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The solar power industry is a rapidly growing sector of renewable energy, and it is crucial that the available energy is accurately forecast. Using numerical weather prediction models, we can forecast the global horizontal irradiance on which the amount of energy produced by photovoltaic systems depends. This study presents the forecast effects for one of the most challenging weather conditions in modelling, occurring in central and eastern Europe. The dates of the synoptic situations were selected from 2021 and 2022. Simulations were carried out for 18 days with a cold front and, in order to verify the model configuration, for 2 days with a warm front, 2 days with an occlusion front and 2 days with a high pressure situation. Overall, 24 forecasts were made for each of the three parameterizations of the Weather Research and Forecasting model. The data were compared with the values measured in situ at the station performing the actinometric measurements belonging to Germany’s National Meteorological Service. This paper presents the spatial distribution of the global horizontal irradiance parameters for several terms to explain the differences between the results of the different simulations.

The energy crisis in Europe in 2022 and its consequences have brought changes to approaches towards the issue of energy security, energy policy, and the ability to react to crisis phenomena in the energy market in a short period of time. European countries that are dependent on Russian fossil fuels have faced numerous dilemmas and challenges in 2022. This paper aims to analyse the instruments and energy policies introduced as the reactions of European countries, specifically with the examples of Poland and Germany, to the ongoing fossil fuel crisis in the context of a short period. Due to the specificity of the energy market, the countries’ energy policies mainly concern long- and medium-term goals. In 2022, there was an unprecedented situation in which many European countries had to make significant changes to their fossil fuel imports quickly. We analyse and evaluate how two European countries that are heavily reliant on imported energy resources responded in a short time to the necessity of modifying their patterns of fossil fuel supply and demand. The results of our research are models that illustrate both countries’ reactions to the disturbances in the energy market during the initial months of the energy crisis. As part of the research, we conducted an analysis of the energy mix of Poland and Germany, their import energy dependency, and self-sufficiency. We then compared them with the short-term energy policies of both countries. As a result, we elaborate on a comparative analysis of the models of Poland’s and Germany’s responses to the crisis. The research also assesses the similarities and differences in the response models in Poland’s and Germany’s short-term energy security policies. The results of our research may help, in the future, to choose the available short-term instruments in the energy policy of countries in the face of a sudden need resulting from disruptions in supply chains. The article contributes to the future discussion on renewed national and regional energy security, as well as efficiency concepts. Our research findings could be valuable in selecting appropriate short-term energy policy tools for countries during supply chain disruptions. This article provides significant input for future deliberations on enhancing national and regional energy security, and also efficiency strategies.

European institutions have decided to ban the sale of Internal Combustion Vehicles (ICEVs) in the EU from 2035. This opens a possible scenario in which, in the not-too-distant future, all vehicles circulating in Europe are likely to be Battery Electric Vehicles (BEVs). The Spanish vehicle fleet is one of the oldest and has the lowest percentage of BEVs in Europe. The aim of this study is to evaluate the hypothetical scenario in which the current mobility of ICEVs is transformed into BEVs, in the geographical area of the province of Barcelona and in Spain in general. The daily electricity consumption, the required installation capacity of wind and solar photovoltaic energies, and the potential reduction of NOx and particulate matter (PM) emissions are estimated. The daily emission reduction would be about 314 tons of NOx and 17 tons of PM in Spain. However, the estimated investment required in Spain to generate the additional electricity from renewable sources would be enormous (over EUR 25.4 billion), representing, for example, 5.5% of the total national budget in 2022.

The shift towards RES introduces challenges related to power system stability due to the characteristics of inverter-based resources (IBRs) and the intermittent nature of renewable resources. This paper addresses these challenges by conducting comprehensive time and frequency simulations on the IEEE two-area benchmark power system with detailed type 4 wind turbine generators (WTGs), including turbines, generators, converters, filters, and controllers. The simulations analyse small-signal and transient stability, considering variations in active and reactive power, short-circuit events, and wind variations. Metrics such as rate of change of frequency (RoCoF), frequency nadir, percentage of frequency variation, and probability density function (PDF) are used to evaluate the system performance. The findings emphasise the importance of including detailed models of RES in stability analyses and demonstrate the impact of RES penetration on power system dynamics. This study contributes to a deeper understanding of RES integration challenges and provides insights for ensuring the reliable and secure operation of power systems in the presence of high levels of RES penetration.

The article presents a study on the impact of the domain nesting method on the results of simulated solar conditions using the mesoscale Weather Research and Forecasting model. The analysis included 8 consecutive days (July 2022), which were characterized by cloudless conditions, as well as complex situations related to the passing of a cold front. The study covered a region located in Central and Eastern Europe—the southern area of eastern Germany. The results of the model simulations using the adopted domain configurations (with spatial resolutions of 9, 3, and 1 km; 3 and 1 km; and 5 and 1 km) were compared to data from ground measurements from Deutscher Wetterdienst (DWD) stations. The effect of the duration of the triggered prediction on the quality of the output data was also investigated, and for this purpose, short-term predictions covering 24 and 48 h, respectively, were selected. Research revealed the advantages of one combination of domains—3 and 1 km—over the others and showed that the results of simulations with different duration lengths were characterized by consistent results. Research supports the demand for high-quality forecasts of solar conditions, which are extremely important in the process of managing energy systems.

These days, with the significant increase in the use of renewable energy sources as additional energy sources connected to the distribution network, many challenges and difficulties arise in ensuring sustainability and reliability. The generation, transmission and distribution, in the current state of the electricity system, are facing quite dynamic changes. They are the result of the liberalization of the energy market, the increased use of renewable energy sources such as photovoltaic systems, wind turbines and the charging stations for hybrid and electric vehicles. The most important factors are related to the balancing of the energy system, the analysis of voltage stability, overcoming the consequences of the increase in short-circuit currents, increasing the transmission capacities of the system forming and distribution networks, as well as the accurate forecasting of the development of loads and consumption over the coming years. This article presents an analysis of the voltage stability in a smart microgrid for two different scenarios. The studied cases describe a linear low-voltage p-type microgrid with loads connected to it at different nodes. Data on the type and cross-section of the conductors of the studied power line are presented. Simulation studies were carried out to determine the limits of grid voltage stability when connecting photovoltaic plants with a set power. The simulation results are commented on and an analysis of the optimal operating mode of the system is realized. The model studies were implemented in the NEPLAN program environment. The research carried out allows an evaluation of the permissible limits for network stability when connecting photovoltaic plants. Through this evaluation, it can be determined how many and at which node the loads should be connected without causing an imbalance in the network. This is useful from the point of view of ensuring the sustainability and reliability of electrical energy in a microgrid.

Poland, which has been a European Union member since 2004, has undertaken compliance with its commitments to increase its share in the generation of energy from renewable sources. The target set for 2020, which is to attain a share of 15 percent of the gross final consumption of energy coming from renewable sources, has been met by Poland. The new targets for 2030, incorporated in the draft the Energy Policy of Poland 2040, assume an increase to at least 23 percent of the proportion of energy obtained from renewable sources in the gross final energy consumption. On the basis of data from the Central Statistical Office, the authors of this work analysed the changes in electricity generation based on renewable energy sources. In the period of 2004–2021, it was stated that the percentages of individual renewable sources constitute 43.7% for wind energy, 35.3% for solid biofuels, 14% for hydropower, 4.2% for biogas, 2.5% for solar energy, and 0.3% for other sources (municipal waste and liquid biofuels). In addition, this work indicates the maximal relative changes in production and the maximal changes in the portion of production, in addition to the annual and periodic rate of changes in the production of these energy sources in Poland.

This paper presents an analysis of the instability of the electricity generation of renewable energy sources (RESs), specifically Digital Twins of RESs. The first part deals with the analysis of RES electricity generation around the world and Ukraine. The following chapter describes features of functioning power grids in modern conditions in Ukraine and ways to ensure the balance reliability in the power system for conditions of high-grade RES integration. The rapid increase in electricity generation RESs causes control problems of distributed power supply in the power grid. A mathematical model of the parameter controls in normal mode electric power systems for conditions with high integration of RESs is proposed in the second part. The study investigates components of the optimality criterion at the control of normal mode parameters of the electric power system with RESs. In general, digital transformation helps decarbonize the energy supply, decrease dependency on fossil fuels, and integrate renewables into power systems. A model Digital Twin (DT) of a photovoltaic system, or an exact 3D visualization, analyzing the accumulator system depending on load and generation, are presented. The problems of Digital Twin are very widely discussed, but many papers and studies are general without any practical implementations. The main part of this paper focuses on research and deals with daily electricity generation from different kinds of RESs, namely mini-hydropower stations, photovoltaic power stations, and wind power stations. Measured data of electricity generation from photovoltaic power plants, wind power plants, and mini-hydropower plants and obtained meteorological factors were used for the calculation of Spearman’s, Kendall’s, and Pearson’s correlation rank coefficients. The main contribution of this research is to determine the main metrological factors for each kind of studied RES. In the future, it will help to decide the task of forecasting power generation more presciently. Additionally, the presented model of DT RESs allows the installation and operation of grids with higher efficiency, because it can help to predict all influences, from shading up to the optimization of the battery storage system.

A dynamic reconfiguration method based on the improved multi-objective dung beetle optimizer (IMODBO) is proposed to reduce the operating cost of the distribution network with distributed generation (DG) and ensure the quality of the power supply, while also minimizing the number of switch operations during dynamic reconfiguration. First, a multi-objective model of distribution network dynamic reconfiguration with the optimization goal of minimizing active power loss and voltage deviation is established. Secondly, the K-means++ clustering algorithm is used to divide the daily load of the distribution network into periods. Finally, using the IMODBO algorithm, the distribution network is reconstructed into a single period. The IMODBO algorithm uses the chaotic tent map to initialize the population, which increases the ergodicity of the initial population and solves the problem of insufficient search space. The algorithm introduces an adaptive weight factor to solve the problem of the algorithm easily falling into a locally optimal solution in the early stage with weak searchability in the later stage. Levy flight is introduced in the perturbation strategy, and a variable spiral search strategy improves the search range and convergence accuracy of the dung beetle optimizer. Reconfiguration experiments on the proposed method were conducted using a standard distribution network system with distributed power generation. Multiple sets of comparative experiments were carried out on the IEEE 33-nodes and PG&E 69-nodes. The results demonstrated the effectiveness of the proposed method in addressing the multi-objective distribution network dynamic reconfiguration problem.

Worldwide, smart/co-working spaces are growing significantly, and prefabricated movable buildings for such an application could (i) save energy, CO2 emissions, and costs; (ii) enhance the worker’s perceived sense of surroundings; and (iii) support the rebirth of small villages with high regenerative potential. Innovative prefabricated movable building configurations to be used as an office for smart/co-working by a maximum of 6 persons have been designed and analyzed based on simulation data. In particular, 10 case studies corresponding to building configurations differing in terms of innovative energy-efficient measures related to the building envelope (smart windows operated under various control logics) and the energy systems serving the building (photovoltaic panels, small wind turbines, and electric storages) have been modeled and simulated by applying detailed dynamic simulation models via the simulation software TRNSYS. The performance of the 10 case studies has been compared from energy, environmental, and economic points of view with respect to a baseline system characterized by conventional building envelope and energy systems, with the aim of assessing the proposed measures and identifying the most efficient configuration. The simulation results highlighted that: (i) all the proposed alternative configurations allow to save primary energy (from 10.3% up to 100%), equivalent CO2 emissions(from 10.3% up to 100%), and operating costs (from 8.5% up to 100%) with respect to the baseline building; (ii) the building configurations including the smart windows only are not economically feasible in terms of simple pay-back (SPB) period, while the building configurations equipped with photovoltaic panels and/or electric storages and/or wind turbine represent a suitable investment thanks to an SPB lower than 15.2 years; (iii) a stand-alone building configuration for smart/co-working with energy demands totally covered by means of renewable sources can be obtained by combining smart windows, photovoltaic panels, electric storages and wind turbine.

This article focuses on the role of local government in energy transition. The aim of this article is to answer the question of whether local government authorities undertake actions aimed at the energy transformation of the region. This article is based on both literature studies and independent research. The empirical research was carried out in the Lower Silesian Voivodeship, located in Southwestern Poland. This region was chosen because it experienced the negative effects of economic transformation (liquidation of the Lower Silesian Coal Basin). Two hypotheses (H1 and H2) were adopted in the article. H1 assumes that in Polish conditions, bottom-up energy transformation requires the involvement of local authorities, and they should act as both initiators and shareholders of energy cooperatives. According to H2, local authorities participate in the energy transition to a limited extent. According to the conducted surveys, there has been no investment in wind farms, hydroelectric power plants or biogas plants. Communes are not interested in large, capital-intensive investments, limiting themselves to local actions with a small spatial range and small economic significance, although they are aware of the advantages of RES.

This paper addresses the problem of economic/environmental optimal power flow with a multiobjective formulation using a second-order conic programming (SOCP) optimization model. This problem formulation considers renewable energy sources (RES), fossil-fuel-based power generation units, and voltage control. The proposed SOCP model is a stochastic scenario-based approach to deal with RES and load behavior uncertainties. An ε-constrained algorithm is used to handle the following three objective functions: (1) the costs of power generation, (2) active power losses in the branches, and (3) the emission of pollutant gases produced by fossil-fuel-based power generation units. For comparative purposes, the SOCP model is also presented using a linearized formulation, and numerical results are presented using a 118-bus system. The results confirm that changing the energy matrices directly affects the cost of objective functions. Additionally, using a linearized SOCP model significantly reduces reactive power violation in the generation units when compared to the nonlinearized SOCP model, but also increases the computational time consumed.

The building sector accounts for over 40% of global energy consumption, and many buildings are old and inefficient. However, the current pace of building renovation is not sufficient to make a tangible impact. A new strategy is needed to accelerate the renovation process. Renovation at the district level and the use of digital tools, such as a digital twin (DT) of a city district, can provide a solution. This paper proposes a novel approach to city block renovation using renewable energy sources (RES), including photovoltaic (PV) solar panels, heat pumps (HP), and electric heaters (EH), while utilizing a DT of a city district to provide a user-friendly representation of the results and data needed for holistic solutions. The proposed method combines an optimization model of the optimal heating system with a solar PV simulation technique to analyse hybrid RES solutions and potential on-site energy generation and supply. Several scenarios are simulated to evaluate RES solutions in the renovation process of the city block using the DT concept. The simulation results demonstrate that a hybrid RES solution, which includes a PV system and a heating system, is optimal when the on-site generated energy is used not only for domestic electricity consumption, but also for the operation of HPs and EHs for heat generation. This study highlights the importance and significance of a DT approach to city block renovation and provides a new solution to accelerate the renovation process and reduce energy consumption in the building sector.

In this new era of energy transition, access to reliable and correctly functioning electricity markets is a huge concern for all economies. The restructuring path taken by most electricity markets involves the movement towards green generation structures and the increasing integration of wind and solar photovoltaic energy sources. Furthermore, it involves the electrification of energy systems, which implies a substantial increase in electricity demand levels. It is also important to add that electricity use has been pivotal in achieving efficient productivity levels in many sectors and is thus crucial to boosting economic activity. Nevertheless, this shift in generation structures has raised several challenges in electricity markets, mainly because the electricity produced from wind and solar photovoltaics is intermittent. In turn, adopting green power sources has been claimed to increase electricity price volatility and thus increase pricing risks. Therefore, to ensure that the right market signals are being sent to investors, the behaviour of electricity prices should be carefully assessed. There are three main types of pricing mechanisms commonly used in electricity markets: zonal, uniform and nodal. This study provides a short literature survey on these three pricing mechanisms. Our analysis has revealed that the assessment of the behaviour of nodal electricity price volatility is rarely studied in the literature. This fact has motivated the exploration of this topic and the consideration of the New Zealand electricity market case. The New Zealand electricity market is an energy-only system with no interconnections with other electricity markets. Furthermore, it has plenty of electricity produced from hydropower, which has a high potential to reduce price volatility through its backup role. The nodal pricing mechanism is complex, and data on it are hard to process. This paper elucidates the main challenges in processing electricity big data. Three different procedures to make this data more useable are described in detail. The main conclusions of this paper highlight the need to access easy-to-manage data and identify certain variables that significantly affect nodal prices for data which are unavailable.

At the current stage of the integration of renewable energy sources into the power systems of many countries, requirements for compliance with established technical characteristics are being applied to power generation. One such requirement is the installed capacity utilization factor, which is extremely important for optimally placing power facilities based on renewable energy sources and for the successful development of renewable energy. Efficient placement maximizes the installed capacity utilization factor of a power facility, increasing energy efficiency and the payback period. The installed capacity utilization factor depends on the assumed meteorological factors relating to geographical location and the technical characteristics of power generation. However, the installed capacity utilization factor cannot be accurately predicted, since it is necessary to know the volume of electricity produced by the power facility. A novel approach to the optimization of placement of renewable energy source power plants and their capacity factor forecasting was proposed in this article. This approach combines a machine learning forecasting algorithm (random forest regressor) with a metaheuristic optimization algorithm (grey wolf optimizer). Although the proposed approach assumes the use of only open-source data, the simulations show better results than commonly used algorithms, such as random search, particle swarm optimizer, and firefly algorithm.

New threats such as the COVID-19 pandemic have brought forth not only threats to human health but also changes to many other sectors of the global economy. Despite strict lockdowns, the highest annual number of global renewable energy installations were completed in 2020, including onshore wind power stations and PV power stations. The development of these two types of renewables is increasing rapidly. Transformations in terms of renewable energy require both governmental and public support; thus, it is important to note that the pandemic did not weaken the public commitment to fight climate change. This article aims to evaluate the actual level of support for renewable energy sources in different countries of the world and how the pandemic has affected public opinion regarding this issue. Our analysis suggests that, regardless of the pandemic, public support for renewable energy remains strong in different regions of the world.

A variety of strategies intended to support environmentally friendly and resource-efficient building processes comprise sustainable construction policies. The limited number of bibliometric analyses in the field may hinder the ability to assess the efficacy and impact of research efforts, impede the potential for collaboration, and even limit the dissemination of best practices. Therefore, the present study aims to analyze the impact of published data on the topic of energy efficiency of buildings using the Web of Science core collection database. We perform a bibliometric analysis and science mapping research that assesses significant parameters for the field. A total of 28,555 papers were analyzed using the VOSviewer program. The data was divided into two periods to determine the evolution of trends in this field. The most prolific countries in this field were China, the United States, and England. Following the analysis of the collaboration maps, it was determined that there is a strong collaborative relationship between these countries in the development of papers. The most prolific papers of the first period were published in Energy Policy and Energy and Buildings, which also ranked first in the second period, followed by Energies. It was observed that the most frequent terms used in literature searches in the field differ according to the periods analyzed. In the beginning, the most frequent term was “energy efficiency and performance”, and between 2011 and 2023, the terms “applied energy” and “renewable and sustainable energy” increased considerably with technological development. The results of this research demonstrate the significant and expanding scientific interest in this area and serve as a valuable asset for researchers studying the energy efficiency of buildings.

Photovoltaic power plants are considered to be environmentally friendly solutions to the production of electricity. Solar energy conversion does not release toxic compounds into the environment. However, the construction of solar power plant components (photovoltaic modules, sup-porting structure, inverter station, electrical installation) is extremely consumptive of energy and materials. Massive volumes of minerals, fossil fuels, and electricity are consumed during the manufacturing process. Efficient management of energy and environmental resources seems to be critical for national policy. It is crucial to admit that the post-consumer management of the components of a photovoltaic power plant is connected with a certain quantity of energy and matter and a negative impact on the natural environment. A life cycle assessment was carried out on a real 2 MW photovoltaic power plant located in the northern part of Poland. The analysis was carried out applying the ReCiPe 2016 model and the Life Cycle Assessment (LCA) approach. The impact of the examined renewable energy system was evaluated using 22 impact categories and 3 emission areas (air, water, soil). Life Cycle Assessment analysis was carried out for 2 post-consumer development scenarios (landfill and recycling). The examination of the collected results reveals that photovoltaic modules are the element causing the most negative environmental repercussions connected to the release of dangerous compounds into the atmosphere. Post-consumer development in the form of recycling would provide major environmental benefits and reduce detrimental environmental consequences across the whole life cycle of the photovoltaic power plant. The obtained research results enabled the formulation of pro-environmental recommendations aimed at the long-term development of the life cycle of solar power plants.

The need for energy has significantly increased in the world in recent years. Various research works were presented to develop Renewable Energy Sources (RESs) as green energy Distributed Generations (DGs) to satisfy this demand. In addition, alleviating environmental problems caused by utilizing conventional power plants is diminished by these renewable sources. The optimal location and size of the DG-RESs significantly affect the performance of Radial Distribution Systems (RDSs) through the fine bus voltage profile, senior power quality, low power losses, and high efficiency. This paper investigates the use of PV (photovoltaic) and (Wind Turbine) WT systems as a DG source in RDSs. This investigation is presented via the optimal location and size of the PV and WT systems, which are the most used DG sources. This optimization problem aims to maximize system efficiency by minimizing power losses and improving both voltage profile and power quality using White Shark Optimization (WSO). This algorithm emulates the attitude of great white sharks when foraging using their senses of hearing and smell. It confirms the balance between exploration and exploitation to discover optimization that is considered as the main advantage of this approach in attaining the global minimum. To assess the suggested approach, three common RDSs are utilized, namely, IEEE 33, 69, and 85 node systems. The results prove that the applied WSO approach can find the best location and size of the RESs to reduce power loss, ameliorate the voltage profile, and outlast other recent strategies. Adding more units provides a high percentage of reducing losses by at least 93.52% in case of WTs, rather than 52.267% in the case of PVs. Additionally, the annual saving increased to USD 74,371.97, USD 82,127.257, and USD 86,731.16 with PV penetration, while it reached USD 104,872.96, USD 116,136.57, and USD 155,184.893 with WT penetration for the 33, 69, and 85 nodes, respectively. In addition, a considerable enhancement in the voltage profiles with the growth of PV and WT units was confirmed. The ability of the suggested WSO for feasible implementation was validated and inspected by preserving the restrictions and working constraints.

To reap the potential of renewable energy investments, many states of the European Union have been enacting policies to attract investments from various actors including citizens. Citizen satisfaction with investments has not been examined so far and, consequently, it is unknown whether the implemented policies are correct. Due to its ability to reveal weaknesses that affect satisfaction, satisfaction analysis may serve as a policy decision making tool, while a higher level of citizen satisfaction may build trustful relationships between citizens and governments and enhance citizen acceptance of renewable energy development plans. The aim of this study was to investigate citizen satisfaction with the state’s actions to facilitate investments in renewable energy sources and to detect the weaknesses of the current investment environment. A representative nationwide citizen sample (n = 1536) was recruited in a country of the European Union (Greece) and the MUlticriteria Satisfaction Analysis (MUSA) method analyzed eight criteria related to RES investments. The analysis indicated a low level of citizen satisfaction, while the most prominent weaknesses involved the licensing process and the policies for mitigating fossil fuel monopolies and improving the competitiveness of renewables. Results from this study point to strategic steps that could address weak policy areas that contribute to low citizen satisfaction.

The stability of electricity service mainly depends on two main factors. One of them is the country’s power plant capacity and electricity imports. Another factor is the network that delivers electricity to consumers. Recently, consumer electricity production has appeared as a third factor due to the spread of renewable energies. The article focuses on the transformation of the structure of Hungary’s electricity sources between 2010 and 2020. We used the concentration indicator to examine the structure of export–import deliveries with neighboring countries. We also analyzed the centralization of Hungary’s electricity-generating units and the composition of their fuels. In this article, we examined the increasingly widespread renewable energies, which are replacing the traditional—mainly fossil fuel—energy carriers. The relationship between coal, natural gas, nuclear, solar, wind, water, and bioenergy, as well as net imports, were analyzed using a Pearson correlation matrix. This article concludes that renewable energies will cause further transformation in the future, both in the structure of export–import and power plants. In electricity imports, green power is increasingly preferred. Electricity from renewable sources will account for an increasing share of electricity production. In the future, electricity production based on non-renewables will move toward power plants with low carbon dioxide emissions. On the other hand, it is also moving in the direction of fast-reacting power plants due to weather-dependent renewables. Annual system load peaks will continue to increase year after year in the future, thereby posing additional challenges to electricity generation and the electricity grid.

Nowadays, the participation of renewable energy sources (RESs) and the integration of these sources with traditional power plants in microgrids (MGs) for providing demand-side power has rapidly grown. Although the presence of RESs in MGs reduces environmental problems, their high participation significantly affects the system’s whole inertia and dynamic stability. This paper focuses on an islanded MG frequency regulation under the high participation of RESs. In this regard, a novel fractional order cascade controller (FOCC) is proposed as the secondary frequency controller. In the proposed FOCC controller structure, a fractional order proportional-integral controller is cascaded with a fractional order tilt-derivative controller. The proposed FOCC controller has a greater degree of freedom and adaptability than integer order controllers and improves the control system’s efficiency. The adjustable coefficients of the proposed controller are tuned via the kidney-inspired algorithm. An energy storage system equipped with virtual inertia is also employed to improve the system inertia. The proposed FOCC controller efficiency is compared with proportional-integral-derivative (PID), tilt-integral-derivative (TID), and fractional order proportional-integral-derivative (FOPID) controllers under different disturbances and operating conditions. The results demonstrate that the presented controller provides better frequency responses compared to the other controllers. Moreover, the sensitivity analysis is performed to show the proposed controller robustness versus the parameters’ changes in the system.

In seaports, low-carbon energy systems and energy efficiency have become increasingly important as a result of the evolution of environmental and climate change challenges. In order to ensure the continued success of seaports, technological advancements must be introduced to a number of systems, such as seaport vehicles, harbor cranes, and the power sources of berthed ships. Harbor areas might need a microgrid to handle these aspects. Typically, microgrids that substitute conventional generator units with renewable energy sources (RES) suffer from system inertia problems, which adversely affect microgrid frequency stability. A load frequency controller (LFC) based on a novel modified proportional integral derivative with filter (MPIDF) is presented in this paper for enhancing the performance of marine microgrid system (MMS). The serval optimization algorithm (SOA), a recent bio-inspired optimization algorithm, is used to optimize the MPIDF controller coefficients. This controller is tested on a marine microgrid containing a number of RES such as wind turbine generators, sea wave energy, and solar generation. The efficacy of the proposed MPIDF controller is verified with respect to other controllers such as PIDF and PI. Similarly, the proposed meta-heuristic algorithm is validated as compared to other algorithms including particle swarm optimization (PSO), ant colony optimization (ACO), and jellyfish swarm optimization (JSO). This study also evaluates the robustness of the proposed controller to different perturbations in step load, changes in system parameters, and other parameter variations.

A significant effort is required to reduce China’s dependency on fossil fuels while also supporting worldwide efforts to reduce climate change and develop hydrogen energy systems. A hydrogen economy must include renewable energy sources (RESs), which can offer a clean and sustainable energy source for producing hydrogen. This study uses an integrated fuzzy AHP–fuzzy TOPSIS method to evaluate and rank renewable energy sources for developing a hydrogen economy in China. This is a novel approach because it can capture the uncertainty and vagueness in the decision-making process and provide a comprehensive and robust evaluation of the alternatives. Moreover, it considers multiple criteria and sub-criteria that reflect the environmental, economic, technical, social, and political aspects of RESs from the perspective of a hydrogen economy. This study identified five major criteria, fifteen sub-criteria, and six RES alternatives for hydrogen production. This integrated approach uses fuzzy AHP to evaluate and rank the criteria and sub-criteria and fuzzy TOPSIS to identify the most suitable and feasible RES. The results show that environmental, economic, and technical criteria are the most important criteria. Solar, wind, and hydropower are the top three RES alternatives that are most suitable and feasible. Furthermore, biomass, geothermal, and tidal energy were ranked lower, which might be due to the limitations and challenges in their adoption and performance in the context of the criteria and sub-criteria used for the analysis. This study’s findings add to the literature on guidelines to strategize for renewable energy adoption for the hydrogen economy in China.

This study introduces the most important energy trends and global food systems, as well as the relationship between the human development index (HDI) and energy supply and the relationship between energy prices and food prices. Based on seven important indicators as variables in 18 relevant countries worldwide, before and after the pandemic, with the help of cluster analysis and comparative analysis, five different primary energy clusters were created and analyzed. Our results prove the high volatility of the composition of these clusters within a short period. Another important finding is that renewable energy sources (RES) are probably not viable options for the largest (developed and developing) countries in the short term. The human development index and food production per capita are the lowest in the renewable energy cluster and the highest in countries dominated by nuclear energy and oil with typically the highest GDP, since they are able to finance the price hike in both food and energy markets. Generally, it can be stated that although there is a relationship between the measured indicators, it is not constant in all cases. Our results and methodology may be a good basis for further research to examine the relationship between the most important relevant indicators in different countries, as well as the effect of a global crisis on strengthening food and energy security.

Recently, energy procurement by renewable energy sources has increased. In particular, as solar power generation has a high penetration rate among them, solar radiation predictions at the site are attracting much attention for efficient operation. Various approaches have been proposed to forecast solar radiation accurately. Recently, hybrid models have been proposed to improve performance through forecasting in the frequency domain using past solar radiation. Since solar radiation data have a pattern, forecasting in the frequency domain can be effective. However, forecasting performance deteriorates on days when the weather suddenly changes. In this paper, we propose a domain hybrid forecasting model that can respond to weather changes and exhibit improved performance. The proposed model consists of two stages. In the first stage, forecasting is performed in the frequency domain using wavelet transform, complete ensemble empirical mode decomposition, and multilayer perceptron, while forecasting in the sequence domain is accomplished using light gradient boosting machine. In the second stage, a multilayer perceptron-based domain hybrid model is constructed using the forecast values of the first stage as the input. Compared with the frequency-domain model, our proposed model exhibits an improvement of up to 36.38% in the normalized root-mean-square error.

Tackling climate change, implementing the principles of sustainable development and a closed-loop economy, and creating an economically and environmentally efficient waste management system are the most serious environmental and economic challenges today. One of the biggest problems with waste is that it causes water, soil, and air pollution. The combination of precipitation and septic processes produces leachates containing heavy metals and acids, which negatively affect surface and groundwater, changing their composition and pH, among other things. According to the Polish waste database, there are more than 2500 waste incineration plants operating worldwide, including more than 500 in Europe, while there are 8 in Poland. The concept of a closed-loop economy is based on the rational use of resources, i.e., less consumption of raw materials and energy by creating a closed loop of processes in which waste becomes raw materials in subsequent production stages. The aim of this study was to develop forecasts of electricity recovery from municipal waste using a prediction method based on an approximating function. The predictions made show that in 2023, the forecast of energy recovery from biodegradable municipal waste will be 6566 TJ. Projections of energy recovery from municipal waste for the future are steadily increasing, with planned recovery in 2030 at 9943 TJ.

The article presents energy, economic and environmental analyses of the possibilities of using alternative, high-efficiency sources of heat and energy for the multi-family residential building located in Wrocław, Poland, in the temperate climate zone characteristic of Central Europe. For conventional, alternative and hybrid heating systems based on renewable energy sources, comparative analyses of final energy demand, demand for non-renewable primary energy, CO2 emissions, investment costs and life cycle costs were carried out. The detailed comparative analyses of the research results led to the formulation of conclusions and recommendations which may serve as guidelines for designers of multi-family residential buildings and investors. The solutions of heating and hot water preparation systems recommended in the article will enable the design and construction of buildings with no negative impact on the environment. Taking into account the economic and environmental analyses, the optimal sources of heat and energy are alternative heating systems based on highly efficient heat pumps supplied from a photovoltaic installation. Such solutions, however, have both technical and legal limitations related to the possibility of their implementation and are generally associated with higher investment costs.

Recent advances in electric grid technology have led to sustainable, modern, decentralized, bidirectional microgrids (MGs). The MGs can support energy storage, renewable energy sources (RESs), power electronics converters, and energy management systems. The MG system is less costly and creates less CO2 than traditional power systems, which have significant operational and fuel expenses. In this paper, the proposed hybrid MG adopts renewable energies, including solar photovoltaic (PV), wind turbines (WT), biomass gasifiers (biogasifier), batteries’ storage energies, and a backup diesel generator. The energy management system of the adopted MG resources is intended to satisfy the load demand of Basra, a city in southern Iraq, considering the city’s real climate and demand data. For optimal sizing of the proposed MG components, a meta-heuristic optimization algorithm (Hybrid Grey Wolf with Cuckoo Search Optimization (GWCSO)) is applied. The simulation results are compared with those achieved using Particle Swarm Optimization (PSO), Genetic Algorithms (GA), Grey Wolf Optimization (GWO), Cuckoo Search Optimization (CSO), and Antlion Optimization (ALO) to evaluate the optimal sizing results with minimum costs. Since the adopted GWCSO has the lowest deviation, it is more robust than the other algorithms, and their optimal number of component units, annual cost, and Levelized Cost Of Energy (LCOE) are superior to the other ones. According to the optimal annual analysis, LCOE is 0.1192 and the overall system will cost about USD 2.6918 billion.

The present and future conditions in the energy market impose extremely high standards to the operation of building energy systems. Moreover, distribution networks face new operational and technical challenges as a result of the rapid penetration of renewable energy sources (RES) and other forms of distributed generation. Consequently, active distribution networks (ADNs) will play a crucial role in the exploitation of smart building prosumers, smart grids, and RES. In this paper, an optimization method for the sustainable operation of active distribution networks hosting smart residential building prosumers, plug-in electric vehicle (PEV) aggregators, and RES was developed. The thermal and electrical loads of the residential buildings were modeled in detail and an aggregation method was implemented to the hosted PEVs. Moreover, smart power dispatch techniques were applied at each building prosumer and PEV aggregator hosted by the active distribution network. Simultaneously, all the operational limitations of the active distribution network, building energy systems, and the hosted PEVs were satisfied. The constrained optimal power flow (OPF) algorithm was exploited to keep the voltages of the hosting distribution network between the permissible bounds. A significant operation cost reduction of 17% was achieved. The developed models were verified through detailed simulation results.

The requirements for the increased penetration of renewable energy sources in electrical power systems have led to a dominance of power electronic interfaces. As a result, short-circuit currents have been reduced by the thermal limitations of power electronics, leading to problems associated with the sensitivity, selectivity, and reliability of protective relays. Although many solutions can be found in the literature, these depend on communications and are not reliable in all grid topologies or under different types of electrical fault. Hence, in this paper, the analysis of ground fault currents and voltages using a wavelet transform in combination with a new algorithm not only detects such ground faults but also allows them to be cleared quickly and selectively in scenarios with low fault current contribution due to a full penetration converter-interface-based generation. To verify and validate the proposed protection system, different ground faults are simulated using an arc ground fault model in a grid scheme based on the IEEE nine-bus standard test system, with only grid-forming power converters as generation sources. The test system is modelled in the MATLAB/Simulink environment. Therefore, the protection relays that verify all the steps established in the new algorithm can detect and clear any ground defect. Simulations are also presented involving different fault locations to demonstrate the effectiveness of the proposed ground fault protection method.

The efficiency of renewable energy sources like PV and fuel cells is improving with advancements in technology. However, maximum power point (MPP) tracking remains the most important factor for a PV-based fuel cell power system to perform at its best. The MPP of a PV system mainly depends on irradiance and temperature, while the MPP of a fuel cell depends upon factors such as the temperature of a cell, membrane water content, and oxygen and hydrogen partial pressure. With a change in any of these factors, the output is changed, which is highly undesirable in real-life applications. Thus, an efficient tracking method is required to achieve MPP. In this research, an optimal salp swarm algorithm tuned fractional order PID technique is proposed, which tracks the MPP in both steady and dynamic environments. To put that technique to the test, a system was designed comprised of a grid-connected proton exchange membrane fuel cell together with PV system and a DC-DC boost converter along with the resistive load. The output from the controller was further tuned and PWM was generated which was fed to the switch of the converter. MATLAB/SIMULINK was used to simulate this model to study the results. The response of the system under different steady and dynamic conditions was compared with those of the conventionally used techniques to validate the competency of the proposed approach in terms of fast response with minimum oscillation.

To achieve the ambitious targets of net-zero greenhouse gas emissions by 2050, there is a need for change in all parts of society, industry, and mobility, as well as in all energy sectors. For this purpose, sector coupling plays a crucial role, e.g., in the form of coupling the electricity with the heat sector using power-to-heat systems. In this article, the effects of the integration of intermittent wind energy via a direct cable, as well as the integration of a boiler into district heating systems powered by a biomass plant and/or a gas boiler, are investigated. Sector coupling in the district heating networks is achieved via the integration of a boiler connected to a local grid station and the use of two air-to-water and two water-to-water heat pumps, which are solely powered by electricity produced by local wind turbines. Furthermore, this work evaluates the economic impacts of the exploding energy prices on the sustainability of district heating systems. Our analysis shows that despite high electricity prices, a reduction in fossil-fuel-based energy generators in the winter season can be determined, and thus a sustainable heat supply can be ensured.

The continuous increase in generation from renewable energy sources, marked by correlated forecast uncertainties, requires specific methodologies to support power system operators in security management. This paper proposes a probabilistic preventive control to ensure N-1 security in presence of correlated uncertainties of renewable sources and loads. By adopting a decoupled linear formulation of the AC load flow equations, the preventive control is decomposed into two subsequent linear programming problems, the former concerning the active power and the latter the voltage/reactive power-related issues. In particular, in the active control problem, the algorithm combines Third Order Polynomial Normal Transformation, Point Estimate Method, and Cornish–Fisher expansion to model the forecast uncertainties and characterize the chance constraints in the problem. The goal is to find the optimal phase shifting transformer tap setting, conventional generation redispatching, and renewable curtailment at the minimum cost to assure the probabilistic fulfillment of N and N-1 security constraints on branch active power flows. The second stage solves another linear programming problem, which aims to minimize the adjustments to generators’ set-point voltages to avoid violations at node voltages and branch-rated limits due to reactive power flows while meeting generator reactive power constraints. Simulations performed on an IEEE test system demonstrate the effectiveness of the proposed security control method in limiting the probability of violating security limits in N and N-1 state, including voltage/reactive power constraints, in presence of correlated uncertainties.

Hybrid installations with respect to renewable energy sources are becoming more popular due to the stringent requirements for the energy efficiency of buildings. Therefore, the thermomodernization of a district-heating substation was proposed. Several scenarios, including different renewable energies (an air–water heat pump versus a heat pump with photovoltaics), different investment financing (equity or bank credit), and different purposes for heating demand (central heating or central heating with ventilation and domestic hot water), were analyzed. The economic aspects involved the calculations of the payback time and net present value, while the ecological and environmental characteristics were weighed using emission reduction. Each of the analyses resulted in different proposed modernization methods. However, taking both factors together, the computations proved that the most profitable was the scenario with energy demand for heating, domestic hot water, and ventilation purposes financed by means of bank credit with a thermomodernization bonus.

Building energy efficiency has grown strong in a context of soaring energy prices, especially in Europe. The use of energy-saving devices strongly influences its improvement, but in many cases, it is far from sufficient., especially if the energy comes from renewable sources with forced production. In the case of buildings, these are usually photovoltaic (PV) sources. For this reason, energy management systems (EMS) are becoming increasingly popular as they allow the increase in self-consumption and reduce the size of energy storage. This article presents analyses of historical energy consumption profiles in selected small- and medium-sized buildings powered by renewable energy sources. The implementation limitations of this type of systems, depending on the profile of the building, were identified and guidelines were presented to assess low-cost solutions dedicated to small buildings and considering the actual conditions of existing systems. Statistical analyzes were conducted for the energy demand profiles of 15 different buildings. The analyzes consisted of the preparation of box plots for each hour of working days and the calculation of the relative standard deviation (RSD) index for annual profiles of 60 min periods. The analyzes showed that the RSD index has low values for commercial buildings (e.g., hospital 7% and bank 15%) and very high values for residential buildings—even over 100%. On this basis, it can be concluded about the usefulness of energy profiles for demand forecasting. The novelty of the proposed method is to examine the possibility of using measurement data as data to forecast energy consumption based on statistical analysis, dedicated to low-cost EMS system solutions.

This paper proposes a multi-objective planning framework for electric vehicle (EV) charging stations in emerging power networks that move towards green transportation electrification. Four cases are investigated to study the impacts of EV integration on environmental and economic requirements. In order to facilitate the installation of EV charging stations, the proposed model is formulated to combine the planning models of renewable energy systems, energy storage systems (ESSs), thyristor-controlled series compensators, and transmission lines into the EV-based planning problem. The first objective function aims to maximize EVs’ penetration by increasing the networks’ capacity to supply charging stations throughout the day, whereas the second objective, on the other hand, emphasizes lowering the carbon dioxide emissions from fossil fuel-based generation units in order to benefit the environment. The third objective is to meet the financial requirements by lowering the initial investment and operating costs of the installed devices. The proposed model is written as a multi-objective optimization problem that is solved using the multi-objective version of the Gazelle optimization algorithm (MGOA). The efficiency of the MGOA was tested by solving a set of four benchmark test functions and the proposed problem. The obtained results demonstrated the MGOA’s superiority in solving multi-objective optimization problems when compared to some well-known optimization algorithms in terms of robustness and solution quality. The MGOA’s robustness was between 20% and 30% and outperformed other algorithms by 5%. The MGO was successful in outperforming the other algorithms in providing a better solution. The Egyptian West Delta Network simulations revealed a 250 MWh increase in the energy supplied to EVs when energy storage was not used. However, storage systems were necessary for shifting EV charging periods away from high solar radiation scenarios. The use of ESS increased greenhouse gas emissions. When ESS was installed with a capacity of 1116.4 MWh, the carbon emissions increased by approximately 208.29 million metric tons. ESS’s role in improving the EV’s hosting capacity grows as more renewables are added to the network. ESS’s role in improving the EV’s hosting capacity rises as more renewables are added to the network.

The purpose of this work is to develop a model for balancing the processes of the generation and consumption of electricity, taking into account the random nature of these processes. The subject of the study is hybrid power systems that use traditional and renewable energy sources and have the properties of a local network. Such systems are sensitive to variable generation modes, and the presence of rapid changes in power requires short time intervals. The presence of wind and solar power plants makes it difficult to ensure a balance of power, which increases the need for intermediate energy storage. The research method is a mathematical modeling of random processes of energy consumption and generation, which allows for the analysis of the current power balancing and the obtaining of the integrated characteristics of the state of energy storage and reuse. The unique goal of the study is to take into account the power gradients and the state of charge of the batteries. The results of the study allow for the comparison of the different configurations of the power system in terms of balance, storage needs, and energy loss. It has been shown that the increase in battery capacity and speed limitations are nonlinearly related to the possibilities of energy conservation and the probability of the incomplete use of the capabilities of the energy storage system.

This work presents a risk-averse stochastic programming model for the optimal planning of hybrid electrical energy systems (HEES), considering the regulatory policy applied to distribution systems in Brazil. Uncertainties associated with variables related to photovoltaic (PV) generation, load demand, fuel price for diesel generation and electricity tariff are considered, through the definition of scenarios. The conditional value-at-risk (CVaR) metric is used in the optimization problem to consider the consumer’s risk propensity. The model determines the number and type of PV panels, diesel generation, and battery storage capacities, in which the objective is to minimize investment and operating costs over the planning horizon. Case studies involving a large commercial consumer are carried out to evaluate the proposed model. Results showed that under normal conditions only the PV system is viable. The PV/diesel system tends to be viable in adverse hydrological conditions for risk-averse consumers. Under this condition, the PV/battery system is viable for a reduction of 87% in the battery investment cost. An important conclusion is that the risk analysis tool is essential to assist consumers in the decision-making process of investing in HEES.

The area of a Microgrid (μG) is a very fast-growing and promising system for overcoming power barriers. This paper examines the impacts of a microgrid system considering Electric Vehicle Grid Integration (EVGI) based on stochastic metaheuristic methods. One of the biggest challenges to slowing down global climate change is the transition to sustainable mobility. Renewable Energy Sources (RESs) integrated with Evs are considered a solution for the power and environmental issues needed to achieve Sustainable Development Goal Seven (SDG7) and Climate Action Goal 13 (CAG13). The aforementioned goals can be achieved by coupling Evs with the utility grid and other RESs using Vehicle-to-Grid (V2G) technology to form a hybrid system. Overloading is a challenge due to the unknown number of loads (unknown number of Evs). Thus, this study helps to establish the system impact of the uncertainties (arrival and departure Evs) by proposing Stochastic Monte Carlo Method (SMCM) to be addressed. The main objective of this research is to size the system configurations using a metaheuristic algorithm and analyze the impact of an uncertain number of Evs on the distribution of residential power in Tripoli-Libya to gain a cost-effective, reliable, and renewable system. The Improved Antlion Optimization (IALO) algorithm is an optimization technique used for determining the optimal number of configurations of the hybrid system considering multiple sources, while the Rule-Based Energy Management Strategy (RB-EMS) controlling algorithm is used to control the flow of power in the electric power system. The sensitivity analysis of the effect parameters has been taken into account to assess the expected impact in the future. The results obtained from the sizing, controlling, and sensitivity analyses are discussed.

In this article, a novel methodology is proposed by utilizing a technique which, in light of the change in the African vulture optimization known as Sine Cosine, adopted an African vulture optimization algorithm (SCaAVOA)-based tilt integral derivative (TID) regulator for the load frequency control (LFC) of a five-area power system with multi-type generations. At first, the execution of the Sine Cosine-adopted calculation is tried by contrasting it with the standard AVOA calculation while considering different standard benchmark functions. To demonstrate the superiority of the proposed SCaAVOA algorithm, the results are contrasted using different standard approaches. In the next stage, the proposed method is used in a five-area thermal power system and is likewise applied to a five-area, ten-unit system comprising different conventional sources as well as some renewable energy sources. The performance analysis of the planned regulator is completed for various system boundaries and loading conditions. It is seen that the said regulator is more viable in comparison to the other standard controllers.

The household as the primary decision-making unit is founded on classical and neoclassical economics. However, household behaviour changes have been noticeable in the last decade, moving towards more green and sustainable patterns, which have been pronounced in EU countries striving for a more significant share of renewable energy sources (RES) in energy consumption. These behaviours can be attributed to sustainable economics and are an essential part of energy transformation, as they are focused on pro-ecological attitudes, considering both financial activities and those related to caring for the environment and future generations. This article aims to segment energy consumers and to determine what attitudes prevailed in the selected segments and to what extent consumers were pro-ecologically oriented when making decisions regarding RES management before the pandemic and the energy crisis outbreak in Europe. We propose a three-segment model for archetyping household energy consumers in Poland by considering the following groups of factors: environmental and energy goods protection (F1), the mirror effect (F2), and energy and devices profitability (F3). The segments are distinguished based on factor analysis and the fuzzy c-means method. The number of segments is determined based on the cluster validity measure. The presented results prove that the F1 factor plays the leading role in each segment. The percentage of positive responses for each segment, including a migrating group of households, oscillates over 80%. It gives strong hope for retaining sustainable attitudes regardless of the pandemic and energy crisis that occurred in 2022 in the EU.

The motivation for this study was the need to extend and supplement the previously conducted research on technical objects in the renewable energy sector with analyses of the environmental impact of the production, operation and post-operational development stages of the wind power plant. The main purpose of the work was to investigate, analyze and assess the ecological effects of a real facility, which is a 2 MW Vestas V90/105 m wind farm, throughout its life cycle. The life cycle assessment analysis of the 2 MW wind power plant was performed using Impact 2002+ modeling. The results are presented for all impact levels and categories. The production stage was characterized by the highest total level of harmful effect. The use of recycling reduces the negative impacts of the life cycle by 6.5%. The investigated technical facility has the greatest negative impact during the production stage, especially in the area of depletion of fossil resources and human health.

Planning for the intensive use of renewable energy sources (RESs) has attracted wide attention to limit global warming and meet future load growth. Existing studies have shown that installing projects such as transmission lines, energy storage systems (ESSs), fault current limiters, and FACTs facilitate the integration of RESs into power systems. Different generation and transmission network expansion planning models have been developed in the literature; however, a planning model that manages multiple types of projects while maximizing the hosting capacity (HC) is not widely presented. In this paper, a novel planning framework is proposed to enhance and control the HC level of RESs by comparing various kinds of renewables, ESSs, fault current limiters, and FACTs to choose the right one, economically and technically. The proposed problem is formulated as a challenging mixed-integer non-linear optimization problem. To solve it, a solution methodology based on a developed decision-making approach and an improved meta-heuristic algorithm is developed. The decision-making approach aims to keep the number of decision variables as fixed as possible, regardless of the number of projects planned. While an improved war strategy optimizer that relies on the Runge-Kutta learning strategy is applied to strengthen the global search ability. The proposed decision-making approach depends primarily on grouping candidate projects that directly impact the same system state into four separate planning schemes. The first scheme relies on the impedance of devices installed in any path to optimally identify the location and size of the new circuits and the series-type FACTs. The second scheme is based on optimally determining the suitable types of ESSs. On the other hand, the third scheme optimizes the reactive power dispatched from the ESSs and shunt-type FACTs simultaneously. The fourth scheme is concerned with regulating the power dispatched from different types of RESs. All of the simulations, which were carried out on the Garver network and the 118-bus system, demonstrated the ability of the investigated model to select the appropriate projects precisely. Further, the results proved the robustness and effectiveness of the proposed method in obtaining high-quality solutions in fewer runs compared to the conventional method.

The general context of this proposal is represented by the energy-efficient smart home that integrates renewable energy sources such as photovoltaic panels. The objective of this article is to minimize the amount of energy consumed from the national energy grid by producer-consumers of energy from renewable sources, in their own smart homes. In order to fulfill this goal, it was necessary to estimate the amount of renewable energy produced on the day-ahead horizon and to schedule the operation of controllable consumers in a smart home. To predict the amount of energy produced, two approaches were used: the first was based on data, and used techniques specific to artificial intelligence, more specifically, multilayer perceptron and radial-basis-function neural networks, and the second was based on models. The accuracy of the short-term prediction horizon of the techniques used was evaluated with quantitative performance indicators so that the most appropriate one in relation to the goal of the article could be selected to be used in the test scenarios. The scheduling of consumer functioning was based on their classification in relation to their ability to be controlled, and on the selection from the peer-reviewed literature of an optimization algorithm which, by load shifting from a smart home, ensured the optimal fulfillment of the objective function. The selected load-shifting algorithm was then integrated into and tested on a real database. The data used were monitored for two representative days, in terms of the amount of energy from renewable energy sources produced and consumed. The load-shifting algorithm proved its effectiveness through the results obtained and which are reported in the article.

Environmentally friendly anaerobic digestion (AD) of lignocellulose-based materials is becoming an increasingly popular alternative to non-renewable energy sources. It also corresponds with the principles of sustainable development. The structure of lignocellulosic materials, which is resistant to biodegradation, requires using pretreatment methods prior to subjecting them to anaerobic processes. The aim of the study was to evaluate the influence of temperature and type of hydrolyzing agent on the efficiency of chemical pretreatment of sugar beet pulp. Biomass samples soaked in distilled water and 0.05 M solutions of NaOH and H2SO4 were left for 20 h, both at room temperature (22 °C) and in a thermostated chamber (50 °C). The changes in pH, electrolytic conductivity (EC), concentration of volatile fatty acids (VFA), dissolved chemical oxygen demand (COD), and phenols were analyzed. The concentration of CODdissolved, demonstrating the efficiency of pretreatment methods, obtained after alkaline hydrolysis conducted at 22 °C was almost at the same level as in the case of acid hydrolysis carried out at 50 °C: 3451 mg dm−3 and 3608.5 mg dm−3, respectively. Hydrolysis carried out in a NaOH solution at 22 °C appears to be the most economical option for sugar beet pulp pretreatment out of all analyzed ones, as there is no need to increase expenditure on heating samples.

Given the importance of renewable energy sources in distribution systems, this article addresses the problem of locating and determining the capacity of these sources, namely, wind turbines and solar panels. To solve this optimization problem, a new algorithm based on the behavior of salp is used. The objective functions include reducing losses, improving voltage profiles, and reducing the costs of renewable energy sources. In this method, the allocation of renewable resources is considered for different load models in distribution systems and different load levels using smart meters. Due to the fact that these objective functions are multi-objective, the fuzzy decision-making method is used to select the optimal solution from the set of Pareto solutions. The considered objective functions lead to loss reduction, voltage profile improvement, and RES cost reduction (A allocating RES resources optimally without resource limitations; B: allocating RES resources optimally with resource limitations). In addition, daily wind, solar radiation, and temperature data are taken into account. The proposed method is applied to the IEEE standard 33-bus system. The simulation results show the better performance of the multi-objective salp swarm algorithm (MSSA) at improving voltage profiles and reducing losses in distribution systems. Lastly, the optimal results of the MSSA algorithm are compared with the PSO and GA algorithms. © 2023 by the authors.

The transportation sector accounts for more than 70% of Nigeria’s energy consumption. This sector has been the major consumer of fossil fuels in the past 20 years. In this study, the technical and economic feasibility of an electrical vehicle (EV) charging scheme is investigated based on the availability of renewable energy (RE) sources in six sites representing diverse geographic and climatic conditions in Nigeria. The HOMER Pro® microgrid software with the grid-search and proprietary derivative-free optimization techniques is used to assess the viability of the proposed EV charging scheme. The PV/WT/battery charging station with a quantity of two WT, 174 kW of PV panels, a quantity of 380 batteries storage, and a converter of 109 kW located in Sokoto provide the best economic metrics with the lowest NPC, electricity cost, and initial costs of USD547,717, USD0.211/kWh, and USD449,134, respectively. The optimal charging scheme is able to reliably satisfy most of the EV charging demand as it presents a small percentage of the unmet load, which is the lowest when compared with the corresponding values of the other charging stations. Moreover, the optimal charging system in all six locations is able to sufficiently meet the EV charge requirement with maximum uptime. A sensitivity analysis was conducted to check the robustness of the optimum charging scheme. This sensitivity analysis reveals that the technical and economic performance indicators of the optimum charging station are sensitive to the changes in the sensitivity variables. Furthermore, the outcomes ensure that the hybrid system of RE sources and EVs can minimize carbon and other pollutant emissions. The results and findings in this study can be implemented by all relevant parties involved to accelerate the development of EVs not only in Nigeria but also in other parts of the African continent and the rest of the world.

Generating energy from renewable sources is becoming more and more popular and widespread. Air source heat pumps are one of the most popular types of heat pumps due to the general availability of a heat source and relatively low investment costs. The aim of the paper was to investigate the impact of the use of air heat pumps on the improvement of energy efficiency of Silesian Botanical Garden facilities in Poland. Year-round numerical analyses of energy consumption were carried out with the use of IDA ICE software, based on data received from the facility administrator. Different variants of the boiler room configuration including air-to-water heat pumps were analysed. The results showed that implementation of heat pumps led to significant decrease of energy consumption in the building and reduction of values of Primary Energy and CO2 emission.

The sustainability of new-generation energy sources has become one of the most critical challenges in recent years as renewable energy sources (RESs) rapidly replace old fossil sources. Integration between RESs and the grid should be completed through power electronics converters and optimized control techniques. RESs have many advantages, such as having increased reliability and sustainability, being environmentally friendly, and having cheaper maintenance costs and more reasonable energy prices. Photovoltaic (PV) panels are among the most popular RESs. A PV array’s generated voltage level is unsuitable for direct load or grid connection and has to be enhanced via a DC-DC boost converter. After that, an inverter should be used to change the generated DC voltage to AC voltage for the grid or loads. In order to reach higher voltage gains, different structures have been proposed in the literature, such as cascaded converters, non-isolated converters (including transformers), and positive- and negative-voltage-lift Luo converters. These converters have some disadvantages, such as including a large number of semiconductor devices and inductors, heavy and bulky structures, and the need for intermediate converters to convert DC to AC voltage and vice versa. Besides the efficiency and high DC voltage gain feature, to achieve more reliability and sustainability and a longer lifetime of the PV source, the current drawn from these sources should be as ripple-free as possible. This study considers all these details by presenting a novel DC-DC power boost converter. The steady-state analysis, simulation, and test results are presented. The most important features of the proposed converter include the lack of need for a transformer, intermediate inverter, rectifier converters, and bulky and heavy components, while still ensuring that high voltage gains and high efficiencies are possible. Simulation results showed that for duty ratios from D = 0.05 to D = 0.15 for the switch S3, the gain of the converter was 22, 35, and 70 times greater than the input voltage, respectively. The desired 200 VDC and 400 VDC voltages for the output nodes were obtained using 12 VDC as the input voltage with and without the switched-capacitor cell, respectively. A limited number of the voltages between −47 and 12 V dropped across the inductors, and a reversed voltage from −12 to −48 V was reported for the power diodes. Additionally, an efficiency close to 96.88% was obtained for the proposed converter. According to the experimental results, a voltage close to 198 VDC was obtained with a 12 VDC input voltage source without using the switched-capacitor cell. A current with a maximum of 7 A was reported for the output diode, and more than 96% efficiency was reported. The results showed that the primary source of the power losses was the semiconductors, and the switching losses made up around 69% and 88% of the total losses for the switches and diodes, respectively. The present topology has three power switches. Two of the switches are activated and deactivated simultaneously. The third switch is activated or deactivated in reverse with the other switches. The results showed that for short-duty ratios such as 0.5 for switches S1 and S2 and 0.35 for switch S3, DC voltage gains close to 35 were obtained theoretically. The generated voltage could be doubled by applying fourth and fifth power switches by making a switched-capacitor-based topology. All of these details are illustrated in this study in detail. The proposed circuit was set up and tested in a laboratory environment. The test results confirm the simulation and theoretical analysis.