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In the context of the continuous development of new energy vehicles, an increasing number of electric vehicles (EVs) are being integrated into microgrids, which impacts the operation of microgrids. It is necessary to analyze the emission scheduling of microgrids connected with EVs to ensure the smooth and reliable operation of microgrids with EV integration. This paper aims to realize optimal microgrid scheduling. This article took the case of EV connection with microgrids through orderly charging and discharging. Firstly, mathematical models for each output unit in the microgrid were established. Then, aiming to minimize both the operational cost and pollution emission treatment cost of the microgrid, an emission dispatch optimization model was developed. The whale optimization algorithm (WOA) was chosen as the solving algorithm, and an improved WOA (IWOA) was obtained by optimizing the original WOA in terms of population initialization and position updating parameters. An example analysis revealed that the IWOA demonstrated superior optimization performance compared to other optimization algorithms. In the scenario of orderly charging and discharging, the EVs discharged during peak hours and charged during off-peak hours to balance the microgrid load. The operating cost obtained through the IWOA was 967.25 yuan, and the pollution emission treatment cost was 241.52 yuan, resulting in a total cost of 1208.77 yuan. These results confirm the reliability of the proposed IWOA in solving the model and its applicability in optimizing microgrids with EV integration. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract In the context of the continuous development of new energy vehicles, an increasing number of electric vehicles (EVs) are being integrated into microgrids, which impacts the operation of microgrids. It is necessary to analyze the emission scheduling of microgrids connected with EVs to ensure the smooth and reliable operation of microgrids with EV integration. This paper aims to realize optimal microgrid scheduling. This article took the case of EV connection with microgrids through orderly charging and discharging. Firstly, mathematical models for each output unit in the microgrid were established. Then, aiming to minimize both the operational cost and pollution emission treatment cost of the microgrid, an emission dispatch optimization model was developed. The whale optimization algorithm (WOA) was chosen as the solving algorithm, and an improved WOA (IWOA) was obtained by optimizing the original WOA in terms of population initialization and position updating parameters. An example analysis revealed that the IWOA demonstrated superior optimization performance compared to other optimization algorithms. In the scenario of orderly charging and discharging, the EVs discharged during peak hours and charged during off-peak hours to balance the microgrid load. The operating cost obtained through the IWOA was 967.25 yuan, and the pollution emission treatment cost was 241.52 yuan, resulting in a total cost of 1208.77 yuan. These results confirm the reliability of the proposed IWOA in solving the model and its applicability in optimizing microgrids with EV integration.

Electricity prices have been exceptionally volatile in recent years. In 2022, we witnessed a sharp increase in electricity prices in many countries. Several factors contributed to this, including reduced electricity production by hydropower plants due to climatic conditions and geopolitical situations around the world, amongst other factors. The research presented in this paper aims to analyze electricity price data and determine the parameters CPS (Capacity Payment Scheme) and CRS (Capacity Remuneration Scheme). These parameters are calculated from hourly data on a monthly basis from 2018 to the present. Determining these parameters provides a clearer understanding of the efficiency of utilizing photovoltaic power plants. However, the results indicate that identical CRS values can signify different situations in the electricity market. Given the current support for projects utilizing photovoltaic energy, the research findings may offer insights into the future direction of photovoltaic energy utilization. If CRS and CPS show positive trends (e.g., increasing values), this may suggest that current political measures (subsidies, supports, etc.) are effective. Policymakers could then strengthen or expand these measures. The research outcomes could shape the geopolitical situation in individual states. The research results show how differences in electricity prices in 2022 compared to pre-2022 affected CSP and CRS parameters. The findings also indicate that electricity prices vary across different countries, which naturally impacts the calculation of CPS and CRS. In 2024, a significant decline in CRS was observed across all analyzed countries, which may indicate issues with integrating solar energy into the market, market saturation, or changes in market dynamics. [ABSTRACT FROM AUTHOR]

Electricity prices can be an important driver of inflation. However, the transmission from wholesale electricity price to inflation depends on the type of pricing mechanism in the retail market. Using the introduction of Real-Time Pricing (RTP) in the Spanish retail electricity market, we analyze the transmission of wholesale electricity price shocks to inflation dynamics and how this transmission is affected by the introduction of RTP. The main findings are that—after RTP was rolled out—electricity price shocks display a swifter transmission to inflation while the maximum impact is around three times higher than in the previous period. This higher transmission is not only generated by a more pronounced reaction of the retail electricity price but is also caused by a greater transmission of these shocks to core inflation. These results imply that introducing RTP impacts inflation dynamics and, therefore, affects how central banks should react to this type of shock. JEL Classification: E31, Q43, Q48 [ABSTRACT FROM AUTHOR]

Fuel cell electric buses (FCBs) have proven to be a technically viable solution for transportation, owing to various advantages, such as reliability, simplicity, better energy efficiency, and quietness of operation. However, large-scale adoption of FCBs is hindered by the lack of extensive and structured infrastructure and the high cost of clean hydrogen. Many studies agree that one of the significant contributors to the lack of competitiveness of green hydrogen is the cost of electricity for its production, followed by transportation costs. On the one hand, to reduce the investment cost of the electrolyzer, high operating hours should be achieved; on the other, as the number of operating hours decreases, the impact of the electricity costs declines. This paper presents an innovative algorithm for a scalable hydrogen refuelling station (HRS) capable of successfully matching and identifying the most cost-efficient levelized cost of hydrogen (LCOH) produced via electrolysis and connected to the grid, based on the HRS components' cost curves and the hourly average electricity price profile. The objective is to identify the least-cost range of LCOH by considering both the electric energy and the investment costs associated with a hydrogen demand given by different FCB sizes and electrolyzer rated powers. In addition, sensitivity analyses have been conducted to quantify the technology cost margins, and a cost comparison between the refuelling of an FCB fleet and the recharging infrastructure required for an equivalent fleet of Battery Electric Buse (BEB) has been performed. An LCOH of around 10.5 €/kg varying from 12 €/kg (2 FCB) to 10.2 €/kg (30 FCB) has been found for the best-optimized configurations. The final major conclusion of this paper is that FCB technology is currently not economically competitive. Still, a cost contraction of the electric energy price and the electrolyzer capital investment would lead to a 50% decrease in the LCOH. Furthermore, increasing renewable energies into the grid may shift the electricity cost curve, resulting in higher prices when the BEB recharging demand is more significant. This impact, in addition to the peak power load and longer recharging times, might contribute to bridging the gap with FCBs. • Techno-economic model for the sizing of an HRS with grid-connected electrolyzer. • The widespread diffusion of H2 technology in mobility needs both reliability and cost-effectiveness. • Investigation of various electrolysers rated powers and fuel cell buses fleet size. • An action on both CAPEX and OPEX would lead to potential cost margins improvement. • It is found that the best configuration that balances the CAPEX and OPEX coincides with an LCOH ranging from 12 to 10 €/kg. [ABSTRACT FROM AUTHOR]

The imbalance cost pass-through (ICPT) is a flexible component of the incentive-based regulation (IBR) that empowers power producers to adjust tariffs in response to variable fuel prices, thereby enhancing the economic resilience of electricity generation. In Malaysia, the Energy Commission has conducted biannual reviews of fuel and other generation costs. Any cost savings or increases identified during these reviews will be passed on to customers in the form of rebates or surcharges. Meanwhile, if an increment in the ICPT price signal can be provided to electricity providers and consumers, early preparation for operation budgeting can be realised, and energy management program development can be properly prepared. Due to this reason, this study proposes ICPT price forecasting for the electricity market in Peninsular Malaysia that will benefit the stakeholders. The study aims to construct an ICPT-related baseline model for the peninsular generation data by employing three forecasting methods. The forecasting performance is analysed using the mean absolute percentage error (MAPE). In light of our findings, the ARIMA method is one of the most accurate forecasting methods for fuel prices compared to the moving average (MA) and LSSVM methods. The observed price differences between the ARIMA and LSSVM models for ICPT are minimal. The ICPT price for July–December 2022 and January–June 2023 is MYR 0.21/kWh for the ARIMA and MYR 0.18/kWh for LSSVM, which are close to the actual TNB's ICPT tariff. As for forecasting, the ICPT price is expected to drop in the next announcement. The findings of this study may have a positive impact on the sustainability of the energy sector in Malaysia. [ABSTRACT FROM AUTHOR]

As the share of renewable energy sources, which are weather dependent and consequently volatile, continues to grow, it becomes increasingly important to explore strategies for organising both electricity production and consumption to ensure system stability. People's flexibility in their energy consumption is one option to regulate the system. To better understand people's willingness to align their electricity-consuming activities with a flexible pricing system, an online survey with 962 respondents was conducted. The analysis focused on the factors influencing their willingness to shift electricity-consuming activities away from peak hours, as well as the maximum shift duration of using certain devices. The results indicate that people with more flexible lifestyles and those who perceive shifting activities as taking less effort are more willing to shift their activities and indicate longer shift durations. The data also show that attitudes towards the environment, as well as financial, ecological, and motivational factors, play a role in explaining the variance in the willingness to shift and the shift duration. To conclude, increasing flexibility in everyday life could make a valuable contribution to the optimal use of electricity resources. [ABSTRACT FROM AUTHOR]

The cloud computing platform has become a favorable destination for running cloud workflow applications. However, they are primarily complicated and require intensive computing. Task scheduling in cloud environments, when formulated as an optimization problem, is proven to be NP-hard. Thus, efficient task scheduling plays a decisive role in minimizing energy costs. Electricity prices fluctuate depending on the vending company, time, and location. Therefore, optimizing energy costs has become a serious issue that one must consider when building workflow applications scheduling across geographically distributed cloud data centers (GD-CDCs). To tackle this issue, we have suggested a dual optimization approach called electricity price and energy-efficient (EPEE) workflow scheduling algorithm that simultaneously considers energy efficiency and fluctuating electricity prices across GD-CDCs, aims to reach the minimum electricity costs of workflow applications under the deadline constraints. This novel integration of dynamic voltage and frequency scaling (DVFS) with energy and electricity price optimization is unique compared to existing methods. Moreover, our EPEE approach, which includes task prioritization, deadline partitioning, data center selection based on energy efficiency and price diversity, and dynamic task scheduling, provides a comprehensive solution that significantly reduces electricity costs and enhances resource utilization. In addition, the inclusion of both generated and original data transmission times further differentiates our approach, offering a more realistic and practical solution for cloud service providers (CSPs). The experimental results reveal that the EPEE model produces better success rates to meet task deadlines, maximize resource utilization, cost and energy efficiencies in comparison to adapted state-of-the-art algorithms for similar problems.

The imbalance cost pass-through (ICPT) is a flexible component of the incentive-based regulation (IBR) that empowers power producers to adjust tariffs in response to variable fuel prices, thereby enhancing the economic resilience of electricity generation. In Malaysia, the Energy Commission has conducted biannual reviews of fuel and other generation costs. Any cost savings or increases identified during these reviews will be passed on to customers in the form of rebates or surcharges. Meanwhile, if an increment in the ICPT price signal can be provided to electricity providers and consumers, early preparation for operation budgeting can be realised, and energy management program development can be properly prepared. Due to this reason, this study proposes ICPT price forecasting for the electricity market in Peninsular Malaysia that will benefit the stakeholders. The study aims to construct an ICPT-related baseline model for the peninsular generation data by employing three forecasting methods. The forecasting performance is analysed using the mean absolute percentage error (MAPE). In light of our findings, the ARIMA method is one of the most accurate forecasting methods for fuel prices compared to the moving average (MA) and LSSVM methods. The observed price differences between the ARIMA and LSSVM models for ICPT are minimal. The ICPT price for July–December 2022 and January–June 2023 is MYR 0.21/kWh for the ARIMA and MYR 0.18/kWh for LSSVM, which are close to the actual TNB’s ICPT tariff. As for forecasting, the ICPT price is expected to drop in the next announcement. The findings of this study may have a positive impact on the sustainability of the energy sector in Malaysia.

This research aims to identify suitable procedures for determining the size of risks to predict the tendency of electricity prices to return to their historical average or mean over time. The goal is to quantify the sensitivity of electricity prices to different types of shocks to mitigate price volatility risks that affect Portugal's energy market. Hourly data from the beginning of January 2016 to December 2021 were used for the analysis. The symmetric and asymmetric GARCH model volatility, as a function of past information, help to eliminate excessive peaks in data fluctuations. The asymmetric model includes additional parameters to separately obtain the impact of positive and negative shocks on volatility. The MSGARCH model is estimated to be in two states, allowing for transitions between low- and high-volatility states. This approach effectively represents the significant impact of shocks in a high-volatility state, indicating an acknowledgment of the lasting effects of extreme events on financial markets. Furthermore, the MSGARCH model is designed to obtain the persistence of shocks during periods of elevated volatility. Accurate price forecasting aids power producers in anticipating potential price trends and allows them to adjust their operations by considering the overall stability and efficiency of the electricity market. [ABSTRACT FROM AUTHOR]

Abstract This study examines the short- and long-term impacts of various factors on the volatility and price of electricity in Cameroon, including hydroelectric power generation, economic growth, energy demand, and exchange rates from 2000 to 2019. The study uses an autoregressive distributed lag model. The study found that increasing hydroelectric power generation has both positive and negative impacts on electricity prices in the short and long term. While increasing the share of hydropower in overall energy production results in increased variation of electricity costs in the short term, it leads to significant price reductions in the long run. The study also found that economic growth has a considerable positive impact on the variation of power prices, while energy demand has a negative but insignificant effect on price volatility in the short term. Further, the study indicates that measures, such as encouraging SME engagement in renewable energy production, could improve the participation of local enterprises in the power industry and reduce the volatility of electricity prices. On the other hand, the study suggests that exchange rates could have a negative impact on electricity prices in the short term, but depreciation of the local currency could lower fuel costs and improve the availability of power. Overall, the study provides insights that can inform policymakers, energy regulators, and investors in making decisions that contribute to the efficient and sustainable development of Cameroon's electricity market. The study also highlights the need to prioritize power generation to stimulate economic growth and private investment while promoting renewable energy production.

This study examines the short- and long-term impacts of various factors on the volatility and price of electricity in Cameroon, including hydroelectric power generation, economic growth, energy demand, and exchange rates from 2000 to 2019. The study uses an autoregressive distributed lag model. The study found that increasing hydroelectric power generation has both positive and negative impacts on electricity prices in the short and long term. While increasing the share of hydropower in overall energy production results in increased variation of electricity costs in the short term, it leads to significant price reductions in the long run. The study also found that economic growth has a considerable positive impact on the variation of power prices, while energy demand has a negative but insignificant effect on price volatility in the short term. Further, the study indicates that measures, such as encouraging SME engagement in renewable energy production, could improve the participation of local enterprises in the power industry and reduce the volatility of electricity prices. On the other hand, the study suggests that exchange rates could have a negative impact on electricity prices in the short term, but depreciation of the local currency could lower fuel costs and improve the availability of power. Overall, the study provides insights that can inform policymakers, energy regulators, and investors in making decisions that contribute to the efficient and sustainable development of Cameroon's electricity market. The study also highlights the need to prioritize power generation to stimulate economic growth and private investment while promoting renewable energy production. [ABSTRACT FROM AUTHOR]

In the context of national efforts to promote country-wide distributed photovoltaics (DPVs), the installation of distributed energy storage systems (DESSs) can solve the current problems of DPV consumption, peak shaving, and valley filling, as well as operation optimization faced by medium-voltage distribution networks (DN). In this paper, firstly, a price elasticity matrix based on the peak and valley tariff mechanism is introduced to establish a master–slave game framework for DN-DESSs under the DPV multi-point access environment. Secondly, the main model optimizes the pricing strategy of peak and valley tariffs with the objective of the lowest annual operating cost of the DN, and the slave model establishes a two-layer optimization model of DESSs with the objective of the maximum investment return of the DESSs and the lowest daily operating costs and call the CPLEX solver and particle swarm optimization algorithm for solving. Finally, the IEEE33 node system is used as a prototype for simulation verification. The results show that the proposed model can not only effectively reduce the operating cost of the distribution network but also play a role in improving the energy storage revenue and DPV consumption capacity, which has a certain degree of rationality and practicality. [ABSTRACT FROM AUTHOR]

Copyright of Bulletin of The Mineral & Energy Economy Research Institute of the Polish Academy of Sciences / Zeszyty Naukowe Instytutu Gospodarki Surowcami Mineralnymi Polskiej Akademii Nauk is the property of Mineral & Energy Economy Research Institute of the Polish Academy of Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Air conditioning (AC) is a key driver to produce electricity consumption, particularly in residential buildings. In Indonesia, the electricity bill during pandemic covid-19 has increased to a high level on hot days. This is because during these periods many consumers applied their AC for a long time at the same time. It is therefore essential to develop an innovative model to control the electricity peak price and keep a comfortable room during hot days. Many previous studies have confirmed that applied evaporative cooling or other technologies can minimize the whole consumption and cost. This research aims to design a pre-cooling model (PM) for AC to avoid high electricity prices at midday during hot days. To achieve this goal a mathematical model was developed to define the energy cost (EC) and total cost (TC) for AC during the hot season. Under numerical optimization, the EC can be minimized when a spike may occur due to increased temperature at midday for a half hour (0.5 h) and one hour (1 h) spike cases. To justify this model, a PM was applied for two different building characteristics. As a result, the benefits of a PM if a spike occurs for 0.5 h and 1 h were achieved, such as IDR 10,061 (30.43 %) and IDR 10,693 (24.44 %) for building-1; IDR 12,071 (37.40 %) and IDR 14,844 (34.97 %) for building-2. In addition, the TC considering the probability of a spike of 0.5 h and 1 h if the spike occurs every five minutes were IDR 45,247 and IDR 38,287 for building-1 and building-2, respectively.

Electricity prices have been exceptionally volatile in recent years. In 2022, we witnessed a sharp increase in electricity prices in many countries. Several factors contributed to this, including reduced electricity production by hydropower plants due to climatic conditions and geopolitical situations around the world, amongst other factors. The research presented in this paper aims to analyze electricity price data and determine the parameters CPS (Capacity Payment Scheme) and CRS (Capacity Remuneration Scheme). These parameters are calculated from hourly data on a monthly basis from 2018 to the present. Determining these parameters provides a clearer understanding of the efficiency of utilizing photovoltaic power plants. However, the results indicate that identical CRS values can signify different situations in the electricity market. Given the current support for projects utilizing photovoltaic energy, the research findings may offer insights into the future direction of photovoltaic energy utilization. If CRS and CPS show positive trends (e.g., increasing values), this may suggest that current political measures (subsidies, supports, etc.) are effective. Policymakers could then strengthen or expand these measures. The research outcomes could shape the geopolitical situation in individual states. The research results show how differences in electricity prices in 2022 compared to pre-2022 affected CSP and CRS parameters. The findings also indicate that electricity prices vary across different countries, which naturally impacts the calculation of CPS and CRS. In 2024, a significant decline in CRS was observed across all analyzed countries, which may indicate issues with integrating solar energy into the market, market saturation, or changes in market dynamics.

The first part of the paper includes a discussion on the impact the energy crisis had on the German power industry and what measures the government has taken to ensure a reliable energy supply. Reliability, efficiency and sustainability make up the «Triangle of Aims» of the energy policy of a country. The second half of the paper is dedicated to the measures that the federal government took during the acute phase of the energy crisis in terms of the second aim. We discuss three main aspects. Firstly, we show the changes in the structure of pricing for two categories of consumers: householders and non-household consumers. Secondly, we demonstrate the formation of the price on the electricity spot market. Lastly, we elaborate on the ways the government could influence the level of prices and to what extent these possibilities have been used in accordance with the rules of the European Union. The crisis showed the readiness of the government to take costly measures such as zeroing the taxes that are due under the Renewable Energy Act, giving a lump sum payment to the employed, the students and the pensioners, refusing to increase the cost of CO2 emission certificates, providing business benefits and subsidies and enforcing a gas and electricity price brake. For the period after the end of the «price brake», the government is considering subsidizing prices for export-oriented production that implements the climate goals. These plans confirm the existence of the «shadow budget» that was formed for overcoming the consequences of the pandemic.

In comparison to other countries, South Korea has a high reliance on energy, with the majority of its electricity being generated by a government-run company to ensure a stable and affordable supply. Unlike the ''pay-as-bid'' pricing approach, South Korea utilizes the system marginal price (SMP), also known as ''pay-as-clear.'' Accurate SMP forecasting is crucial for guaranteeing steady economic growth because manufacturing is South Korea's flagship industry and accounts for 50 % of the nation's power consumption. In this study, a combination of machine learning-based batch learning and online learning techniques were employed to forecast the SMP in South Korea, utilizing a dataset consisting of five energy sectors, two financial sectors, and one transportation sector. The analysis of the F-value revealed that the coal sector, which is one of the energy sectors, had the most significant influence on SMP indicating the greatest score of 2,328. In this study, three machine learning models, namely support vector regression, simple deep neural network, and deep neural network, were suggested and compared for batch learning to determine the best-trained model. The evaluation metrics were used to assess the performance of these models. Based on the results obtained, the simple deep neural network was found to outperform the other models in terms of accuracy. Furthermore, two methods such as weight modification and time interval updating between inputs and output were employed for online learning based on the trained batch model. Upon the implementation of model updates, an ongoing assessment of its performance transpired utilizing the metrics of coefficient of determination, root mean square error, mean absolute error, and mean absolute percentage error. The average values for these metrics were observed to be 0.924, 7.991, 5.035, and 0.052, respectively. This study is expected to provide direct assistance in the formulation of energy plans for decision-makers in the industrial sector.

Copyright of Journal of Soochow University Philosophy & Social Sciences Edition is the property of Soochow University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The current need to reduce carbon emissions makes hydrogen use essential for self-consumption in microgrids. To make a profitability analysis of a microgrid, the influence of equipment costs and the electricity price must be known. This paper studies the cost-effective electricity price (EUR/kWh) for a microgrid located at ''La Rábida Campus" (University of Huelva, south of Spain), for two different energy-management systems (EMSs): hydrogen-priority strategy and battery-priority strategy. The profitability analysis is based, on one hand, on the hydrogen-systems' cost reduction (%) and, on the other hand, considering renewable energy sources (RESs) and energy storage systems (ESSs), on cost reduction (%). Due to technological advances, microgrid-element costs are expected to decrease over time; therefore, future profitable electricity prices will be even lower. Results show a cost-effective electricity price ranging from 0.61 EUR/kWh to 0.16 EUR/kWh for hydrogen-priority EMSs and from 0.4 EUR/kWh to 0.17 EUR/kWh for battery-priority EMSs (0 and 100% hydrogen-system cost reduction, respectively). These figures still decrease sharply if RES and ESS cost reductions are considered. In the current scenario of uncertainty in electricity prices, the microgrid studied may become economically competitive in the near future. [ABSTRACT FROM AUTHOR]

Copyright of EMERG: Energy. Environment. Efficiency. Resources. Globalization is the property of Romanian National Committee of World Energy Council and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Pollution and energy crisis are actual issues in Europe, including the EU Central and Eastern European states. In this context, the objective of this paper is to assess the impact of economic growth and electricity prices for non-household consumers on pollution. The empirical findings reveal the U pattern for energy industry and inverted U pattern for manufacturing in the period 2007–2021 in the EU countries from Central and Eastern Europe. Renewable energy consumption reduces the CO2 and GHG emissions in energy industry. FDI and electricity prices determine the reduction in GHG and CO2 emissions in both sectors. These results are the basis for policy recommendations. [ABSTRACT FROM AUTHOR]

This dataset's data relate to Japan's electricity consumption from 1990 to 2015. It can be used by researchers, industry practitioners, and policymakers to analyze the impact of changes in electricity prices on the demand for electricity by region in the residential and industrial sectors. The dataset is divided into three categories: (i) regional sectoral electricity consumption data, (ii) regional sectoral electricity consumption intensity data, and (iii) regional sectoral electricity price data. Each category was obtained on an annual basis directly from various government databases. All data are aggregated by region.

Research Question: The launch and the beginning of trade on the South East Electric Power Exchange (SEEPEX) in Belgrade, early in 2016, opened the issue of forecasting volatility and price movements in the market. Motivation: The issue is of vital importance for all market actors for the purpose of maximising profits, reducing risks, planning production and making investment decisions. Forecasting volatility and price movements in electric power markets is important for traders with profit maximisation and yield-to-risk ratio optimisation in mind and, equally, for producers, large industrial consumers, investors and portfolio managers. Idea: Exploring models and techniques to forecast volatility in electricity markets and subsequently testing statistical methods based on time series data, the ARMA-GARCH being the preferred model, with a view to identifying optimal methods for this market. The volatility of the power market and price movements have been tested during a given period. The results can be used to gauge market parameters and opportunities to extrapolate future volatility and movements in electricity prices. Data: For the purposes of this analysis, a time series involving price movements and trade volumes were used, covering a period between the SEEPEX trade launch and the end of 2019. Tools: In the empirical part of the paper, "Stata 13" statistical and econometric software was used to explore stylised facts and model the volatility of SEEPEX electricity price returns. Findings: The authors offer an overview of different methods used in the research, having selected different specifications of the ARMA-GARCH model as the most reliable in predicting volatility in the given market. The exponential GARCH model with student-t error distribution is believed to have provided the best overall performance in modelling the SEEPEX return volatility, as well as the best volatility forecast. Contribution: This is one of the first empirical studies of the Serbian power market that deals with risk modelling. Forecasting time-varying electricity exchange volatility is important for all market participants interested in variance forecasts to be used to calculate risk and hedging measures.

In the framework of fuel reduction and energy conservation, the electric vehicles (EV's) has been identified as a promising option in contrast to fuel-driven vehicles. EV's battery limits to require visiting a greater number of times to the recharging stations, which must be viewed as in the route planning to keep away from inefficient vehicle routes with lengthy diversions. These problems have to consider, we propose an Efficient Electric Vehicle Route Optimization with Time-of-Use Electricity Pricing using Bat algorithm. Which can reduce the used vehicles as well as electricity-cost and total travel distance. Additionally, functional model and collective models are used to minimize the objectives: distance and cost. The computational assessment in light of the notable benchmarking test instances exhibits, proposed optimization algorithm electricity cost conservation on average 12.17% with Learnable Partheno-Genetic Algorithm (Yang et al. in IEEE Trans Smart Grid 6:657–666, 2015) 8.45% with VNS/TS Algorithm (Lin et al. in Trans Res Part-C 130:103285, 2021) and 5.15% with Mixed Integer Programming model (Ham and Park in IEEE Access 9:37220–37228, 2021). [ABSTRACT FROM AUTHOR]

Pollution and globalization are current challenges for Central and Eastern Europe (CEE), especially in the context of negative effects of globalization on environment. The aim of this paper is to assess the impact of globalization index on CO2 emissions in the CEE countries that joined the EU in the period 2005-2021. The results based on a panel data approach with DOLS/FMOLS estimators confirm the hypothesis that globalization enhances pollution in this region, but a non-linear relationship was validated between CO2 emissions and globalization index. After reaching a maximum level, the CO2 emissions begin to decrease, while globalization enhances. On the other hand, economic freedom and electricity prices in household consumers had a positive impact on pollution. Renewable energy consumption has the capacity to reduce pollution, while higher electricity prices for non-household users discourage economic activities, which contribute to environmental protection. These empirical findings could support policy proposals to reduce pollution in this region. [ABSTRACT FROM AUTHOR]

The electricity prices are often sensitive data that pose challenges in terms of collection. The volatility and soaring prices make the day-ahead electricity markets appealing for scientists. Several events took place from 2020, such as COVID-19 and military conflict in Ukraine leading to higher inflation, interest rates and energy crisis. Since 2020, the electricity prices on day-ahead market (DAM) increased even up to ten times. To perform the electricity price prediction, extensive feature engineering and historical data are required. Data sources from Romania are assessed to analyze the opportunity to extract relevant data for the electricity price and traded quantities. Thus, in this, paper, we investigate opportunity to extract data from web pages. We will suggest solutions to extract historical data from web sites that do not provide APIs or csv files. Several Python libraries such as BeautifulSoup and Selenium will be showcased, and approaches will be compared.

Fighting greenhouse gas emission requires actions to reduce energy usage and increase the proportion of renewable energy sources in the total energy production. Managing this energy transformation process requires significant financial outlays, so financial performance is crucial; it enables energy producers to make future energy transformation decisions. The study discussed in this paper evaluated the financial performance of renewable energy producers in comparison to conventional energy producers in the Baltic Sea Region. The research also aimed to determine how the financial performance is influenced by firm-specific and country-specific factors. We analyzed a unique panel dataset of 328 energy producers, most of which were private limited companies, in the period ranging from 2011 to 2019. Using random effects and fixed effects models, we found that private limited companies had a better financial performance than public limited companies. The size of the company’s assets had an ambiguous effect on its financial performance. Moreover, the difference in the performance of various type of energy producers was small. However, since the Paris Agreement, the impact of electricity prices on both ROA and ROE of the producers has been increasing.

Research Question: The launch and the beginning of trade on the South East Electric Power Exchange (SEEPEX) in Belgrade, early in 2016, opened the issue of forecasting volatility and price movements in the market. Motivation: The issue is of vital importance for all market actors for the purpose of maximising profits, reducing risks, planning production and making investment decisions. Forecasting volatility and price movements in electric power markets is important for traders with profit maximisation and yield-to-risk ratio optimisation in mind and, equally, for producers, large industrial consumers, investors and portfolio managers. Idea: Exploring models and techniques to forecast volatility in electricity markets and subsequently testing statistical methods based on time series data, the ARMA-GARCH being the preferred model, with a view to identifying optimal methods for this market. The volatility of the power market and price movements have been tested during a given period. The results can be used to gauge market parameters and opportunities to extrapolate future volatility and movements in electricity prices. Data: For the purposes of this analysis, a time series involving price movements and trade volumes were used, covering a period between the SEEPEX trade launch and the end of 2019. Tools: In the empirical part of the paper, "Stata 13" statistical and econometric software was used to explore stylised facts and model the volatility of SEEPEX electricity price returns. Findings: The authors offer an overview of different methods used in the research, having selected different specifications of the ARMA-GARCH model as the most reliable in predicting volatility in the given market. The exponential GARCH model with student-t error distribution is believed to have provided the best overall performance in modelling the SEEPEX return volatility, as well as the best volatility forecast. Contribution: This is one of the first empirical studies of the Serbian power market that deals with risk modelling. Forecasting time-varying electricity exchange volatility is important for all market participants interested in variance forecasts to be used to calculate risk and hedging measures. [ABSTRACT FROM AUTHOR]

With the continuous promotion of China's electricity market reform, the introduction of competition in the power generation market provides a new research direction for the generation expansion planning (GEP) problem, which is of great significance in the promotion of the optimization of the power energy structure. In the context of marketization, the electricity price expectation during the planning period is a key factor of GEP for independent power generation groups. There is some literature showing that the electricity price expectation in the planning period can be estimated according to certain laws of market supply and demand, while it seems to us that a future Pay as Bid (PAB) mechanism is better to determine the electricity price expectation. In this paper, to explore the impact of these two different electricity price formation mechanisms on the evolution of the generation market, a multi-agent framework is first established to describe the interaction process among the generation market agents; then, a GEP model for independent power generation groups is developed in the market competition environment, and four representative scenarios are finally designed for detailed comparative studies. Based on these case studies, the conclusion can be summarized as: (1) the PAB bidding mechanism has a lower electricity price and higher market installed capacity almost all the time during the whole planning period for all four scenarios; (2) it is more important that PAB can reduce the impact of parameter uncertainty in the laws of market supply and demand, which can obtain more reliable and reasonable results regarding the long-term evolution of the generation market. [ABSTRACT FROM AUTHOR]

In many urban areas and urban-rural regions, the price of electricity is not only subject to time-of-use tariffs, but also dependent on the usage purpose or service sector, significantly varying across different geographic locations or land uses. Such a spatial price difference greatly affects the charging behavior of electric vehicle drivers. This paper models and analyzes the possible impact of electricity-charging price on the routing choice and flow pattern of electric vehicles in a home-workplace commute traffic network. The particular circumstance in such a network accommodating electric vehicles is that commuters driving battery electric vehicles have opportunities to charge their vehicles at both home and workplace and take into account different electricity-charging prices in their travel choices. For the illustration and evaluation purposes, we presented a small synthetic example and proposed a convex programming model for characterizing the commute network flow pattern of electric vehicles that may be charged at home and/or workplace, encapsulating a piecewise electricity-charging cost function that implies commuters' combined home-based and workplace-based charging behaviors. Given different electricity-charging prices at home and workplace, solving this problem requires tracking all individual paths in the network. As such, a path-based solution algorithm sketched in the disaggregated simplicial decomposition framework, with some modifications in the path generation phase and an added k-shortest path search procedure, is designed for problem solutions. We implemented the modeling and solution methods for deriving the network flow pattern of electric vehicles and comparing it with that of gasoline vehicles, as well as evaluating the network performance change caused by varying electricity-charging prices with multiple network-level and link-level evaluation matrices. [ABSTRACT FROM AUTHOR]

This research proposes an empirical method to estimate the impact on the wholesale electricity market of an increase in the price of CO2 emission allowances. The current literature in this field is mainly focused on long-term simulation analyses, while this study carries out a short-term analysis with microdata from the electricity market. A higher price of CO2 implies an increase in the electricity generation costs of polluting units and therefore an increase in the price of the electricity market. When CO2 becomes more expensive, polluting electricity generators are shifted in the hourly electricity supply curve towards less competitive positions (in favour of less polluting/cheaper units). Displaced polluting units could even be taken out of the market, which would imply a reduction in CO2 emissions. These short-term movements can be reproduced with our microdata of the day-ahead electricity market –data Provided by the Spanish Market Operator (OMIE). According to our results, increases in the carbon price of 10, 20, or 30 € per ton, respectively, cause increases of 1.8%, 4.2% and 5.3% in the electricity price (year 2018), while the negative effect on emissions is relatively small. Our analysis concludes with the estimation of an ARIMA-SARIMA model that looks for the main determinants of the variations in the hourly energy prices and the carbon emissions. The estimations show that the marginal supply technology in the electricity market is important in explaining these variations. [ABSTRACT FROM AUTHOR]

Electricity is by far the most valuable energy commodity for households; hence, it is of the utmost importance for national regulatory authorities and the European Commission (EC) to guarantee affordable and unimpeded access for European citizens to this vital social good. The existing academic literature mainly focuses on the effect of specific renewable energy resources (RES), such as solar, wind, etc., on electricity prices, thus neglecting the crucial impact of the electricity market structure. In an effort to fill this gap, the present paper attempts to clarify whether the real effect of the share of total renewable energy production in the generation scheme and certain electricity market liberalization indices constitute key determinants of household electricity prices. The study is further innovative on the grounds that the empirical analysis utilizes both static and dynamic panel methodologies for a dataset including several variables introduced for the first time in academia. The dataset consists of yearly observations regarding 26 EU countries for a time horizon from 2003 until 2019. The econometric outcomes revealed the complex relationship between RES deployment and generation concentration with the level of household electricity prices. In contrast, the deregulation of the retail market and especially the presence of many retailers with a market share exceeding 5% can benefit European consumers by reducing electricity prices. Additionally, the relative costs concerning the outward-orientation of the economy and the power system's upgrade were found to be transferred to the final electricity price. The opposite applies to environmental taxes, allowing European governments to accumulate considerable funds for ecological and environmental protection actions. Lastly, due to the estimated slow adjustment rate of electricity prices, policymakers are advised to develop long-term strategic energy planning. [ABSTRACT FROM AUTHOR]

In order to establish a new optimal load distribution between CHP units and wind turbines and realize a new type of energy-saving dispatching, the authors proposed a dispatching system based on smart grid. The author proposed that the heat pump with electric drive air conditioning should share part of the heating load of the hot water radiator, accordingly, the supply of heating water is reduced, increase heating power load. This directly leads to the increase of the total power load in the grid and the decrease of the total heating and hot water load, changing the proportion of thermal power load. Based on the new heating water and power load constraints, a mathematical model of optimal scheduling is established, the energy-saving dispatching of cogeneration units and wind turbines is realized. The simulation results show that using the new energy-saving scheduling method, 342.4 MWh of fuel can be saved per hour, and the energy saving benefit is about 8.83%. If the calorific value of standard coal is 29271 kJ/kg, this means that the fuel consumption savings per hour is about 42.14 ton standard coal. The calculation results show that the higher the value is, the more economically feasible the feed-in price of wind power is. In conclusion based on the current electricity price and heating heat price, in order to ensure that the economic benefits of each participant are not changed, the feed-in price of wind power is discussed, and the economic feasibility of the method is proved. [ABSTRACT FROM AUTHOR]

In order to establish a new optimal load distribution between CHP units and wind turbines and realize a new type of energy-saving dispatching, the authors proposed a dispatching system based on smart grid. The author proposed that the heat pump with electric drive air conditioning should share part of the heating load of the hot water radiator, accordingly, the supply of heating water is reduced, increase heating power load. This directly leads to the increase of the total power load in the grid and the decrease of the total heating and hot water load, changing the proportion of thermal power load. Based on the new heating water and power load constraints, a mathematical model of optimal scheduling is established, the energy-saving dispatching of cogeneration units and wind turbines is realized. The simulation results show that using the new energy-saving scheduling method, 342.4 MWh of fuel can be saved per hour, and the energy saving benefit is about 8.83%. If the calorific value of standard coal is 29271 kJ/kg, this means that the fuel consumption savings per hour is about 42.14 ton standard coal. The calculation results show that the higher the value is, the more economically feasible the feed-in price of wind power is. In conclusion based on the current electricity price and heating heat price, in order to ensure that the economic benefits of each participant are not changed, the feed-in price of wind power is discussed, and the economic feasibility of the method is proved. [ABSTRACT FROM AUTHOR]

The article sets the objectives of describing and systematising measures to influence the price of electricity in Poland after 2018. The article’s first part presents the reasons for the enactment of the law freezing electricity prices for 2019 which is followed by the discussion of its content, shortcomings and case law arising from the law’s execution. What is later presented focuses around the European response to the global energy crisis (2021–2023) as well as the Polish legislation mitigating the electricity prices. The last section outlines a typology of instruments mitigating the price of electricity from the most market-interfering to the most benign. [ABSTRACT FROM AUTHOR]

The unexpected pandemic eruption in March 2020, the European efforts to diminish the gas house emissions, prolonged drought, higher inflation and the war in Ukraine clearly have had a strong impact on the electricity price. In this paper, we analyze the electricity prices on the Romanian Day-Ahead Market (DAM) along with other variables (inflation, consumption and traded volume of gas on DAM) over the last three and a half years in an attempt to understand its evolution and future trend in the economic and geopolitical context. Autoregressive Distributed Lag models are proposed to analyze the causality among variables on short- and long-term perspective, whereas Quantile Regression (QR) is proposed to enhance the results of the Ordinary Least Squares (OLS) regression. Furthermore, using market concentration metrics – Herfindahl-Hirschman Index (HHI), C1 and C3 ratio, we analyze the competitiveness on the Romanian DAM and correlate it with the electricity price evolution. The concentration indicators on this market reflect the degree of competition manifested between sellers and buyers respectively, their dynamics being able to influence the price level. The higher concentration on the sellers’ side (HHI = 1500) indicates a potential speculative behavior on this market that led to higher prices on DAM. [ABSTRACT FROM AUTHOR]

Energy modeling with an objective function targeting the entire power supply system can determine a power supply mix for a high share of variable renewable energy (VRE) electricity in the power supply. However, actual power supply systems are developed by the partial optimization of the various components of the entire system, such as the power transmission and distribution (PTD) company. This study aims to determine the effective electricity price conditions for the optimal utilization of geographically distributed VRE power by the PTD company. The analysis method used is an optimization model with an objective function targeting the PTD company cost. The optimization model minimizes the PTD company cost, which includes the costs of electricity purchase and installation of battery storage and transmission systems. The methodology is applied to the power supply system of Hokkaido as a case study. The results show that the amount of VRE electricity utilized by the PTD company depends on the electricity prices from thermal and VRE power generation companies. Increasing the VRE share of the power supply requires higher electricity prices from thermal power. The higher shares of VRE require increases in battery storage and power transmission capacities to minimize the VRE power output fluctuations. Further, the results show that the price difference between thermal and VRE electricity prices is a key factor to determine the VRE utilization by the PTD company. A larger price difference is necessary for the PTD company to increase the VRE share and install battery storage and transmission systems. High prices of thermal power increase the revenues of power generation companies and the cost to the PTD company. To reduce the PTD company costs, additional measures such as a carbon tax, cheaper VRE electricity prices, and subsidies may be effective to incentivize battery storage and transmission costs.

In the context of national efforts to promote country-wide distributed photovoltaics (DPVs), the installation of distributed energy storage systems (DESSs) can solve the current problems of DPV consumption, peak shaving, and valley filling, as well as operation optimization faced by medium-voltage distribution networks (DN). In this paper, firstly, a price elasticity matrix based on the peak and valley tariff mechanism is introduced to establish a master–slave game framework for DN-DESSs under the DPV multi-point access environment. Secondly, the main model optimizes the pricing strategy of peak and valley tariffs with the objective of the lowest annual operating cost of the DN, and the slave model establishes a two-layer optimization model of DESSs with the objective of the maximum investment return of the DESSs and the lowest daily operating costs and call the CPLEX solver and particle swarm optimization algorithm for solving. Finally, the IEEE33 node system is used as a prototype for simulation verification. The results show that the proposed model can not only effectively reduce the operating cost of the distribution network but also play a role in improving the energy storage revenue and DPV consumption capacity, which has a certain degree of rationality and practicality.

The objective of this article is to analyze and empirically validate the differential effects in the daily schedules of the induced electricity prices by selling bids for three different technologies, namely hydraulic, thermal and renewable energy sources (RES), in hourly values, by daily observations for the year 2018. To achieve this objective, we employ an autoregressive distributed lag (ARDL) model-bound testing approach The results of the ADRL-ECM method, which also reports the long-run analysis, show that (a) the renewable and thermal technologies positively and significantly affect the electricity price for Endesa and Hidroelétrica del Cantábrico generators and (b) the hydraulic technology impacts negatively the electricity price, both at a 1% level of significance. In addition, following a long-term perspective it must be highlighted that RES negatively impact the price of electricity with a 1% level of significance for the Iberdrola, E.ON Energy, Unión Fenosa and EDP Energy of Portugal generators. However based on a short-term perspective, the results report a positive effect between the quantities traded by hydraulic and thermal technologies on the electricity price for Endesa, Iberdrola, Hidroelétrica del Cantábrico and EDP Energy of Portugal, at a 1% level of significance.