Energy Prices Research Articles

Pollution Permits and Financing Costs

AbstractEffective environmental policy should consider how the financiers of polluting firms behave. We study phase III of the EU Emission Trading System. Loan spreads for cap‐and‐trade participants are a function of compliance costs, permit market features, and firms’ strategic actions. In contrast with the program intentions, we find that loan spreads fall by approximately 25%. We show that this decrease is almost entirely driven by low permit prices, the firms’ proactiveness to store permits, and imperfect foresight of market conditions in phase III. The drop in spreads cannot be explained by the decline in energy prices and/or other confounding factors.

Adaptive robust self‐scheduling for a wind‐based GenCo equipped with power‐to‐gas system and gas turbine to participate in electricity and natural gas markets

AbstractIntegrating renewable energy, particularly wind generation, into power systems brings significant uncertainty and intermittency, challenging generation companies (GenCos) to maintain economic operations. This paper proposes a strategy for a wind‐based GenCo equipped with a gas turbine and a power‐to‐gas (PtG) system to balance wind variability. The gas turbine offsets power shortfalls by consuming natural gas, while the PtG system absorbs excess wind energy, converting it to natural gas and injecting it into the natural gas network as a form of storage. The GenCo operates in day‐ahead electricity and natural gas markets and the real‐time electricity market, addressing uncertainties from wind output, energy prices, and natural gas access. A tri‐level adaptive robust optimization model is introduced for the GenCo’s short‐term operational scheduling. At the first level, decisions related to the GenCo's participation in the day‐ahead electricity and natural gas markets are made. The second level deals with determining the worst‐case realization of the uncertainties, constrained by the budget of uncertainty and the limited range of variations of uncertain variables. The third level sets the operational strategy on the actual day, considering power and gas balance, as well as constraints for the wind farm, gas turbine, and PtG unit. A column & constraint generation (C&CG) algorithm is used to solve the model. Numerical results demonstrate the model’s effectiveness in various case studies, showing that the GenCo can manage wind uncertainties, benefit from arbitrage opportunities in electricity and gas markets, and improve economic outcomes. This approach supports the broader integration of renewable energy into power systems.

AI-driven decarbonization of buildings: Leveraging predictive analytics and automation for sustainable energy management

The decarbonization of buildings is a pivotal strategy in addressing global climate change, and artificial intelligence (AI) is increasingly recognized as a powerful tool in this process. This paper explores the application of AI in optimizing building energy consumption, focusing on predictive analytics, real-time energy monitoring, and smart energy systems. By leveraging machine learning algorithms, AI enhances energy efficiency through precise automation, particularly in heating, ventilation, and air conditioning (HVAC) systems. AI-powered demand response solutions dynamically adjust energy use based on factors like occupancy, weather patterns, and energy prices, further minimizing carbon footprints. Additionally, the integration of renewable energy sources such as solar and wind with AI-driven energy management systems enables smarter utilization of clean energy. The paper examines both new construction projects and the retrofitting of existing infrastructures, offering insights into AI’s role in reducing carbon emissions and improving energy sustainability. Case studies highlight successful implementations, underscoring AI’s ability to drive significant energy savings and carbon reduction. The study also discusses challenges such as data privacy, the high cost of AI technology, and the regulatory framework required for widespread adoption. The findings present AI as an essential component of the future of sustainable, carbon-neutral buildings.

Modeling and Analyzing Carbon Emission Market Volatility and Impact: Evidence from Guangdong Province, China

This research investigates the volatility of carbon prices in Guangdong’s emission trading market, a critical element of China’s broader climate strategy aimed at reducing greenhouse gas emissions and promoting sustainable development. This study applies ensemble empirical mode decomposition (EEMD) to analyze the complex interactions between carbon price fluctuations and various economic factors, including energy prices and environmental regulations. By decomposing the data, we identify key trends and cycles within the market, providing a clearer understanding of both short-term volatility and long-term market trends. Our findings reveal that regulatory policies play a pivotal role in shaping carbon market dynamics, with shifts in regulations leading to significant price volatility. Additionally, fluctuations in global energy prices, especially oil and coal, are found to have a considerable impact on carbon price movements, further complicating the market’s stability. This underscores the interconnected nature of the carbon trading market with broader economic and environmental factors, both domestic and international. The findings provide valuable insights for policymakers and market participants, underscoring the importance of stable carbon markets for promoting the transition to a low-carbon economy and achieving broader sustainability goals.

An investigation of the impact of energy performance certificate (EPC) ratings on residential property prices in Oxfordshire: a hedonic study

ABSTRACT This paper investigates whether energy performance ratings measured by Energy Efficiency Certificates (EPCs) affect the sales prices of residential properties in Oxfordshire, UK. The study makes use of 186,913 sales transactions that took place between 1995 and 2023. Using a hedonic regression estimation, a nonlinear pattern between energy efficiency levels and the sales prices of dwellings, are observed. The results also show that there is evidence of a significant positive relationship between EPC ratings and detached, semi-detached, terraced houses and flats. In the second part of this study, we examine the effects of age, county and type of dwelling on price premiums. To the best of the authors’ knowledge, this study is the only study, using the most comprehensive literature; and the largest, most up to date available data in analysing the Oxfordshire real-estate market, in terms of energy efficiency and price premiums.

Stochastic Decision-Making Optimization Model for Large Electricity Self-Producers Using Natural Gas in Industrial Processes: An Approach Considering a Regret Cost Function

In the context of high energy costs and energy transition, the optimal use of energy resources for industrial consumption is of fundamental importance. This paper presents a decision-making structure for large consumers with flexibility to manage electricity or natural gas consumption to satisfy the demands of industrial processes. The proposed modelling energy system structure relates monthly medium and hourly short-term decisions to which these agents are subjected, represented by two connected optimization models. In the medium term, the decision occurs under uncertain conditions of energy and natural gas market prices, as well as hydropower generation (self-production). The monthly decision is represented by a risk-constrained optimization model. In the short term, hourly optimization considers the operational flexibility of energy and/or natural gas consumption, subject to the strategy defined in the medium term and mathematically connected by a regret cost function. The model application of a real case of a Brazilian aluminum producer indicates a measured energy cost reduction of USD 3.98 millions over a six-month analysis period.

Driving Forces of the Consumer Price Index During the Crises in the Eurozone: Heterogeneous Panel Approach

This paper examines key driving forces of inflationary pressures, taking into account supply and demand side determinants and actions of policy makers, during the pandemic and geopolitical crises in the Eurozone. Using heterogeneous nonstationary macro-panel models, especially the Mean Group (MG) and Pooled Mean Group (PMG) methods in the period 2020q1–2024q4, it is concluded that the dominant determination of inflationary pressures comes from the supply side. There is a long-run positive equilibrium relationship between the growth of energy prices and the growth of the consumer price index (CPI), and between the index representing supply bottlenecks (SBI) and the growth of CPI, while the relationship with the unemployment rate is insignificant. Also, the existence of a long-run equilibrium between the interest rate and CPI is homogeneous due to the unique monetary policy on a sample, and negative, indicating the efficiency of that policy. However, the speed of adjustment of individual economies is heterogeneous, and in the case of Greece and Ireland, insignificant. The heterogeneous or insignificant response of Eurozone member states, especially related to core-periphery asymmetry, refers to the vulnerability and structural weakness of the Eurozone economies, and the need for deeper integration.

The European Energy Crisis and the Consequences for the Global Natural Gas Market

The Russian invasion of Ukraine in February 2022 led to severe disruptions in the European gas market with significant repercussions on a global scale. The conflict caused a surge in energy prices, a major reshuffling of global natural gas flows, and a shift in the policy-makers’ agendas toward energy supply security. This paper provides a comprehensive review of the developments in the global gas market in the aftermath of the war in Ukraine, focusing in particular on the European gas market and on global LNG trade flows. We first review the characteristics of the gas market in terms of both pricing benchmarks and contractual terms. Next, we analyze the changes to LNG and natural gas production, consumption, and trade flows throughout the European energy crisis. Finally, we review the main policy response to the energy crisis and present some considerations on the gas market outlook. JEL Classification: L95, P28, Q35

Long-Term Effects of Thinning in Sub-Mountainous Thermophilic Sessile Oak (Quercus petraea Mill.) and European Beech (Fagus sylvatica L.) Coppices in the Croatian Dinarides

Coppicing has been neglected in recent decades, leaving gaps in knowledge on silvicultural interventions, especially the long-term effects on coppices of Southeast Europe. The recent economic crisis, the sudden increase in energy prices, and the increased demand for assorted wood products have initiated higher interest in coppices in Croatia. Thus, our work aims to define the long-term effects of thinning in sessile oak (Quercus petraea (Matt.) Liebl.) and European beech (Fagus sylvatica L.) sub-mountainous thermophilic low coppices in the Croatian Dinarides. The experiment includes two localities with thinning and control plots. Thinning was performed in 2002, with 17.71% of wood volume removed in the European beech coppice and 26.09% in the sessile oak coppice. In 2020, 1276 trees were marked, measured (DBH, tree height, number of stems per stump), and assessed for vitality, origin, and six silvicultural features revealing tree quality. In 2022, trees were again measured (DBH, tree height) to gain data on tree growth. Conventional data analysis methods were used. The results show a statistically significant positive long-term effect of thinning on tree growth, stem, and crown features and support findings that thinning, by increasing growth and quality, is a necessary intervention in European beech and sessile oak low coppices. Thinning promoted the healthiest and best quality trees/stems, resulting in a more valuable range of wood products and the potential for higher income. It had a positive impact on tree growth regardless of the tree’s origin (seed or stump) and improved the growth and quality of trees among the supporting tree species. Short-term effects of thinning in trial plots suggested that thinning supported tree species diversity, but this effect diminished over time, showing no positive effect after twenty years. The study points to the need for more frequent thinning in coppices to support tree species diversity (i.e., to allow growth to less competitive tree species) but underlines the need for further research.

Energy Price Distortions and Urban Carbon Emission Efficiency: Evidence from China’s Energy-Intensive Sectors

With a primary focus of achieving carbon neutrality, the energy-intensive industrial sectors (EIIs) contribute to more than half of China’s carbon dioxide (CO2) emissions. During the process of China’s rapid economic development, distorted energy prices gradually became the main obstacle to energy conservation and emission reductions in the EIIs. Therefore, this study focused on determining the mechanisms affecting the energy price distortions of carbon emission efficiency (CEE) in China’s EIIs. Based on a stochastic frontier analysis, the changing trend of CEE in China’s EIIs was evaluated. The channels impacting the energy price distortions of CEE were further analyzed by a mediating effect and moderated mediating effect model. The main contributions and findings include the following: energy price distortions have a significant negative impact on CEE by suppressing technological innovations and hindering the restructuring of energy consumption; governmental environmental regulatory policies mitigate their suppressing effect on technological innovations and reduce the preventing effect by improving the energy consumption structure. The results suggest that market-based price reforms and moderate environmental regulation by the government could help to improve CEE in China’s EIIs. These findings are of great significance for promoting the sustainable development of the energy-intensive industrial sectors and achieving carbon neutrality.

The experiences of unpaid carers of people living with dementia during the cost-of-living crisis: A reflexive thematic analysis.

Some carers have reported struggling to manage the additional costs of caring for someone with dementia, which has negatively impacted upon their financial resilience. Since 2021, this has been compounded by the cost-of-living crisis experienced in the United Kingdom. This crisis has been driven by sharp increases in energy prices and the prices of everyday basics such as food. This study aimed to better understand how unpaid carers, supporting and co-habiting with people living with dementia, experience the cost-of-living crisis, and the impact this has on their ability to provide care for the person living with dementia. Eleven carers supporting and co-habiting with people living with dementia in England, took part in two semi-structured interviews approximately 3 months apart between November 2022 and February 2023. All transcripts were analysed using reflexive thematic analysis. Carers reported having to make difficult and sometimes drastic decisions in reaction to the cost-of-living crisis and the uncertainty of future cost-of-living increases, such as using blankets and extra lays of clothing for them and the person living with dementia in place of using the heating system in their home, going without food so their loved one can eat, or even missing social opportunities. Some carers described aspects which they felt mitigated some of the negative impact of the cost-of-living crisis, such as being able to draw upon financial resources or their local authority providing social events which included a heated space with food. The cost-of-living crisis has led to carers having to make difficult decisions which created worry and anxiety. Findings indicate that many carers need financial support, and it would be beneficial for free social events to be organised which provide a heated space and food, where carers and people living with dementia can socialise with others.

Time Series Forecasting of Thermal Systems Dispatch in Legal Amazon Using Machine Learning

This paper analyzes time series forecasting methods applied to thermal systems in Brazil, specifically focusing on diesel consumption as a key determinant. Recognizing the critical role of thermal systems in ensuring energy stability, especially during low rain seasons, this study employs bagged, boosted, and stacked ensemble learning methods for time series forecasting focusing on exploring consumption patterns and trends. By leveraging historical data, the research aims to predict future diesel consumption within Brazil’s thermal energy sector. Based on the bagged ensemble learning approach a mean absolute percentage error of 0.089% and a coefficient of determination of 0.9752 were achieved (average considering 50 experiments), showing it to be a promising model for the short-time forecasting of thermal dispatch for the electric power generation system. The bagged model results were better than for boosted and stacked ensemble learning methods, long short-term memory networks, and adaptive neuro-fuzzy inference systems. Since the thermal dispatch in Brazil is closely related to energy prices, the predictions presented here are an interesting way of planning and decision-making for energy power systems.

Rationalization of Energy Expenditure: Household Behavior in Poland

Background: The implementation of the EU climate and energy policy, along with changes in the legal environment, has led to a significant increase in energy prices in Poland. Consequently, energy expenditures are now a larger part of household budgets. These rising energy costs and the evolving legal landscape are compelling households to invest in energy-saving solutions and modify their energy consumption habits. This article aims to identify the activities of households in Poland regarding the rationalization of energy expenditures. It formulates the following research hypothesis: households invest in energy-saving appliances to rationalize energy expenditures and/or change their behaviors to reduce energy consumption. Methods: The paper is based on primary research conducted using an online questionnaire survey on a sample of 331 respondents in Poland in March and April 2023. Results: A classification tree algorithm was used to identify the level of investment activities and behavioral changes made by households to reduce energy expenditures. The authors found that low-income households and people who fear further energy price increases are the first of all to change their behaviors for more energy-efficient ones. Medium- and high-income households take investment measures. They replace household appliances with more energy-efficient ones and install heat pumps and photovoltaic panels. These investments are motivated by responsible consumption, environmental protection, cleanliness, and the ease of use of the appliances.

The Causal Nexus Among Energy Dependency, Human Capital, and Renewable Energy: An Empirical Analysis for EU Members

Countries have turned to developing renewable energy production, avoiding the risks posed by the disruptions in global energy trade, the high volatility in energy prices, and the remarkable environmental impairment. Numerous economic, environmental, institutional, and social factors have been put forward as driving factors toward renewable energy. The goal of this research article is to study the causal nexus among energy dependency, human capital, real GDP per capita, CO2 emissions, and renewable energy of the 27 EU members between 2000 and 2020 through Emirmahmutoglu and Kose causality test. The results of the panel-level causality tests demonstrate feedback interplay among energy dependency, human capital, real GDP per capita, CO2 emissions, and renewable energy use. However, the results of the country-level causality analysis unveil that the interplay among renewable energy utilization, energy dependency, real GDP per capita, CO2 emissions, and human capital remarkably varies among EU members. The results of this study suggest that renewable energy investments are significant instruments to make progress in energy security, human capital, real GDP per capita, and CO2 emissions. Furthermore, energy security, human capital, real GDP per capita, and CO2 emissions are significant drivers of renewable energy development.

Developing a robust indicator to evaluate circular economy through reuse strategy: A case study of using water treatment sludge as a coagulant for dewatering of iron ore tailings slurry

Due to the lack of tools, which are simple enough to assess the reuse strategy in the concept of circular economy (CE), in the present paper, reuse efficiency index (REI) was introduced as a new indicator, inspired by previously developed indexes, namely circular economy index (CEI) and value-based resource efficiency (VRE). The REI, as a measure of economic efficiency of reuse strategy, is the ratio of the net value obtained from the reuse of end of life (EOL) product instead of using a new product over the value of materials and energy inputs required to reproduce the EOL product. To calculate REI for any specific EOL product, information regarding mass, physicochemical properties and operational data on the consumption of energy, which are mainly available in the financial archives of companies, is crucial and the market price of EOL product must also be predicted. In another part of the study, the reuse of ferric chloride-based water treatment sludge (FCS) as a coagulant, instead of using high cost polymeric coagulant, for iron ore tailings slurry dewatering was evaluated through REI. The results showed that depending on the market prices of energy and materials as well as the iron (FeCl3) content in the FCS, the REI could vary between 0.51 and 1.04, corresponding to 51–104% added value. Overall, REI is based on accessible information and its simplicity and comprehensibility makes it applicable for managers and policymakers to invest and create technologies for reaching closed-loop cycles in the concept of CE through reuse action.

Optimal Scheduling of Energy Storage and Shiftable Loads in Grid-Connected Residential Buildings with Photovoltaic Micro-Installations

Photovoltaic (PV) systems are becoming increasingly popular, especially in residential buildings. However, the high penetration of prosumer PV micro-installations can have a negative impact on the operation of distribution networks due to the low self-consumption of the energy produced. One way to mitigate this problem is to use a residential energy storage system (RESS) and load shifting under a demand-side management (DSM) scheme. Energy management systems (EMSs) are used to control the operation of RESSs and to implement DSM. There are two main categories of EMSs: rule-based and optimization-based. Optimization-based EMSs provide better results than rule-based EMSs but can be computationally expensive. This article proposes an optimization-based EMS that is designed specifically for residential buildings. The proposed home energy management system (HEMS) uses a particle swarm optimization method to maximize the prosumer’s financial neutrality, which is calculated based on dynamic energy prices. Simulation-based evaluation using the measurements taken in a building equipped with a PV source, RESS, and shiftable loads shows the improved performance of the proposed HEMS compared to rule-based RESS control. The results show that the designed HEMS increases self-consumption, thus reducing the impact of the prosumer’s PV micro-installations on the distribution grid.

Optimal Energy Management of EVs at Workplaces and Residential Buildings Using Heuristic Graph-Search Algorithm

As the adoption of electric vehicles (EVs) continues to rise, efficient scheduling methods that minimize operational costs are critical. This paper introduces a novel EV scheduling method utilizing a heuristic graph-search algorithm for cost minimization due to its admissible nature. The approach optimizes EV charging and discharging schedules by considering real-time energy prices and battery degradation costs. The method is tested on systems with solar generation, electric loads, and EVs featuring vehicle-to-grid (V2G) connections. Various charging rates, such as standard, fast, and supercharging, along with uncertainties in EV arrival and departure times, are factored into the analysis. Results from various case studies demonstrate that the proposed method outperforms popular heuristic optimization techniques, such as particle swarm optimization and genetic algorithms, by 3–5% for different real-time energy prices. Additionally, the method’s effectiveness in reducing operational costs for workplace EVs is confirmed through extensive case studies under varying uncertain conditions. Finally, the system is implemented on a digital real-time simulator with DNP3 communication, where real-time results align closely with offline simulations, confirming the algorithm’s efficacy for real-world applications.

Influencing Factors and Formation Mechanism of Carbon Emission Rights Prices in Shanghai, China

With the intensification of global climate change issues and increasingly stringent carbon emission controls, carbon emissions trading has become a crucial market-based mechanism for reducing carbon emissions. A deeper understanding of the factors influencing carbon emission rights prices and their formation mechanisms can enhance market transparency and effectiveness and promote the healthy development of the carbon trading market. This paper identifies the main factors affecting carbon emission rights prices and explores their formation mechanisms from both the internal and external perspectives of enterprises. Using a vector autoregression (VAR) model, it quantifies the impacts of energy prices, the climate, and technology levels on carbon trading prices in Shanghai, China. The results show that, in the short term, enterprise technology levels and energy prices significantly impact carbon emission rights prices, while the climate becomes more critical in the medium to long term. These findings propose specific policy recommendations for improving the carbon emissions trading market, with a focus on these three factors.

Smart Grid Forecasting with MIMO Models: A Comparative Study of Machine Learning Techniques for Day-Ahead Residual Load Prediction

With the fast expansion of intermittent renewable energy sources in the upcoming smart grids, simple and accurate day-ahead systems for residual load forecasts are urgently needed. Machine learning strategies can facilitate towards drastic cost minimizations in terms of operating-reserves avoidance to compensate the mismatches between the actual and forecasted values. In this study, a multi-input/multi-output model is developed based on artificial neural networks to map the relationship between different predictor inputs, including time indices, weather variables, human activity parameters, and energy price indicators, and target outputs such as wind and photovoltaic generation. While the information flows in only one direction (from the predictor nodes through the hidden layers to the target node), benchmark training algorithms are employed and assessed under different case studies. The model is evaluated under both parametric and non-parametric formulations, namely neural networks and Gaussian process regression. Essential improvements are achieved by enhancing the number of embedded predictors, while superior performance is observed by using Bayesian regularization mechanisms. In terms of mean-error indices and determination coefficient, this opens the pathway towards minimization via Bayesian inference-based approaches in the presence of increased and highly stochastic renewable inputs.

The Determinants of Household Water Demand: A Focus on Water, Energy, and Food Prices

The Determinants of Household Water Demand: A Focus on Water, Energy, and Food Prices