing promising power-generation technology first, and then specifying the generating-capacity structure. The paper describes the structure of the proposed multi-criteria methods: the interval TOPSIS method for Step 1; for Step 2, an upgraded analytic hierarchy process based on identifying the structure of the decision maker’s preferences. We demonstrate the use of this method with evidence from the Penzhinsky District, Kamchatka Krai. Thermal power plants, hydroelectric power plants, diesel power plants, as well as solar and wind power are analyzed as power sources. Step 1 includes: analyzing the potential power-supply loads in a specific area; formulating alternative power-generation technology; formulating goals and criteria; criterion-based evaluation of alternative options using objective and subjective models; multi-criteria evaluation of alternatives; analyzing the sensitivity of results and the selection of promising technology. Step 2 includes: formulating goals and criteria on the basis of the selected power-generation technologies; formulating the available alternatives; criterion-based evaluation of alternatives; multi-criteria evaluation and final decision-making.

The expediency of using several sources of information on climate factors for estimating the potential of wind and solar energy is substantiated. Specific features of the methodology and developed software for estimating the generation of energy by wind power plants based on weather stations open access data are considered. The possibility of taking into account the results of aerodynamic modeling of the flow of the terrain by the wind flow is realized. The methodology is implemented in the form of a computer program called Wind-MCA. It includes a module for analyzing wind power potential, a module for analyzing wind turbines, an economic analysis module, and a multi-criteria analysis module. Specific features of the methodology and developed software for estimating the generation of energy by solar power plants based on data on the transparency of the atmosphere, temperature and cloudiness are considered. The technique is implemented in the form of a computer program called Sun-MCA. The estimation of the wind energy and solar energy potential of several settlements in the central zone of the Baikal region is carried out taking into account the climate change in the region.

The paper discusses the problem of parallel operation of wind and solar power plants with the power system associated with the influence of power fluctuations on the power quality and stability. A brief assessment of the prospects for the commissioning of the wind and solar power plants in Russia and a description of the associated negative impacts on the power system is given. A methodology for assessing the possible energy production of wind and solar power plants using raw data from weather stations is presented. Based on the methodology, an assessment of some regions of Russia in relation to the variability of wind speed and cloudiness is made. The wind power duration curves are given. An analysis of possible deviations of solar power under the influence of cloudiness is carried out. The effect of geographical aggregation of wind or solar power output to increase the guaranteed power generation and reduce the negative impact on the stability of the energy system is shown.

This study proposes an approach to multicriteria assessment of biomass gasification-based hybrid energy systems in remote areas using the modified SMARTS method. This approach involves comparing two groups of hybrid energy systems: those based on biomass gasification and those based on diesel generation, which is already widely used in remote areas. The hybrid energy system configurations are developed using the HOMER software in terms of the maintenance schedule of biomass gasifiers. Alternatives are compared according to the criteria of economic, technical, environmental and social efficiency. The proposed modification of the SMARTS method uses both crisp and interval weights of criteria and criteria estimates for decision making under uncertainty of the preferences and input data, especially in terms of initial capital and operating costs, feedstock and diesel fuel prices. The final choice of a hybrid energy system is based on the relative dominance indicator. Two case studies of isolated rural settlements show that biomass gasifiers when supplemented with diesel generation and other renewable energy sources are the best sustainable solution as compared with other feasible alternatives. These conclusions are robust to sufficiently significant changes in preferences and input data. [ABSTRACT FROM AUTHOR]