Electricity Prices Research Articles

A framework for measuring electricity price literacy: Application to South Korea

Consumers’ knowledge of electricity prices and its effect on their behaviors have been widely studied, yet the comparison and generalization of these findings are challenging due to variations in definition and survey items. This paper proposes a comprehensive framework to assess Electricity Price Literacy by introducing a functional representation that encompasses various electricity pricing systems and categorizing into three knowledge domains to systematically organize previous literature's knowledge measures. To demonstrate the practicality of this framework, a survey was administered to a nationwide sample of 4214 electricity consumers in South Korea. The survey results highlight variations in electricity price literacy across different domains and reveal varying relationships between domain-specific knowledge and behaviors, thereby highlighting the need for a decomposed measure of price knowledge.

Patterns and correlations in European electricity prices.

Electricity markets are central to the coordination of power generation and demand. The European power system is divided into several bidding zones, each having an individual electricity market price. While individual price time series have been intensively studied in recent years, spatiotemporal aspects have received little attention. This article provides a comprehensive data-centric analysis of the patterns and correlations of the European day-ahead electricity prices between 2019 and 2023, characteristically abnormal due to the energy crisis in Europe. We identify the dominant communities of bidding zones and show that spatial differences can be described with very few principal components. Most bidding zones in Continental Europe were brought together during the energy crisis: Correlations increased, and the number of relevant principal components decreased. Opposite effects occur in the Nordic countries and the Iberian Peninsula where correlations decrease and communities fragment.

Impacts of intermittent water supply on household electricity demand: An econometric analysis for the Pune Metropolitan Region, India

Private household water and energy use are closely linked, especially in areas of intermittent water supply where more than one billion people live globally. However, the demand-side Water-Energy Nexus at the household level is often overlooked in empirical econometric studies. Based on a household survey (n = 1872) on water and energy in the Pune Metropolitan Region, India, we find statistical relationships between intermittent water supply and household electricity demand. More than 90 % of the surveyed households use water storage to cope with water supply intermittency, low-income households are particularly affected. Electricity consumption for water access accounts for 27 % of total household electricity consumption. Using a Discrete-Continuous Choice model, we identify significant impacts from factors such as household size and income, electricity price, and particularly the duration of water supply and the use of large water storage on household electricity demand. Our results indicate that households with 24-h water access consume 30 % less electricity than those with 12-h daily access. Extending municipal piped water supply by 1 h per day for all households could reduce total household electricity consumption by 3 %. Our findings suggest that water supply intermittency is a massive cause of unnecessary emissions in cities around the world that has thus far received hardly any attention. The significant amount of electricity used to access water reveals a hidden water affordability problem that can be more prevalent during droughts. Our analyses highlight the demand-side Water-Energy Nexus from an econometric perspective and emphasize the importance of breaking down silos in resource management.

A general sizing methodology of grid-connected PV systems to meet the zero-energy goal in buildings

This work expresses the most general definition of zero-energy building through a set of equations based on measured energy consumption and electricity generation predictions. This helps determining the optimal size of grid-connected photovoltaic systems for upgrading any highly efficient or conventional building into a zero-energy building. Opposed to previous works, which consider only electrical consumptions, this article includes consumption from fossil fuels and district heat and cooling networks. The study also covers environmental parameters and an economic study adaptable to any electricity market structure, contributing to deciding the best electricity tariff to hire.The methodology was applied to passive and conventional single-family houses in Spain. Results showed that the performance of a grid-connected photovoltaic system depends critically on the energy sources imported by the building. In particular, the performance improves in buildings where the electricity represents most of the imported energy. The required minimum size of the photovoltaic system in the conventional house (14 kWp) is far greater than in the passive house (9 kWp). After the recent global increase in electricity prices, annual economic savings of 70.12 % and 49.71 %, and payback periods of 6 and 13 years for the passive and conventional houses, respectively, make the investment profitable.

Фінансові перешкоди впровадженню теплових насосів в централізованому теплопостачанні

The use of powerful industrial heat pumps (HP) is a fairly effective means of rational and environmentally friendly use of energy resources both in individual households and in district heating (DH), as well as in production processes in various industries. Powerful HPs are used in Ukraine much less than in developed European countries. In fact, these are isolated cases. The post-war reconstruction of destroyed cities with their district heating systems gives Ukraine a unique chance to use modern energy-efficient technologies, including HP. As a result, efficiency will improve and DH competitiveness will increase due to the low cost of thermal energy for consumers. Almost all developed countries of the world have government financial support for the HP implementation. The purpose of this study is to determine the level and form of government financial support for projects to implement HP in DHs of Ukraine. Financial modeling of the implementation of heat pump plants shown that they are more expediently to be used in the mode of generating thermal energy than in the mode of regulating the electric load of power systems. It is shown that the projects of heat pump plants, which use the heat of flue gases as a source of low-potential heat (LPH), are the most economically attractive and practically do not require government financial support. Projects of heat pump plants, which use air, ventilation emissions, waste water, soil and groundwater, sea, rivers, and waste heat of technological processes as LPH, are not financially attractive without government financial support, and some of them are even unprofitable. The most appropriate comprehensive financial support is tax incentives and interest compensation on the loan. Sensitivity analysis showed that projects for the implementation of heat pump plants may be the most sensitive to the amount of generated thermal energy, the price of natural gas and electricity. Keywords: heat pumps, low-potential heat sources, district heating, financial obstacles, tax incentives, grants.

A Smart Approach For Budget Deficits PredictionUnder Economic Shocks

Historically, the main behavior of fiscal policy is to distribute resources, income, and expenditures, which are interconnected functions of economic stability. Recently, the scope of public sector economics has expanded beyond budgetary components in parallel with the development of public finance. While the budget reached the economic division form of budget items based on program and performance theories, Budget monitoring, and financial risk management are currently challenging, particularly in the face of monetary policy uncertainty. Financial institutions are crucially concerned with the stability of public finance in low-income countries (LICs) as it contributes to improved investor confidence and fiscal decision-making. Hence, economists investigated uncertainty shocks and contributed to managing financial risks with global and energy uncertainty indices. Furthermore, the maturity of digital transformation and artificial intelligence financial applications catalyzed scholars to examine its contributions in the fiscal distress prediction field. Hence, this research aims to integrate artificial intelligence into financial performance analysis to bridge the gap in budget forecasting. The study was aimed at proposing an Economics Division Uncertainty approach (EDUA), which combined (ARIMA) and (LSTM) models for time series analysis of Nuclear Material Authority expenditures over the previous five years divided into quarterly periods, to achieve efficiency in spending. The (ARIMA) model’s (ADF) results showed that uncertainty indicators are highly significant. The best (p-value) in the first and second differences in (ARIMA) models is (0.0001) for petroleum items, (0.0001) for solar price rates, (0.001) for the US exchange rate, and (0.003) for electricity price rates, when compared to (EDU_LSTM). Both models have similar accuracy rates, with the best being (EDU_ARIMA) (solar price 97%, USD exchange rate 84%). The second study proposed a composite model of four machine-learning tools to enhance financial performance during financial distress. The study collected (12) indicators from general financial literature and corporate studies, utilizing the (XGBOOST, Random Forest, KNN, and Naïve Bayes) models. Comparing the accuracy results for each model presented different accuracy results in the deep learning models over five years of data. The best accuracy score was for Random Forest at (69%), XGBOOT at (68%), and KNN at (68%). We recommended explainable AI as future research to interpret the budget deficit during the fiscal year period. Keywords: Economic Shocks, Uncertainty, Budget Reliability, Financial Distress Prediction, Artificial Intelligence.

The impact of electricity sales side reform on energy technology innovation: An analysis based on SCP paradigm

The Electricity Sales Side Reform (ESSR) is a market-oriented reform directly targeting the electricity terminal, aimed at fostering a diversified and fully competitive market structure, particularly to encourage energy technology innovation (ETI). This study establishes a Structure-Conduct-Performance (SCP) framework, selects Chinese A-share listed companies from 2013 to 2019 as the research subjects, and employs the multi-period difference-in-differences (DID) method to examine the impact of ESSR on ETI. The primary findings are as follows: (1) ESSR effectively promotes ETI, indicating that changes in market structure enhance energy innovation performance. (2) ESSR stimulates long-term innovation incentives rather than short-term profit impulse, signifying that it intensifies market competition, optimizes resource allocation, and guides information disclosure, thereby supporting firms' ETI. This validates the theoretical paradigm of SCP. (3) The positive impact of ESSR on ETI exhibits heterogeneity. On the electricity generation side, the catalytic effect of ESSR on ETI is more pronounced in regions with lower traditional energy endowments and greater green electricity generation potential. On the electricity sales side, the incentive effect is more prominent in regions with higher electricity prices and a lower capacity in the electricity sales market. On the electricity consumption side, the incentive effect is stronger for enterprises with a cleaner energy structure and increased green investments. Therefore, our research findings bridge gaps in the existing literature and offer valuable insights for achieving a high level of ETI and facilitating energy structure transformation.

P2M systems based on proton-conducting solid oxide cells: Future prospects and costs of renewable methanol production

As consequence of the transition towards sustainable energy sources, the future production of liquid energy carriers (e.g. methanol) via H2 supply pathways utilizing water electrolyzers (power-to-liquid) will most likely be based on fluctuating grid electricity or islanded renewable inputs. As a result, these production processes are subject to fluctuating operating conditions and varying production capacities, ultimately leading to uncertainties with respect to their process economics and profitability. Therefore, the impact of different electricity supply-side scenarios (static grid and intermittent grid or renewable supply) on the production economics of power-to-liquid processes needs to be assessed thoroughly for the upcoming decades. Methanol is considered as an essential base chemical which is widely known for its versatility and broad potential use contexts in future chemical industries and energy storage applications. Methanol production pathways powered by renewable electricity sources, also known as power-to-methanol processes, are characterized by low specific life-cycle emissions and are therefore of paramount interest. One possible renewable process chain features proton-conducting high temperature electrolyzers combined with a direct hydrogenation of CO2. In this paper, a techno-economic forecast study of this process chain is presented and specific production costs of renewable methanol under different electricity supply scenarios are determined and discussed for the years 2030 and 2050. The studies showed that flexible grid-supported scenarios through direct spot-market participation and renewable scenarios based on wind onshore production enable the largest production cost reduction potential in the upcoming decades. Minimum production costs of 740 € t−1MeOH (2030) and 415 € t−1MeOH (2050) are determined for a flexible operation of the system with spot-market participation, benefitting from times of low or even negative electricity prices. Among the renewable production scenarios, islanded power-to-methanol systems coupled to wind onshore plants were identified as the most beneficial configuration with ascertained production costs as low as 820 € t−1MeOH and 353 € t−1MeOH by 2030 and 2050, respectively.

Research on the charging strategy of battery swapping stations under the optimization of peak-valley electricity price of electrical technology

Research on the charging strategy of battery swapping stations under the optimization of peak-valley electricity price of electrical technology

Control-oriented dynamic modeling and GPC for single-tower double-circulation wet flue gas desulfurization system

In China, the current situation of large-scale grid integration of renewable energy generation and the government's introduction of capacity-based electricity pricing policies have compelled coal-fired power plants to gradually enhance their load adjustment capabilities and expand their load adjustment ranges. Simultaneously, the national environmental requirements for these plants have become increasingly stringent. However, the economic control of wet flue gas desulfurization (WFGD) systems is still far from meeting practical requirements. In this paper, starting from practical engineering applications and utilizing historical operational data, considering the actual system operation process, the impact of the absorption tower variable-frequency slurry circulation pump on the concentration of sulfur dioxide (SO2) in flue gas under different slurry pH values is investigated. This led to the establishment of a dynamic model for a single-tower double-circulation wet flue gas desulfurization (SD-WFGD) system. Recognizing the significant delay and inertia characteristics of pH value control in the SO2 absorption tank, a desulfurization system pH value desulfurization capacity-based optimization control scheme for the SD-WFGD system is proposed, termed as the Direct Sulfur Control (DSC) strategy. Based on the established dynamic model, a SD-WFGD system series control system is developed using a step generalized predictive control algorithm (GPC) combined with the DSC strategy. Validation results indicate that, compared to SGPC control schemes and traditional PID control schemes, this approach reduces fluctuations in slurry supply and outlet SO2 concentration, significantly enhancing the control performance of the SD-WFGD system. This ensures the safe, stable, economical, and flexible operation of the desulfurization system against the backdrop of flexible operation in coal-fired power plants.

Renewable energy investment under stochastic interest rate with regime-switching volatility

We examine the impact of the interest rate and its characteristics, such as long run mean and instantaneous variance risk (VR), on renewable energy investments in the power sector. The model has stochastic electricity price, stochastic interest rate, and variance regime switches. We show that an increase in the interest rate, while generally increasing the value of a power project, can have a non-monotone effect if the subsidy is sufficiently large. VR increases (reduces) the project value in the high variance regime, if the subsidy is sufficiently large (low). Under a fixed price contract, value declines and it is optimal to delay investment following an increase in the interest rate. The model helps to explain the US offshore industry experience in 2023.

PRO-INFLATIONARY SIGNIFICANCE OF ENERGY COMMODITY AND ELECTRICITY PRICES

Aim: The main aim of this article is to evaluate the influence of electricity prices on the overall economic price level. Methods: The research methodology was carefully designed to encompass various analytical tools, including the graphical representation of data, basic statistical analyses, the computation of the Pearson correlation coefficient with consideration of time lags, and the application of the Granger causality test. Re­sults: The outcomes of the study revealed a significant inefficiency within the market mechanism. Contrary to expectations, the anticipated correlation between electricity prices and inflation (PPI and CPI) indices was found to be statistically insignificant. However, among the examined relationships, a strong and noteworthy connection emerged between coal prices and the PPI inflation index, particularly with a distinct two-month lag in this correlation. Conclusions: Drawing conclusions from the analysis, it became evident that while energy commodity prices, such as coal, do not directly translate into electricity prices and subsequently influ­ence inflation, coal prices do emerge as a significant predictor of inflation. This observation suggests a gap in the intermediate stage of the production cycle, shedding light on a pronounced market inefficiency. The significance of these findings extends beyond the narrow scope of the energy sector. They provide a broader perspective on pricing relationships in the economy, highlighting the limited impact of the market price of electricity on shaping the overall price level. This nuanced understanding constitutes a noteworthy and valuable contribution to the field of economic research, emphasizing the multifaceted dynamics that underlie pricing mechanisms in a complex economic system.

Analysis and comparison of the membrane-cryogenic hybrid process and multistage membrane process for pre-combustion carbon capture based on the superstructure method

The feed in pre-combustion carbon capture has a high CO2 concentration and high pressure, which gives membrane technology and membrane-based hybrid technology a strong separation driving force. Because the membrane-cryogenic hybrid method can inexpensively achieve the high CO2 purity and recovery, it offers a novel approach in addition to traditional separation methods. Nevertheless, it is uncommon to consider the combination of membrane materials and processes. Furthermore, investigations comparing multistage membrane processes with membrane-cryogenic hybrid processes under identical conditions are lacking. Therefore, this paper establishes separately the membrane-cryogenic hybrid processes and multistage membrane process by the superstructure method, which considers the number of membrane stage, membrane material type, and operating parameters simultaneously. The optimal process structures for three-stage membrane, cryogenic-membrane process, and membrane-cryogenic process are investigated and compared. The influences of membrane price and electricity price on the CO2 capture cost are also investigated. Results shows that the CO2 capture cost of cryogenic + membrane process is the lowest (11.69 $/t CO2) of the studied processes at 96 % CO2 purity and 90 % CO2 recovery. The system structure of the cryogenic + membrane process varies with CO2 purity and recovery. Electricity price has a more important impact on the CO2 capture cost than membrane price under the optimized conditions.

THE PRICE DEVELOPMENT OF SELECTED ENERGETIC COMMODITIES

The aim of the paper was to evaluate the price development of electrical energy and inflation as a commodity in the course of the past 10 years. The production of electrical energy decreased from 83.7 TWh in 2010 to 70.6 TWh in 2020 in the Czech Republic in accordance with the results achieved by using the methods of descriptive statistics, time series, correlation analysis and regression analysis. The consumption of energy slightly increased. The proportion of production to consumption declined and consumption was higher than production in 2020. The prices of electricity reveal oscillations influenced by the conflict in the Ukraine. The use of the sources of energy changed, however, without specific trends. The dependence of prices on inflation is considerable and direct. Energy policy ought to deal with the dependence on import and support the domestic production of energy.

Experimental analysis of freezing desalination using gravity and centrifugal methods with economic evaluation of coupled freezing desalination and ice storage system

This study conducted experiments to compare the desalination efficiency of gravity and centrifugal desalination methods for freezing desalination, showing the superiority of centrifugal desalination. Moreover, through experiments on supercooled water production, it was determined that supercooled water can operate stably at an evaporator temperature of −4.5 °C, and its supercooling state can be released using ultrasound. Based on the experimental results, a novel system that combines freezing desalination with ice storage is proposed. The proposed system utilizes supercooled water for ice production and centrifugal post-treatment, using desalinated ice as a cold storage medium to integrate desalination and ice storage. The proposed system showed a 30 % lower total capital cost compared to conventional systems, with a payback period of 38.18 % lower than conventional system (5.42 years). Additionally, the system demonstrated optimal economic performance when utilizing 80 % of its ice storage capacity, with a payback period of 3.38 years, contrasting with 7.62 years when utilizing 20 % storage capacity. The application of this system in four regions with varying peak and off-peak electricity prices, including Guangdong, Fujian, Hainan, and, France revealed that the system demonstrates greater economic viability in areas with significant disparities in electricity pricing.

Decarbonizing urea: Techno-economic and environmental analysis of a model hydroelectricity and carbon capture based green urea production

This study reports a comprehensive techno-economic and environmental assessment of a realistic pathway for decarbonizing the urea industry. The proposed green urea synthesis plant utilizes hydroelectricity-powered electrolysis process and carbon capture from cement flue gas to create sustainable and environmentally friendly production process. Utilizing Aspen Plus and MATLAB, this study first, models the electrolysis, air separation, ammonia synthesis, carbon capture and urea synthesis units, and then evaluates the economic and environmental parameters of the synthesis process. Furthermore, the study highlights the transformative impacts of carbon credit and the renewable energy prices on the profitability metrics of the green urea plant. For the proposed 220 kt/year urea plant, the total energy consumption is 8.18 × 106 GJ/year with the electrolysis unit accounting for half of the energy demand. The estimated total capital investment for the urea plant is 510.79 million USD, with an annual operating expenditure of 156.02 million USD. The urea synthesis unit accounted for half of the total capital expenditure, while electricity contributed to the largest proportion (73%) of the operating expenses. The levelized cost for urea (LCOU) is estimated to be 570.96 USD/t which is approximately 62.2% higher than the urea obtained from conventional process. The electrolyzer unit contributed to 34.4% of the total LCOU. Sensitivity analysis showed that 30% decrease in the electricity price from the base case could lower the LCOU by 27%. The global warming potential of the proposed green urea process is 326.11 kg CO2/t of urea. Lower hydroelectricity prices and carbon credit opportunities significantly improve the economic viability of the green urea production process.

Impact of Multi-Energy System and Different Control Strategies on a Generic Low-Voltage Distribution Grid

The rising electricity costs, cost of space heating, and domestic hot water end up driving consumers toward reducing expenses by generating their electricity through devices like photovoltaic systems and efficient combined heat and power plants. When coupled with thermal systems via an energy management system (EMS) in a Multi-Energy System (MES), this self-produced electricity can effectively lower electricity and heating bills. However, MESs with EMSs can serve various purposes beyond cost reduction via self-consumption, such as reacting to variable electricity prices, meeting special grid connection conditions, or minimizing CO2 emissions. These diverse strategies create unique prosumer profiles, deviating significantly from standard load profiles. The potential threat to the power grid arises as grid operators lack visibility into which consumers employ which control strategies. This paper investigates the impact of controlled MESs on the power grid compared to average households and answers whether new control strategies affect the planning strategies of low voltage grids. It proposes a comprehensive four-step toolchain for the detailed simulation of thermal–electrical load profiles, MES control strategies, and grid dynamics. It includes a new method for the grid impact analysis of extreme and average bulk values. As a result, this study identifies three primary factors influencing distribution power grids by MESs. Firstly, the presence and scale of photovoltaic (PV) systems significantly affect extreme values in the grid. Secondly, MESs incorporating combined heat and power (CHP) and heat pump (HP) units impact the overall grid performance, mainly reflected in bulk values. Thirdly, the placement of an MES with heating systems, especially when concentrated in one feeder, plays a crucial role in grid dynamics. Despite the three distinct factors identified as impactful on the power grid, this study reveals that the various control strategies, despite leading to vastly different grid profiles, do not exhibit divergent impacts on buses, lines, or transformers. Remarkably, the impact of MESs remains consistently similar across the range of control strategies studied. Therefore, different control strategies do not pose an additional challenge to the grid integration of MESs.

Surrogate-based modeling and sensitivity analysis of future European electricity spot market prices

Europe is transitioning towards a sustainable energy economy. Planning these future power and energy systems is multifaceted and complex, requiring long-term projections of future electricity market dynamics. The prognostication of future prices is crucial for power system planning, as these prices will drive investments and impact incentives to build and improve new systems. The price of electricity in different regions impacts transmission investment. Energy systems models have high-dimensional output and are expensive to evaluate, leading to unique challenges in managing the uncertainty in model assumptions. This study presents a novel methodology for determining which cost assumptions are most impactful on the output of a detailed model of the European energy system. Because this model is computationally intensive, only a small number of samples are available to us. A multiple-output support vector regression is used as a surrogate of predicted electricity prices timeseries. We find that solar and wind costs are the primary drivers of electricity prices, but natural gas costs generally drive peak electricity prices and those far in the future.

Optimization of electric charging infrastructure: integrated model for routing and charging coordination with power-aware operations

With the increasing adoption of electric vehicles (EVs), optimizing charging operations has become imperative to ensure efficient and sustainable mobility. This study proposes an optimization model for the charging and routing of electric vehicles between Origin-Destination (OD) demands. The objective is to develop an efficient and reliable charging plan that ensures the successful completion of trips while considering the limited range and charging requirements of electric vehicles. This paper presents an integrated model for optimizing electric vehicle (EV) charging operations, considering additional factors of setup time, charging time, bidding price estimation, and power availability from three sources: the electricity grid, solar energy, and wind energy. One crucial aspect addressed by the model is the estimation of bidding prices for both day-ahead and intra-day electricity markets. The model also considers the total power availability from the electricity grid, solar energy, and wind energy. The alignment of charging operations with the capacity of the grid and prevailing bidding prices is essential.This ensures that the charging process is optimized and can effectively adapt to the available grid capacity and market conditions. The utilization of renewable energies led to a 42% decrease in the electricity storage capacity available in batteries at charging stations. Furthermore, this integration leads to a substantial cost reduction of approximately 69% compared to scenarios where renewable energy is not utilized. Hence, the proposed model can design renewable energy systems based on the required electricity capacity at charging stations. These findings highlight the compelling financial advantages associated with the adoption of sustainable power sources.

Blockchain-Based Joint Auction Model for Distributed Energy in Industrial Park Microgrids

To address the centralized trading demand within industrial parks and the scattered peer-to-peer trading demand outside industrial parks, this paper proposes a blockchain-based joint auction architecture for distributed energy in microgrids inside and outside industrial parks. By combining blockchain technology and auction theory, the architecture integrates the physical energy transactions within industrial parks with the distributed transactions in external microgrids to meet the centralized trading demand within industrial parks and the scattered peer-to-peer trading demand outside industrial parks, optimizing resource allocation and improving system resilience. In the microgrid auction mechanism for industrial parks, considering distributed energy providers (sellers) and distributed energy buyers, an auction mechanism with power transmission distance, average electricity price, and enterprise nature as its main attributes was constructed to maximize social welfare, realizing efficient energy flow in a multi-microgrid environment and enabling coordinated mutual benefits for producers and consumers within the region. Finally, a case study was conducted on the joint auction mechanism for microgrids inside and outside industrial parks, including the impacts of market dynamics and user preferences on electricity prices using different trading methods, the computational results using different trading matching methods (comparing single-attribute and multi-attribute methods), and multi-dimensional verification of user satisfaction with peer-to-peer transactions in a blockchain environment. The effectiveness of the joint trading between physical energy transactions within industrial parks and external microgrids was demonstrated, which could efficiently coordinate energy allocation inside and outside the parks and reduce the cost of energy configuration.