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The volatility and uncertainty of electricity prices due to renewable energy sources create challenges for electricity trading, necessitating reliable probabilistic electricity-price forecasting (EPF) methods. This study introduces an EPF approach using quantile regression (QR) with general predictors, focusing on the UK market. Unlike market-specific models, this method ensures adaptability and reduces complexity. Using 1,132 days of training data, including electricity prices, demand forecasts, and generation forecasts obtained from UK electricity companies, results show that the proposed model achieved a mean absolute error of 18.27 [(£/MWh] for predicting volatile short-term spot market prices. The QR model achieved high predictive accuracy and stability, with only a 4–25% average pinball loss increases when the previous day’s prices ( $P_{t-1}$ ) were excluded due to bidding deadlines. These findings demonstrate the model’s robustness and its potential to enhance market efficiency by providing reliable and simplified probabilistic forecasts, aiding stakeholders in mitigating risks and optimizing strategies.

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.

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.

Appropriate operation of the dam reservoir in a hydroelectric power plant (HEPP) is necessary for energy planning, reservoir management, and efficient operation. For good energy planning, the operator needs to make an accurate estimate of the energy production capacity for the next day and plan for production when the energy need is highest. The energy produced in HEPPs depends on the level of water stored in the reservoir, which is directly connected to the reservoir flow. As the water level in the reservoir varies throughout the year depending on climatic conditions, it is important to estimate energy production in order to operate the HEPP most effectively. In this study, the next-day energy production of the HEPP was estimated using a neural network with two hidden layers, each with 10 neurons. A neural network with a hidden layer of 20 neurons was used to estimate future electricity prices and the best hours for market clearing price (MCP). This study found that using short-term training provided the best hourly estimation of MCP, with an average accuracy of 90%; the daily estimation of MCP was ≥95%.

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.

Hybrid production plants harness diverse climatic sources for electricity generation, playing a crucial role in the transition to renewable energies. This study aims to forecast the profitability of a combined wind–photovoltaic energy system. Here, we develop a model that integrates predicted spot prices and electricity output forecasts, incorporating relevant climatic variables to enhance accuracy. The jointly modeled climatic variables and the spot price constitute one of the innovative aspects of this work. Regarding practical application, we considered a hypothetical wind–photovoltaic plant located in Italy and used the relevant climate series to determine the quantity of energy produced. We forecast the quantity of energy as well as income through machine learning techniques and more traditional statistical and econometric models. We evaluate the results by splitting the dataset into estimation windows and test windows, and using a backtesting technique. In particular, we found evidence that ML regression techniques outperform results obtained with traditional econometric models. Regarding the models used to achieve this goal, the objective is not to propose original models but to verify the effectiveness of the most recent machine learning models for this important application, and to compare them with more classic linear regression techniques.

This article examines the impact of financial support mechanisms and geopolitical factors on the profitability of investments in solar power plants within Slovenia. The European Union’s energy policy prioritizes increases in renewable energy sources, aiming to reduce dependency on unstable and volatile fossil fuel markets. Solar power plants play a vital role in this transition. The energy policy framework also includes mechanisms and support systems to operate such facilities. This article analyzes electricity price trends over the past decade and addresses which support type—guaranteed purchase or operational support—has proven more profitable for investments in solar power plants up to 50 kW in Slovenia, considering economic and geopolitical influences on the electricity market. Although the global energy market has been affected by various significant events in recent years, it was found that the COVID-19 pandemic had minimal impact on the electricity market. In contrast, the onset of the conflict in Ukraine has contributed to rising electricity prices and has influenced the support dynamics essential for the development and sustainability of renewable energy systems. Analyses from the past decade indicate a higher return on investment in solar power plants when operational support mechanisms are chosen over guaranteed purchase support.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Energy challenges are crucial issues to achieve Sustainable Development and its goals. Energy availability and affordability are pillars for ending poverty, giving access to commodities as well as water, etc. Modern lives rely on appliances and gadgets based on electric energy being its price a key issue making it worth to analyze and promote simple models able to predict electric energy prices to support in decision-making processes and in management. This work studied the correlation of electricity price with variables such as the electricity mix, GDP (gross domestic product), energy productivity, electricity consumption per capita, fossil fuel reserves, and diesel price, using Spearman correlation. To the significant correlations found it was then applied the Kruskal–Wallis test and the variables that presented statistically significant differences were then considered to model electricity price based on these macro variables. Our findings revealed that the best models were a logarithmic and a linear model of energy productivity to predict electricity price, which is fundamental to achieve Sustainable Development Goals (SDG), specifically SDG7. In the validation process, these models presented an average deviation of 10.3% and 11.7%, respectively, which is reasonable considering the simplicity of the models developed.

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.

By 2030 Cyprus committed to reduce greenhouse gas emissions by 40% and increase renewables’ energy share by 19% according to the European engagements. Difficulties appear due to the continuous increase in domestic energy consumption, the large dependency on fossil fuels and the adverse climate. A number of macroeconomic, demographic and climatic indicators that influence Cyprus’s electricity consumption (EC) was analysed for years 2000–2018 using augmented Dickey–Fuller unit root test and the autoregressive distributed lag model (ARDL). The ARDL model revealed that an increase in population could radically increase EC in the long-run and short-run. Results posit that a 1% increase in urban population, electricity price and unemployment decrease domestic EC in the long-run by 17.25%, 0.48% and 0.30%, respectively. In the short-run smaller elasticities were found because time was too short for adjustments to be made. However, their decrease could not outweigh the population growth effect on increasing domestic EC.

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.

This study proposes an optimized model of a micro-energy network (MEN) that includes electricity and natural gas with integrated solar, wind, and energy storage systems (ESSs). The proposed model is based on energy hubs (EHs) and it aims to minimize operation costs and greenhouse emissions. The research is motivated by the increasing use of renewable energies and ESSs for secure energy supply while reducing operation costs and environment effects. A general algebraic modeling system (GAMS) is used to solve the optimal operation problem in the MEN. The results demonstrate that an optimal MEN formed by multiple EHs can provide appropriate and flexible responses to fluctuations in electricity prices and adjustments between time periods and seasons. It also yields significant reductions in operation costs and emissions. The proposed model can contribute to future research by providing a more efficient network model (as compared with the traditional electricity supply system) to scale down the environmental and economic impacts of electricity storage and supply systems on MEN operation.

The aim of the article: The aim of the article is to evaluate and analyze the dynamics of electricity prices for households in the European Union member states (EU-27) in the period 2011–2020. The study also focuses on the key components of electricity prices in the countries analyzed. The discussed issues are important from the socio-economic point of view. It refers to the issue of sustainable development, where electricity prices are addressed in relation to the problem of energy poverty of households. Methodology: The study is of a theoretical and analytical character. In addition to the review of available dnational and foreign literature, Eurostat data on electricity prices for households in the EU-27 were analysed. Moreover, the paper presents the application of a selected cluster analysis method, i.e. the k-means method, to assess the situation of the EU-27 countries in terms of electricity prices for households in the analysed period of 2011–2020. Results of the research: The result of the analyses undertaken is a presentation of the share of VAT and other taxes and levies in the price of electricity for households. The analyses showed differences between the countries in the structure of establishing the electricity price for households. Only in three countries (Bulgaria, Hungary, Slovakia) it was found that there was no share of other taxes and levies in the electricity price. In turn, the applied k-means method contributed to obtaining the division of countries into four groups reflecting the differentiation in terms of the amount of electricity price for households in the period under study.

The study focuses on the implications of the changes occurring on the energy market, specifically on the electricity market, over the performances of the stocks of companies from the energy and utilities sector listed at the Bucharest Stock Exchange. The objective of the paper is to determine if there is a short-term correlation between electricity consumption and price, and the Bucharest Exchange Trading Energy and Related Utilities Index (BET-NG). After performing a quantitative analysis of the competitive segment of the electricity retail market in Romania, and of the evolution of BET-NG, we used the Pearson’s correlation test to find if electricity consumption and price influence the trend of BET-NG. Our findings suggest that there is a statistical correlation between the consumption on electricity and BET-NG. Electricity price also influences the index, but only in the case of nonhousehold clients, while the price paid by households in not correlated to BET-NG.

Detecting electricity price spikes is crucial as it helps market participants to gain confidence and formulate appropriate strategy to maximize their benefits. In this paper, a multi-feature based approach with the incorporation of variable thresholds is developed to detect electricity price spikes in the national electricity market of Australia. The variable thresholds, which are determined using a weighted sliding window average and an adjusted standard deviation, help to segregate spikes from normal price variations. Also, significant features are extracted from the market after thoroughly analyzing the underlying causes resulting into the price spikes. These features are employed as inputs to a support vector machine to classify electricity prices as spikes or non-spikes. A case study is conducted using a dataset acquired from the state of New South Wales, Australia. The results show that the proposed method can successfully detect the price spikes with high accuracy and confidence.

With the advancement of power market reforms, electricity price prediction has attracted increasing attention. This paper proposes a novel probabilistic forecasting approach based on deep neural network for electricity prices. Firstly, reasonable price distributions are constructed from historical data based on the nearest neighbors. Then, a deep convolutional neural network(DCNN) is employed to extract high-level features. Meanwhile, these features are fed to label distribution learning forests (LDLFs) to generate probabilistic forecasts. The proposed framework, dubbed DCNN–LDLFs, can jointly learn the price distributions. The DCNN–LDLFs provides three types of forecasts, including deterministic forecasts, prediction intervals (PIs), and probability density functions. Unlike most popular models, the DCNN–LDLFs can be trained in an end-to-end manner, which has the opportunity to obtain a globally optimal solution. The case study on Singapore shows that the proposed method provides superior forecasts over the existing approaches.

The distributed photovoltaic power generation is an important way to make use of solar energy in cities. China issues a series of policies to support the development of distributed photovoltaics in law, electricity price, grid connection standard, project management, financial support and so on. However, there are still some defects in policies and market mechanism. China creates a competitive market with a significant number of projects of distributed photovoltaic power through the reform of the electricity market, yet substantial drawbacks of the corresponding investment subsidies prevent distributed photovoltaic power from rapidly developing. This paper summarizes the status quo of China’s distributed photovoltaic power development, given its long-term plan, presents excellences and shortcomings of the existing policy system, and looks into the supporting policies and implementation paths for China’s distributed photovoltaic power in different stages. Innovative business models and financial support models are conducive to the development of distributed photovoltaic power. Financial innovation methods such as crowd funding and asset securitization should be encouraged to develop a sound risk assessment mechanism for projects, involve insurance institutions, and establish a risk sharing mechanism. In the context of a series of supporting policies, the distributed photovoltaic power in China will move towards market-oriented standardization for a healthier and more stable development.

This study aims to explore the influence of electricity consumption, electricity price, inflation and interest rate on GDP and investments in Indonesia in the period 2001-2018. This paper is explanatory research. A Generalized Structured Component Analysis was a component-based approach to Structural Equation Modelling has used as a research model. The empirical analysis uses time-series data of GDP, Electricity Consumption, Electricity Price, Inflation Rate, Interest Rate, Investments and GDP in Indonesia in the period 2001-2018. The findings of this study are electricity consumption has a significant positive effect on GDP and electricity price. Electricity price has an insignificant positive effect on electricity consumption and investment. GDP has a significant positive effect on electricity consumption but insignificant on investment and inflation. Investment has an insignificant negative effect on electricity consumption and inflation. Inflation has a significant positive effect on the interest rate, vice versa, but is insignificant to electricity consumption. The interest rate has an insignificant positive effect on investment. The Originality of this study, namely previous studies focused more on the relationships and causality between Electricity Consumption, FDI, GDP, while in this study the emphasis is more on predictions between latent variables using the GSCA. In previous studies using total electricity consumption, in this study, the latent variable of electricity consumption is formed by industry electricity consumption and business electricity consumption which is productive consumption in increasing GDP. This study uses a multi-variate study consisting of Electricity Consumption of Industrial and Business, Electricity Price, Investment, GDP variables, and adding Inflation Rate and Interest Rate that represent macro-economic conditions in the research model.

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.

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.

Renewable energy is increasingly mobilizing more investment around the globe. However, there has been little attention to evaluating economic and regulatory (E&R) uncertainties, despite their enormous impact on the project cashflows. Consequently, this review analyzes, classifies, and discusses 130 articles dealing with the design of renewable energy projects under E&R uncertainties. After performing a survey and identifying the selected manuscripts, and the few previous reviews on the matter, the following innovative categorization is designed: sources of uncertainty, uncertainty characterization methods, problem formulations, solution methods, and regulatory frameworks. The classification reveals that electricity price is the most considered source of uncertainty, often alone, despite the existence of six other equally influential groups of E&R uncertainties. In addition, real options and optimization arise as the two main approaches researchers use to solve problems in energy system design. Subsequently, the following aspects of interest are discussed in depth: how modeling can be improved, which are the most influential variables, and potential lines of research. Conclusions show the necessity of modeling E&R uncertainties with currently underrepresented methods, suggest several policy recommendations, and encourage the integration of prevailing approaches.

The merit order effect (MOE), which renewable energy sources can decrease wholesale electricity prices, plays an important role in establishing low-carbon societies. After the liberalization of the electricity market, the trade volume of the Japan Electric Power Exchange (JEPX) day-ahead spot market drastically increased between 2016 and 2019; however, price spikes still occur often. Ordinary least squares and quantile regression analyses were applied in this study to investigate how wind and solar photovoltaics (PV) energy generation affect the JEPX day-ahead spot price by time, price range, and area, and we concluded that the MOE of wind increased between 2016 and 2019 while that of PV decreased during this time. In regard to the high price ranges, although wind generation is not significant in terms of reducing price spikes, PV had this effect in 2016 and 2017 but not during the other years covered. The study area was divided into four regions, and each area followed trends that were different from those of the national analysis. Overall, the key finding of our study is that wind power has more potential to reduce electricity prices than PV.

Being affected by a variety of factors, power-generation structure plays an essential role in a high-quality and sustainable development. The focus of this paper is to evaluate the influence of electricity price on it. First, we provide a microeconomic framework to understand the impact mechanism. We discuss two effects through which price level can affect power generation, and then the power-generation structure. After that, an empirical test is conducted using provincial panel data, and the results of it are robust. We also test the above-mentioned mechanism empirically. There are two main conclusions. First, the electricity price has a positive effect on the share of thermal power in electricity generation. Second, the mechanism test shows that an increase of electricity price can not only improve efficiency of power plants but also propel firms to invest in more renewable energy plants.

The relevance of the work is caused by the intensive economic development of the countries of Northeast Asia, the increase in their electricity demand and feasibility of improving the economic and environmental efficiency of electricity supply through the implementation of electrical energy integration projects. In this case, the large-scale introduction of renewable energy sources can play an important role. The aim of the research is to determine the optimal structure of wind power plants in the Mongolian part of the Gobi Desert (the ratio of the capacities of different types of power plants and their generation of electricity) for different combinations of economic and climatic conditions. Methods: systematization of climatic and meteorological information, preliminary assessment of the effectiveness of energy sources of various types according to the criterion of the cost of electricity produced, mathematical modeling of the structure and modes of operation of the power system. The mathematical model takes into account the random nature of solar and wind energy. Results. It is shown that the electricity produced by photoelectric converters and wind turbines, taking into account the cost of its transport along power transmission lines, is competitive in the electricity markets of China and other countries of Northeast Asia. For various values of solar radiation input and wind speed, the optimum ratios were determined between the capacities of renewable energy sources, the production of electricity by photoelectric converters and wind turbines, as well as the supply of electricity from China’s power system to compensate the unevenness in production of renewable energy sources. The economic efficiency of joint use of solar and wind energy in the Mongolian part of the Gobi desert is shown, with the exception of some areas with low wind speeds. The combined use of solar and wind energy allows reducing the total costs in the power supply system by more than a quarter compared with the variant of using only solar energy.

The aim of this paper is to provide an answer on question if energy transition would have an effect on electricity prices in Serbia. As a part of the Energy Community, Serbia is obliged to harmonize its energy regulation with the Europan Union. Even though electricity price in Serbia is at the lowest level in Europe, it has a high share in the household consumer basket. However, electricity price does not include the cost of CO2 emissions. The recent adoption of National Emission Reduction Plan implies Serbia's obligation to reduce emissions from large combustion plants by 2028. This commitment will entail significant investments and will influence the further development of the electricity sector. Accession of the Republic of Serbia to the EU and consequent implementation of EU ETS in the period 2023-2028, will have a significant impact on the EPS electricity cost production. At the moment, rough estimates show that this amount goes up to 2.5 eurocents per kWh produced by thermal power plants.

Precise and reliable forecasting of short-term electricity load is essential to the development of smart grids. Particularly, deep neural networks (DNNs) are widely utilized for the prediction of short-term electricity load due to their automatic feature extraction ability. However, these available stacked deep-learning models may lose some temporal features or spatial features of original input data. To capture more comprehensive information, in this article, we present an integration scheme based on empirical mode decomposition (EMD), similar day methods, and DNNs to perform short-term load forecasting. It is especially worth noting that the electricity price is also an important factor for load variation, which is considered in our proposed scheme. Specifically, there are two primary layers: a feature extraction layer and a forecasting layer. In the feature extraction layer, EMD is applied to decompose load time series into several components, which are arranged into the 2-D input matrix of the convolutional neural network (CNN). Both the output vectors of the CNN and the raw load sequences are fed into the long short-term memory (LSTM) layer. Therefore, the whole EMD based CNN-LSTM approach extracts multimodal spatial-temporal features from input data. Meanwhile, the electricity price data is utilized to obtain multimodal spatial-temporal features in the same way. Additionally, the day and hour information and loads of similar days are to augment extra features for prediction. In the forecasting layer, the forecasting task is accomplished through a fully-connected neural network based on the outputs of the feature extraction layer. Leveraging these techniques enables our proposed scheme to extract more latent features, which significantly improve the accuracy. In order to demonstrate the performance of our proposed scheme, related experiments are conducted on actual data from the electricity market in Singapore. Compared to other available models, our proposed scheme is superior in graphic and numerical results.

Electricity price forecasting has been a booming field over the years, with many methods and techniques being applied with different degrees of success. It is of great interest to the industry sector, becoming a must-have tool for risk management. Most methods forecast the electricity price itself; this paper gives a new perspective to the field by trying to forecast the dynamics behind the electricity price: the supply and demand curves originating from the auction. Given the complexity of the data involved which include many block bids/offers per hour, we propose a technique for market curve modeling and forecasting that incorporates multiple seasonal effects and known market variables, such as wind generation or load. It is shown that this model outperforms the benchmarked ones and increases the performance of ensemble models, highlighting the importance of the use of market bids in electricity price forecasting.

Electricity bills in Kenya have been an issue of concern to electricity consumers in the recent past. Highly volatile oil prices and unprecedented weather fluctuations have acted as significant shocks for electricity generation, influencing electricity pricing. This study sought to investigate the contribution of diversity, spare capacity, and system structure as metrics in determining energy resilience. We contend that electricity prices represent the underlying fleet structure's ability to adjust to change and, therefore, can be used to predict energy resilience. Resilience metrics were determined using electricity generation data, electricity sales, electricity installed capacities, and electricity imports, while electricity prices represented the response variable. A regression model was fitted between the response variable and resilience metrics. Diversity, spare capacity, and import metrics play a significant role in predicting electricity prices. However, the diversity metric's role depends on the portfolio mix and requires further comparative empirical evidence.

We consider the wholesale electricity market prices in England and Wales during its complete history, where price-cap regulation and divestment series were introduced at different points in time. We compare the impact of these regulatory reforms on the dynamics of electricity prices. For this purpose, we apply flexible distributions that account for asymmetry, heavy tails, and excess kurtosis usually observed in data or model residuals. The application of skew generalized error distribution is appropriate for our case study. We find that after the second series of divestments, price level and volatility are lower than during price-cap regulation and after the first series of divestments. This finding implies that a sufficient horizontal restructuring through divestment series may be superior to price-cap regulation. The conclusion could be interesting to other countries because the England and Wales electricity market served as the benchmark model for liberalizing energy markets worldwide.

The challenge of the development of the retail electricity markets competition in order to improve the efficiency of electric power industry in Russia has considered. The modern Russian retail electricity markets model does not provide the adequate level of competition in retail energy sales sector has revealed. This article studies specific features of the Russian retail electricity markets organization. The level of competition in retail energy sales sector and the competition impact to the retail electricity prices have estimated. The competition prohibiting factors for retail electricity markets have suggested.

Promotion of competition at retail electricity markets and electric power industry liberalization are the worldwide trends. The rewarding experience of retail electricity markets performance demonstrates that the deregulated model of wholesale and retail electricity markets provides mostfavorite competitive mechanism. The UK and Northern Europe countries (Norway, Sweden, Finland, Denmark) competitive mechanisms practiced at retail electricity markets have analysed. The deregulated model of wholesale and retail electricity markets distinctive features has determined, also the applied types of electricity supply contract and tariffs, the special aspects of electric power pricing for ultimate customers have perceived.

Increasing on electricity price by Indonesian Government in 2001 by 17.47% and 2002 by 24% not only has negative impact on industry sectors but also on agricultural sectors. This paper aims to analysis impact on agricultural sectors performance with using a Recursive Dynamic Computable General Equilibrium (CGE) Model, namely “INDOF model”. The simulation indicates both in the short run and long run increasing on electricity price will reduce industry output, household consumption and employment in agricultural sectors. Besides that the policy will increase price of agricultural product. The negative impacts except household consumption are higher in the long run than that in the short run.

This paper focuses on the relationship between the European Union Emission Trading System allowances’ prices and the Italian electricity price, aiming at assessing whether such a mechanism has been a driver for the decarbonization of the power sector. To this aim, we calculate the long-run relationships between energy prices, natural gas prices and allowances’ prices, through a VECM model, distinguishing between peak and off-peak prices. The analysis is carried out for the third phase of the EU-ETS, which started in 2013, and for two-year rolling windows that account for changes over time of the pass-through rates. It is shown that the natural gas price has a high pass-through rate of roughly 70%, which is increasing over time. On the contrary, the pass-through rate of the allowances’ price is as low as 7% for the wholesale electricity price, being slightly more and less for the peak and off-peak prices, respectively. However, this rate has been substantially changing over time, starting from a high level and falling significantly, becoming negative in the recent years. This could signal that the EU-ETS has been increasingly more effective in endogenizing emission costs for power producers, inducing them to reduce their production costs associated with emissions by means of a change in technologies. However, the analysis of the impulse response functions hardly supports this finding, eventually casting doubts on the effectiveness of the EU-ETS in Italy to drive the transition toward a less carbon-intensive power supply.

Accumulation of influencing factors during several consecutive time periods makes the variation of target parameters lag behind the variation of their influencing factors. This important phenomenon, known as the cumulative effect, would lead to relatively large forecasting errors. In this paper, the dynamic similar sub-series method is proposed to take cumulative effect into consideration. The similar sub-series for the forecasting parameter sub-series are selected based on similarities of target parameter sub-series and influencing factors sub-series. The internal variations of target parameter sub-series and each influencing factor sub-series are innovatively integrated into the selection rules for the dynamic similar sub-series. The corresponding forecasting algorithm is designed, and the forecasting parameters are deduced and forecasted according to the variation of the dynamic similar sub-series. The proposed method is compared with a variety of representative methods under the short-term daily average load forecasting, the electricity price forecasting and the global horizontal irradiance forecasting scenarios to demonstrate its effectiveness.

The study on the sizing of renewable energy generation systems and energy storage systems together in a household considering different price mechanisms can further promote the development of the home energy management system (HEMS). In this paper, a HEMS expressed as a bi-level model is provided to investigated capacity allocation strategy of the photovoltaic (PV) and battery energy storage system (BESS) in a smart household considering: 1) the impact of electricity price mechanisms which include the time-of-use pricing (TOU), the real-time pricing (RTP), and the stepwise power tariff (SPT); 2) the effect of subsidies of PV; and 3) the uncertainty in the PV output and seasonal load profiles. Then, the hybrid approach which combines the cataclysmic genetic algorithm and the DICOPT solver in GAMS is employed to find an optimal solution. Finally, six cases with different price mechanisms and approaches, as well as the sensitivity analysis of optimal solution to subsidies are presented. Results indicate that, with the subsidies, only the PV system needs to be equipped in a household under the SPT, while the PV system and BESS need to be equipped together under the RTP and TOU. Only when the subsidies of PV reach a certain level will the installation of PV be considered.

Forecasting the electricity price and load has been a critical area of concern for researchers over the last two decades. There has been a significant economic impact on producers and consumers. Various techniques and methods of forecasting have been developed. The motivation of this paper is to present a comprehensive review on electricity market price and load forecasting, while observing the scientific approaches and techniques based on wind energy. As a methodology, this review follows the historical and structural development of electricity markets, price, and load forecasting methods, and recent trends in wind energy generation, transmission, and consumption. As wind power prediction depends on wind speed, precipitation, temperature, etc., this may have some inauspicious effects on the market operations. The improvements of the forecasting methods in this market are necessary and attract market participants as well as decision makers. To this end, this research shows the main variables of developing electricity markets through wind energy. Findings are discussed and compared with each other via quantitative and qualitative analysis. The results reveal that the complexity of forecasting electricity markets’ price and load depends on the increasing number of employed variables as input for better accuracy, and the trend in methodologies varies between the economic and engineering approach. Findings are specifically gathered and summarized based on researches in the conclusions.

Abstract This paper aims to optimize total energy costs in an operational model of a novel energy hub (EH) in a residential area. The optimization problem is set up based on daily load demand (such as electricity, heat, and cooling) and time-of-use (TOU) energy prices. The extended EH model considers the involvement of solar photovoltaic (PV) generation, solar heat exchanger (SHE), and a battery energy storage system (BESS). A mathematical model is constructed with the objective of optimizing total energy cost during the day, including some constraints such as input-output energy balance of the EH, electricity price, capacity limitation of the system, and charge/discharge power of BESS. Four operational cases based on different EH structures are compared to assess the effect of solar energy applications and BESS on the operational efficiency. The results show that the proposed model predicts significant changes to the characteristics of electricity and gas power bought from utilities, leading to reduced total energy cost compared to other cases. They also indicate that the model is appropriate for the characteristics of residential loads.

The Portuguese and the Spanish electricity generation systems are analysed in this paper. The Iberian market has been isolated and has an increasing proportion of renewable sources. The main objective of this study is to understand how electricity generation sources are interacting with electricity wholesale prices. The VAR approach was used because of its high robustness to cope with the endogeneity detected by Granger block Exogeneity tests. To do this, workweek data recorded since the opening of the Iberian market (July 2, 2007) was used. Despite the geographical proximity of the countries and their access to natural resources, the results provide empirical evidence of different modes of interaction in the market. This outcome could be due to the different sizes of the national systems. The Portuguese electricity generating system does not have an extensive structure to share back-up with Spain via conventional sources. Spain's substantial generation structure could be used to provide intermittent back-up generation for Portugal. Considering the similar supply and demand patterns of the Iberian generation systems, their openness to the other markets with different consumption and generation patterns could allow a more rational utilization of the renewables already deployed and, consequently, bring greater efficiency to the Iberian electricity market.

Demand response programs (DRs) can be implemented with less investment costs than those in power plants or facilities and enable us to control power demand. Therefore, they are highly expected as an efficient option for power supply–demand-balancing operations. On the other hand, DRs bring new difficulties on how to evaluate the cooperation of consumers and to decide electricity prices or rebate levels with reflecting its results. This paper presents a theoretical approach that calculates electricity prices and rebate levels in DRs based on the framework of social welfare maximization. In the authors’ proposal, the DR-originated changes in the utility functions of power suppliers and consumers are used to set a guide for DR requests. Moreover, optimal electricity prices and rebate levels are defined from the standpoint of minimal burden in DRs. Through numerical simulations and discussion on their results, the validity of the authors’ proposal is verified.