Energy System Model Research Articles

Integration of disamenity costs and equality considerations regarding onshore wind power expansion and distribution into energy system optimization models

BackgroundSocial acceptance of energy infrastructure projects affects public support for the energy transition and is essential for the transition’s sustainability and success. Despite extensive research focusing on the social acceptance of renewable energy, and on the acceptance of onshore wind power in particular, energy system models have largely prioritized techno-economic aspects. This focus has created a gap between model results and decision-makers’ needs. In this study, we offer recommendations for integrating disamenity costs and equality considerations—two critical social aspects related to onshore wind power—into energy system optimization. To achieve this, we use a spatially distributed model from a climate-neutral Germany and explore various implementations and trade-offs of these two social aspects.ResultsWe identified effective linear formulations for both disamenity costs and equality considerations as model extensions, notably outperforming quadratic alternatives, which exhibit longer solution times (+ 50–115%). Our findings reveal that the endogenous consideration of disamenity costs in the optimization approach can significantly reduce the human population’s exposure to wind turbines, decreasing the average disamenity per turbine by 53%. Drawing on notions of welfare economics, we employ two different approaches for integrating equality into the optimization process, permitting the modulation of the degree of equality within spatial distributions in energy system models. The trade-offs of the two social aspects compared to the cost-optimal reference are moderate, resulting in a 2–3% increase in system costs.ConclusionsDisamenity costs emerge as a predominant factor in the distribution of onshore wind power in energy system optimization models. However, existing plans for onshore wind power distribution in Germany underscore equality as the driving factor. The inclusion of social aspects in energy system models facilitates the identification of socially superior wind turbine locations. Neglecting disamenity costs and equality considerations leads to an overestimation of the practical solution space for decision-makers and, consequently, the resulting energy system designs.

The effects of fair allocation principles on energy system model designs

Abstract What constitutes socially just or unjust energy systems or transitions can be derived from the philosophy and theories of justice. Assessments of distributive justice and utilising them in modelling lead to great differences based on which justice principles are applied. From the limited research so far published in the intersection between energy systems modelling and justice, we find that comparisons between the two principles of utilitarianism and egalitarianism dominate in assessments of distributive justice, with the latter most often considered representing a 'just energy system'. The lack of recognition of alternative and equally valid principles of justice, resting on e.g. capabilities, responsibilities and/or opportunities, leads to a narrow understanding of justice that fails to align with the views of different individuals, stakeholders and societies. More importantly, it can lead to the unjust design of future energy systems and energy systems analysis. 
 
In this work, we contribute to the growing amount of research on distributive justice in energy systems modelling by assessing the implications of different philosophical views on justice on modelling results. Through a modelling exercise with a power system model for Europe (highRES), we explore different designs of a future (2050) net-zero European electricity system, and its distributional implications based on the application of different justice principles. In addition to the utilitarian and egalitarian approach, we include, among others, principles of 'polluters pay' and 'ability-to-pay', which take historical contributions of greenhouse gas emissions and the socio-economic conditions of a region into account. 
 
We find that fair distributions of electricity generating infrastructure look significantly different depending on the justice principles applied. The results may stimulate a greater discussion among researchers and policymakers on the implications of different constructions of justice in modelling, expansion of approaches, and demonstrate the importance of transparency and assumptions when communicating such results.

Cooperative energy and reserve trading strategies for multiple integrated energy systems based on asymmetric nash bargaining theory

To tackle the issues of cooperative energy and reserve trading as well as fair cooperative benefit allocation among multiple integrated energy systems (IESs), this paper proposes a two-stage cooperative energy and reserve trading model for multiple integrated energy systems (MIESs). Specifically, at day-ahead stage, MIESs aim to maximize their overall profit through cooperative electricity and heat trading. At real-time stage, MIESs trade demand response (DR) reserve to minimize the overall wind power deviation compensation cost. To reduce the complexity in model solution, we transform the model into two sub-problems. In sub-problem 1, we determine the energy and DR reserve trading volumes. Here, distributionally robust optimization (DRO) is utilized to manage the severe uncertainties in wind power distribution. In sub-problem 2, based on the outcomes from sub-problem 1, we settle the energy and DR reserve trading prices. To ensure the fairness of benefit allocation, asymmetric Nash bargaining theory is applied to assess each IES's contributions in trading volumes and profit growth. Interval adaptive alternating direction method of multipliers (IA-ADMM) is used to preserve each IES's privacy. Finally, simulation results demonstrate that, compared with independent operation, cooperative trading among MIESs increases profits for all IESs, thereby motivating their participation in cooperative trading.

Aligning the Western Balkans power sectors with the European Green Deal

Abstract Located in Southern Europe, the Drina River Basin is shared between Bosnia and Herzegovina, Montenegro, and Serbia. The power sectors of the three countries have an exceptionally high dependence on coal for power generation. In this paper, we analyse different development pathways for achieving climate neutrality in these countries and explore the potential of variable renewable energy (VRE) and its role in power sector decarbonization. We investigate whether hydro and non-hydro renewables can enable a net-zero transition by 2050 and how VRE might affect the hydropower cascade shared by the three countries. The Open-Source Energy Modelling System (OSeMOSYS) was used to develop a model representation of the countries' power sectors. Findings show that the renewable potential of the countries is a significant 94.4 GW. This potential is 68% higher than previous assessments have shown. Under an Emission Limit scenario assuming net zero by 2050, 17% of this VRE potential is utilized to support the decarbonization of the power sectors. Additional findings show a limited impact of VRE technologies on total power generation output from the hydropower cascade. However, increased solar deployment shifts the operation of the cascade to increased short-term balancing, moving from baseload to more responsive power generation patterns. Prolonged use of thermal power plants is observed under scenarios assuming high wholesale electricity prices, leading to increased emissions. Results from scenarios with low cost of electricity trade suggest power sector developments that lead to decreased energy security.

Evaluation of influential factors on energy system optimisation

Energy system analysis is gaining more importance in political decisions, science, and society, thus, the basis of interpreting the results is increasingly being questioned. Different approaches have been used in energy system models: Scenario variations, changing base assumptions or sensitivity analyses. A direct comparison of these approaches aims to show that the influence on the results is comparable for all and thus needs to be considered alongside each other. Therefore, this paper uses energy system optimisation within a case study on Germany. It confirms that all approaches greatly influence the results and are thus suitable for discussing uncertainties. Among other things, the sole change of the reference weather year can have a comparable influence on the emissions just like the specification of an entirely different mobility scenario (+44% and +56% respectively). However, the target function (total costs) is significantly more influenced by scenarios. Among the evaluated parameters, the investment costs of renewable technologies and the potential of renewable feedstock affect the system the most. In summary, all three methods provide important insights into the optimisation model and should therefore be used together for validation, discussion and design of energy system models.

On plutonium-241 and americium in the two-component nuclear energy system

The paper deals with the influence of the strategy for using separated plutonium from spent fuel of thermal and fast reactors, as well as homogeneous burning of americium in the BN reactor core on the balance of americium in the Russian nuclear fuel cycle throughout the 21st century. The assessment is carried out with the use of mathematical modeling of nuclear materials movement including nuclide transformations throughout the nuclear energy system based on the CYCLE code. Scenario modeling of the americium and Pu-241 accumulation was carried out in Russia’s two-component nuclear energy system model with thermal (VVER) and fast (BN) reactors. In so doing, spent nuclear fuel (SNF) reprocessing was simulated in 2 options: as priority reprocessing of SNF of VVER reactors (1) or SNF of BN reactors (2). In addition to americium accumulation in the system without burning, the accumulation of this actinide was studied taking into account its homogeneous burning in the MOX-fuel of fast reactors at the level of its equilibrium content of ~1%. It has been shown that the priority reprocessing of VVER spent fuel makes it possible to reduce the americium accumulation by the end of the century by ~8 tons, and the effect is achieved by using freshly separated plutonium with short-term cooling, thus, by priority the americium source is eliminated, without directly handling it. Homogeneous addition of americium to the fuel of fast reactors of the BN-1200 type at a level of ~1% makes it possible to stop accumulation of americium in a two-component energy system by 2070, stabilizing it at a level of ~40 tons in the scenario with priority reprocessing of VVER SNF and ~50 tons in the scenario with priority reprocessing of BN SNF.

Tackling the multitude of uncertainties in energy systems analysis by model coupling and high-performance computing

This paper identifies and addresses three key challenges in energy systems analysis—varying assumptions, computational limitations, and coverage of a few indicators only. First, results depend strongly on assumptions, i.e., varying input data. Hence, comparisons and robust results are hard to achieve. To address this, we use a broad range of possible inputs through an extensive literature review by scenario experts. Second, we overcome computational limitations using high-performance computing (HPC) and an automated workflow. Third, by coupling models and developing 13 indicators to evaluate the overall quality of energy systems in Germany for 2030, we include many aspects of security of supply, market impact, life cycle analysis and cost optimization. A cluster analysis of scenarios by indicators reveals three recognizable clusters, separating systems with a high share of renewables clearly from more conventional sets. Additionally, scenarios can be identified which perform very positive for many of the 13 indicators. We conclude that an automated, coupled workflow on supercomputers based on a broad parameter space is able to produce robust results for many important aspects of future energy systems. Since all models and software components are released as open-source, all components of a multi-perspective model-chain are now available to the energy system modeling community.

Optimising Portugal’s 2050 energy system: Electric vehicles and hydrogen integration using Grey Wolf Optimiser and EnergyPLAN

The global shift to sustainable energy, driven by climate concerns and energy security, is backed by worldwide governmental commitments to reduce greenhouse gas emissions and enhance renewable energy sources in the energy systems. In 2016, the Portuguese Government pledged to carbon neutrality by 2050 as part of this global effort. This article proposes technical solutions for reducing carbon dioxide emissions and increasing renewable energy in the Portuguese mainland energy system by 2050, considering the targets set by the government. An energy system modelling approach based on the Grey Wolf Optimiser and the EnergyPLAN software was introduced. Seven scenarios for 2050 assess the impacts of transport electrification and hydrogen grid integration while minimising costs and/or carbon dioxide emissions. The results show that achieving a nearly 100% renewable electricity system and carbon neutrality is possible, considering a carbon capture ranging from 2.65Mt to 5.87Mt. The 2050 energy system requires a minimum battery storage of 5.1GWh and a minimum natural gas storage of 2,063GWh. Transport electrification requires an increase in the total installed capacity by at least 1.83GW, while hydrogen integration increases it by a minimum of 8.25GW. Developing interconnection capacity beyond 15% of the total installed capacity is crucial for grid stability and reducing fossil fuel dependence.

Between green hills and green bills: Unveiling the green shades of sustainability and burden shifting through multi-objective optimization in Swiss energy system planning

The Paris Agreement is the first-ever universally accepted and legally binding agreement on global climate change. It is a bridge between today’s and climate-neutrality policies and strategies before the end of the century. Critical to this endeavor is energy system modeling, which, while adept at devising cost-effective carbon-neutral strategies, often overlooks the broader environmental and social implications. This study introduces an innovative methodology that integrates life-cycle impact assessment indicators into energy system modeling, enabling a comprehensive assessment of both economic and environmental outcomes. Focusing on Switzerland’s energy system as a case study, the model reveals that optimizing key environomic indicators can lead to significant economic advantages, with system costs potentially decreasing by 15% to 47% by minimizing potential impacts from the current system still operating with fossil technologies to an alternative only relying on renewable and where the impact are mainly related to the construction of the infrastructure. However, a system optimized solely for economic efficiency, despite achieving 63% reduction in carbon footprint compared to 2020, shows a potential risk of burden shift to other environmental issues. The adoption of multi-objective optimization in this approach nuances the exploration of the complex interplay between environomic objectives and technological choices. The results illuminate pathways towards more holistically optimized energy systems, effectively addressing trade-offs across environmental problems and enhancing societal acceptance of the solutions to this century’s defining challenge.

Energy system models should consider evolving charging profiles

Abstract Globally, sales of battery electric vehicles (BEVs) are surpassing records every year, and their growing charging needs will ultimately reshape power infrastructure planning practices. While studies have analyzed the impact of light-duty BEVs on the electricity sector, they have overlooked the prospective evolution in charging profiles. We developed a framework for analyzing passenger vehicle electrification futures accounting for the evolution in charging infrastructure, BEVs technical features, and socio-demographics. We soft-link a BEV charging profiles generator with an energy system optimization model to analyze a light-duty vehicle (LDV) electrification scenario in the U.S. from 2020-2050. Compared to static charging profiles, common in prior work, evolving profiles lead to substantial differences in projected power plant installed capacity (up to ~300 GW more solar PV) and activity (up to ~460 TWh more solar PV generation). Hence, future studies should consider not only different charging profiles (e.g., day, night, uncontrolled) but also how these evolve over time.

Dispatch of Decentralized Energy Systems using Artificial Neural Networks: A Comparative Analysis with Emphasis on Training Methods

Due to the availability of flexibility, Decentralized Energy Systems (DES) play a central role in integrating renewable energies. To efficiently utilize renewable energy, dispatchable components must be operated to bridge the time gap between inflexible supply and energy demand. Due to the large number of energy converters, energy storage systems, and flexible consumers, there are many ways to achieve this. Conventional rule-based dispatch strategies often reach their limits here, and optimized dispatch strategies (e.g., model predictive control or the optimal dispatch) are usually based on very good forecasts. Reinforcement learning, particularly the application of Artificial Neural Networks (ANN), offers the possibility to learn complex decision-making processes. Since long training times are required for this, an efficient training framework is needed. The present paper proposes different training methods to learn an ANN-based dispatch strategy. In Method I, the ANN attempts to learn the solution to the corresponding optimal dispatch problem. In method II, the energy system model is simulated during the training to compute the observation state and operating costs resulting from the ANN-based dispatch. Method III uses the fast executable Method I solution as a warm-up solution for the computationally expensive training with Method II. In the present paper, a model-based analysis compares the different ANN-based dispatch strategies with rule-based dispatch strategies, model predictive dispatch (MPC), and optimal dispatch regarding their computational efficiency and the resulting operating costs. The dispatch strategies are compared based on three case studies with different system topologies for which training and test data are applied.Training method I proved to be non-competitive. However, training methods II and III significantly outperformed rule-based dispatch strategies across all case studies for both training and test data sets. Notably, methods II and III also surpassed MPC-based dispatch strategies under high and medium uncertainty forecasts for the training data in the first two case studies. In contrast, MPC-based dispatch was superior in the third case study, likely due to the higher system’s complexity and in the test data set due to the methodological advantage of being optimized for each specific data set. The effectiveness of training method III depends on the performance of the warm-up training with method I: warm-up is beneficial only if this results in an already promising dispatch (as seen in case study two). Otherwise, training method II proves more effective, as observed in case studies one and three.

Thermoeconomic modeling of new energy system based on biogas upgrading to multigenerational purpose

The following article outlines an integrated approach toward the valorization of biogas for energy, chilled water, hot water, freshwater, and liquid carbon dioxide production. The suggested system includes a biogas upgrading unit with a biomethane-fueled burner, a Kalian cycle, an LNG regasification process, and a multi-effect desalination unit that can provide CO2, electricity, cooling, and heating. The new ideas in the proposed system include a cryogenic biogas upgrading process that can be used in many industrial settings, the production of liquid carbon dioxide through biogas upgrading, and the use of biomethane in the integrated combustion process. Furthermore, the grey wolf optimization technique is applied in various multi-objective optimization contexts to achieve optimal operating conditions and preferred performance outcomes. In this simulation, the generation of electricity reaches a value of 755 kW. Attached to it is a cooling load of about 5102 kW, with a heating load of 4725 kW, freshwater capacity about 1.33 kg/s, production rate of liquid CO2 at 0.58 kg/s, and natural gas at 3.1 kg/s. Thermodynamic results reveal that the proposed multigeneration performance scheme providently enhances energy and exergy efficiencies up to 59.82 % and 5.74 %, respectively, compared to the condition of a single product. The performed exergy analysis shows that the overall irreversibility of the suggested configuration is equal to 18,047 kW while about 42.6 % of this value is contributed from the heat exchanger E-104. Moreover, the CO2 emission intensity of the process can be kept as 0.35 kg/kWh. Cryogenic separation can obtain about 88 % of CO2 in biogas and turn it into its liquid phase. The performed economic assessment has resulted in the following total cost rate as well as cost per unit exergy: 321.38 $/h and 83.06 $/GJ, respectively. Under optimal conditions, an exergy efficiency of 13.40 % and a net power output of 2034.39 kW are attained.

Regional CCUS strategies in the context of a fully decarbonized society

Global strategies to combat climate change must be implemented through actions at the local level. The complexity and interconnection of the global challenges are, however, difficult to interpret at the local level. This requires enhanced collaboration and coordination together with a shared understanding of the interconnected nature of our energy systems and the strategies that shape them. Global, international, and national strategies for carbon capture utilization and storage (CCUS) have emerged in recent years as carbon is increasingly outlined as a key element in the future energy system. This emphasizes CCUS strategies as an important part of a transition toward a fully decarbonized society. Therefore, principal strategies for CCUS and the energy system need to be coordinated and translated following a spatiotemporal assessment to enable meaningful actions at the local level. This article applies a regional perspective, in which a regional energy system model seeks to bridge the gap between national and local energy planning. The regional energy system is developed as part of an overarching national scenario for the transition towards a fully decarbonized society. This context provides a national boundary shaped by a global ceiling to provide a meaningful and adequate framework for regional and local development in the future. The overarching national scenario frames the potentials and constraints of the regional energy system and CCUS strategies as part of a global transition while enabling investigations of potential ramifications from changes in national objectives. The analysis in the article uses the North Denmark Region as a case and investigates scenarios in which the region assumes various roles as a contributor to the national transition. The results indicate a need for more interactive and dynamic energy planning and CCUS strategies to comprehend possible impacts from the various roles and changes in the national objectives. This article suggests that spatial and temporal awareness at the regional level of CCUS strategies as part of a future fully decarbonized society is key to creating a meaningful context for local actions today.

Assessing the energy system's greenhouse emissions via the health of Smart Territories and Cities

The aim of this paper is to understand how the main drivers of air pollution and greenhouse emissions impact public health in a Smart Territory — meaning a technologically integrated city or neighborhood — as well as how to design an optimized energy system model beyond only data by including the holistic experience of its people through phenomenology and ethics. We understand that a Territory and a City is a complex system where mathematical tool modeling known as Design of Experiment (DOE) and its optimization solutions are required to establish causality and identify the variables that have a broader impact on public health so that mortality rates due to air pollution are reduced. DOE's statistical branch is a novel methodology when applied to energy systems and the design of Smart Territories and Cities. Because of it, we have been able to demonstrate how energy emissions, capacity and Levelized Cost of Energy (LCOE) affect the mortality rate and create the foundations for building fully decarbonised energy systems enhanced by mathematical solutions. However, the Cartesian approach of extrapolating from models which align themselves with economic growth alone will not solve the energy dilemma. In fact, the current energy transition project is based on outdated archetypes that lead to scary futures. To course-correct the problematic energy model of a Smart Territory, phenomenologists and creative imagination need to be cultivated and put into action together with mathematical modeling, leading to vectors of positive change and adaptation. Only then might the energy models designed to be reproducible could became ethically virtuous and therefore accepted by the Territory's co-dwellers within the chain of causalities.

Energy Transition Analysis and Climate Action Strategy in the Power Sector: A Case Study of the Java-Bali System

Abstract The transformation of societies over time has led to a marked shift in energy consumption patterns. In the face of rapid industrial growth and a burgeoning population, Indonesia confronts the multifaceted challenge of achieving a balance among energy trilemma: energy security, economic affordability, and environmental sustainability. This study assesses the feasibility of low-carbon energy systems in Indonesia, evaluating current policies and proposing a roadmap for net-zero emissions. Utilizing the Low Emissions Analysis Platform (LEAP) and the Next Energy Modeling System for Optimization (NEMO), the study explores multiple scenarios—namely, Business-as-Usual (BAU), Carbon Capture and Storage Mitigation (CCSM), and New and Renewable Energy Mitigation (NREM). Of these, the NREM scenario, emphasizing the integration of innovative and renewable energy sources, achieves the most substantial reduction in greenhouse gas emissions while maintaining economic viability. This strategy advocates for the comprehensive adoption of new and renewable energy, aiming to approximate zero emissions by 2060. The peak direct cost of production for this scenario is estimated at $270.8 billion. However, each scenario presents its own set of technological, economic, and policy-related challenges, necessitating strategic solutions for a successful transition to sustainable energy systems.

Heat pump and thermal energy storage: Influences of photovoltaic, the control strategy, and price assumptions on the optimal design

Combining heat pump, thermal energy storage, and photovoltaic is a common option to increase renewable energy usage in building energy systems. While research finds that optimal system design depends on the control, design guidelines neglect an influence of (1) photovoltaic, (2) the supervisory control, and (3) prices assumptions on the design of heat pump and thermal energy storage. We analyze how these influences may impact the optimal design. To analyze possible impacts, a pre-screening approach is used to identify relevant model input combinations with a potentially high influence on the optimal design. Applying a simulation-based design optimization with a detailed building energy system model, we optimize three cases: no photovoltaic, no supervisory control, and a state-of-the-art supervisory control. Results indicate that cost optimal design does not change with photovoltaic or the supervisory control, but heat pump size increases with higher electricity prices. For the storage, maximizing the size achieves high self-sufficiency degrees, but cost optimal design changes only for selected price assumptions. Overall, we find that neglecting photovoltaic and surplus control in the system design is a valid assumption. However, price assumptions should be included to allow simple but optimal design rules in current guidelines.

Impacts of lifestyle changes on energy demand and greenhouse gas emissions in Germany

Most energy scenario studies typically focus on technological options and fuel substitution for decarbonising future energy systems. Lifestyle changes are rarely considered, although they can significantly reduce energy demand and climate change mitigation efforts. By using an energy system model, this study shows that it is possible to reduce final energy demand in Germany by 61 % in 2050 relative to 2019 levels, resulting in an annual per capita energy demand of 44 GJ for a representative country of the Global North. This goal can be achieved through a combination of technological measures and lifestyle changes without sacrificing a decent standard of living. Societal chances can eliminate reliance on not-yet-established negative emission technologies, reduce energy dependency, and reduce the need for energy-intensive hydrogen and e-fuels. Downsizing the energy system provides an opportunity for strengthening climate change mitigation, decrease material demand and reduce land use.

Identifying Robust Decarbonization Pathways for the Western U.S. Electric Power System Under Deep Climate Uncertainty

AbstractClimate change threatens the resource adequacy of future power systems. Existing research and practice lack frameworks for identifying decarbonization pathways that are robust to climate‐related uncertainty. We create such an analytical framework, then use it to assess the robustness of alternative pathways to achieving 60% emissions reductions from 2022 levels by 2040 for the Western U.S. power system. Our framework integrates power system planning and resource adequacy models with 100 climate realizations from a large climate ensemble. Climate realizations drive electricity demand; thermal plant availability; and wind, solar, and hydropower generation. Among five initial decarbonization pathways, all exhibit modest to significant resource adequacy failures under climate realizations in 2040, but certain pathways experience significantly less resource adequacy failures at little additional cost relative to other pathways. By identifying and planning for an extreme climate realization that drives the largest resource adequacy failures across our pathways, we produce a new decarbonization pathway that has no resource adequacy failures under any climate realizations. This new pathway is roughly 5% more expensive than other pathways due to greater capacity investment, and shifts investment from wind to solar and natural gas generators. Our analysis suggests modest increases in investment costs can add significant robustness against climate change in decarbonizing power systems. Our framework can help power system planners adapt to climate change by stress testing future plans to potential climate realizations, and offers a unique bridge between energy system and climate modeling.

Modeling of the temperature field in the soil massive for different operating modes of the ground source heat pump

The paper considers modeling the influence of the operating modes of the ground source heat pump for heating and air conditioning systems using geothermal source of energy from borehole on the temperature of the soil formation around the borehole with the account of climate conditions of Ukraine. The proposed methodology of modelling of the ground source heat pump with the account of reversible direction of heat flow for heating and cooling work modes contributes to the field of energy system models. The ultimate goals are to obtain criterion dependencies for calculating the operating parameters of a geothermal borehole and to develop a methodology for designing geothermal boreholes taking into account their long-term operation in a cyclic mode. The idea behind the method allows high spatial and temporal resolution as well as the inclusion of the technical details of the power system and formation the temperature of the soil massive around the borehole for the climate conditions of the Ukraine and South-East Regions of Europe. The novelty of this method is the usage of a parametric approach is chosen to analyze different reversible operating modes of the ground source heat pump for heating and cooling. This provides insights on the systematic effects of ground source heat pump operation with regeneration with limitation on reaching negative temperatures at the bottom of the borehole, which can lead to ice formation, an increase in the volume of soil massive during freezing and destruction of pipelines. The article describes the change in the temperature of the soil mass under the operating conditions of a heat pump in two modes - stationary and with alternating "heat supply - cooling (air conditioning)" for long-term operation. Based on the obtained results it was revealed that when a geothermal borehole operates with an alternating direction of heat flow (with regeneration), the long-term consequences of operation associated with the deviation of the soil massive temperature from the background value are reduced. The additional novelty aspect of modelling was obtained for the operating parameters of a geothermal source of energy with the account of longterm reversible operating mode of the heat pump. The energy balance model is well suited for the analysis of temperature in the bottom part of the geothermal boreholes was revealed for heating and air conditioning systems due to the non-stationary thermal loads determined by climatic conditions of the Ukraine and South-East Regions of Europe. The obtained results may serve as a new approach to optimization technical and economic indicators of geothermal boreholes during operation of the ground source heat pump operating with the account of reversible direction of heat flow for heating and cooling work modes to have continuous improvement of energy efficiency

A holistic analysis of environmental impacts and improvement pathways for the Brazilian electric sector based on long-term planning and life cycle assessment

Brazil presents a high share of renewable electricity generation, mainly due to its favorable hydroelectric potential. However, reports estimate that demand will grow substantially in the long term, requiring a significant diversification of the electricity mix. In this context, this paper analyzes how the environmental impacts of electricity generation tend to change over the horizon 2025–2050 in Brazil and identifies improvement opportunities accounting for environmental and economic factors. To do so, the Open Source Energy Modeling System (OSeMOSYS) is applied to estimate the composition of the electricity mix in 2050. Then, life cycle assessment (LCA) is used to evaluate the associated environmental impacts across ten categories. Lastly, hybrid weighted ɛ-constraint multi-objective optimization (MOO) is employed to evaluate which sources can be used to minimize climate change and the levelized cost of electricity while ensuring that other categories are reasonably restrained. The results indicate that the environmental performance of the Brazilian electricity matrix tends to deteriorate substantially, both when analyzing overall impacts and impacts per kWh. Moreover, the proposed MOO approach suggests that some sources, mainly hydropower, onshore wind, and centralized PV present an all-around economic/environmental performance and might be favorable for expanding the power system.