Electricity Market Prices Research Articles

Developing an energy storage systems selection methodology for peak shaving purposes in photovoltaic micro-installations

This study investigates the selection methodology for peak shaving purposes in photovoltaic micro-installations. Due to the increasing share of photovoltaic micro-installations in the national electricity production and the consequent changes in the country's energy policy, the need for energy storage is becoming increasingly apparent. Therefore, based on data collected throughout the study of a photovoltaic installation conducted in years 2020-2024 and data on market electricity prices, the correlation between production, market electricity prices, and energy value was analyzed. The analysis established interconnections between these values and identified factors influencing their changes. It was observed that the market price of electricity is relatively low during hours of increased photovoltaic energy production. By comparing the correlations between energy production and electricity prices over different years, an increasing impact of the increasing share of photovoltaic energy production in national electricity production on the market price of electricity was noted, highlighting the importance of energy storage. For the described household, an energy storage system was selected with a view to meeting the energy demand during hours of higher market electricity prices. The selected model was Lithium-Iron-Phosphate battery with a capacity of 5 kWh. Furthermore, the cost effectiveness of household-scale energy storage systems was examined. Implementing the proposed solutions could result in increased auto-consumption and presumably improve cost-effectiveness of photovoltaic installations in the future.

A tri-level hybrid stochastic-IGDT dynamic planning model for resilience enhancement of community-integrated energy systems

In this paper, a resilience-oriented dynamic planning framework is developed for optimal sizing of the community-integrated energy system components, including photovoltaic system, wind turbine, boiler, power to gas technology, combined heat and power, and storage devices. The proposed framework is formulated as a tri-level linear programming that performs Community-Integrated Energy Systems design under normal conditions at the first level while evaluating system operation during disastrous conditions at the second level. In the third level, re-planning is done based on information-gap decision theory to enhance community-integrated energy systems’ resilience against various natural disasters. Stochastic programming is also employed at all levels to address the uncertainty of electricity market price, energy demand, solar radiation, and wind speed. A detailed P2G system including, a methanation device, electrolysis, and hydrogen storage is designed to improve the resilience of the system. In addition, the power to gas proposed in this model is coupled with a carbon capture unit to mitigate carbon emission by reusing emitted carbon from the flue gas of the boiler and combined heat and power. Various economic metrics and technical constraints are also considered to achieve a realistic design. Numerical simulation results demonstrate that the positive interplay of renewable energy resources and energy storage technologies, specifically P2G, assisted the CIES in maintaining a stable and uninterrupted energy supply during extreme events. The results exhibit that increasing only 10 % of the resilience budget can decrease >93 % of unserved demand and helps reduction of >37 % of carbon emissions.

Enhancing the Reliability of Weak-Grid-Tied Residential Communities Using Risk-Based Home Energy Management Systems under Market Price Uncertainty

This paper evaluates the reliability of smart home energy management systems (SHEMSs) in a residential community with an unreliable power grid and power shortages. Unlike the previous works, which mainly focused on cost analysis, this research assesses the reliability of SHEMSs for different backup power sources, including photovoltaic systems (PVs), battery storage systems (BSSs), electric vehicles (EVs), and diesel generators (DGs). The impact of these changes on the daily cost and the balance of energy source contribution in providing electrical energy to household loads, particularly during power outage hours, is also evaluated. To address the uncertainty of electricity market prices, a risk management approach based on conditional value at risk is applied. Additionally, the study highlights the impact of community size on energy costs and reliability. The proposed model is formulated as a mixed-integer nonlinear programming problem and is solved using GAMS. The effectiveness of the proposed risk-based optimization approach is demonstrated through comprehensive cost and reliability analysis. The results reveal that when electric vehicles are used as backup power sources, the energy index of reliability (EIR) is not affected by market price variations and shows significant improvement, reaching approximately 99.9% across all scenarios.

Forecasting Wholesale Electricity Market Prices Considering Bidding Conditions Using Price Sensitivity

This article proposes a sophisticated forecasting model to predict significant price volatility on the Japan Electric Power Exchange (JEPX) due to the growing influence of solar photovoltaics (PV) in the energy mix. As solar power generation continues to grow, it significantly impacts the daily and seasonal price trends in the market. This method employs a neural network that integrates daily prices, net electricity demand (total demand minus PV output), and underlying time‐series data to determine the objective variable, defined as the deviation from the average price over the past week. Incorporating price sensitivity data from JEPX increases the accuracy of the model. These data reflect how prices respond to market‐specified bid adjustments, including a variety of bidding, providing a nuanced understanding of market responses. The results demonstrate that adding multiple price sensitivities significantly improves the model's ability to detect price spikes and provides a robust tool for transmission and distribution system operators to manage risk and optimize their market strategies in an increasingly renewable energy‐dominated landscape. This approach not only addresses daily and seasonal price fluctuations but also aligns with broader sustainability goals as the share of solar PV generation continues to grow.

Optimization of renewable energy-based integrated energy systems: A three-stage stochastic robust model

The integration of renewable energy into an integrated energy system (REIES) represents a promising approach to achieving clean and low-carbon energy consumption. However, the inherent uncertainty and variability of renewable energy sources and load demands present significant challenges to the operational efficiency and stability of REIES. To address these challenges, we propose a three-stage stochastic robust optimization (TRSRO) model. This model accounts for market electricity prices, source-load scenario probabilities, and output uncertainties to optimize system configurations in REIES. It also includes the optimization of power exchanges with the regional grid and strategic operational scheduling. Initial scenarios of uncertainty variables are generated using the spectral normalized conditional Wasserstein generative adversarial network (SNCWGAN), forming polyhedral uncertainty sets. Comprehensive norm constraints are then applied to capture probability distribution uncertainties within these sets. To improve the efficiency of solving the model, we introduce a two-layer column constraint generation algorithm, considering variable alternate iterations (TLCCG-VAI). The results demonstrate that the proposed method can achieve a 12.16 % reduction in total cost compared to the baseline method. Furthermore, the TLCCG-VAI algorithm reduces runtime by 82.97 % while maintaining the same level of solution accuracy.

Electricity Price and Inflation: Macroeconomic Implications of Real-Time Pricing

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

Artificial intelligence-based power market price prediction in smart renewable energy systems: Combining prophet and transformer models

With the increasing integration of smart renewable energy systems and power electronic converters, electricity market price prediction is particularly important. It is not only crucial for the interests of power suppliers and market regulators but also plays a key role in ensuring the reliable and flexible operation of the power system, particularly during extreme weather events or abnormal conditions. This study develops a hybrid time series forecasting model that combines Prophet and Transformer, which takes advantage of deep learning to provide a new solution for electricity market price forecasting. By introducing the Stacking optimization strategy, this study improves the accuracy and stability of electricity market price sequence prediction. In addition, the study tries to integrate traditional time series forecasting methods (such as the Prophet model) with deep learning models (such as the Transformer model), aiming to make full use of their respective advantages to achieve more accurate and stable predictions. Through experimental evaluation on four electricity market data sets, this study finds that the hybrid forecast model exhibits significant performance improvements in enhancing the accuracy and stability of electricity market price predictions. This method not only provides a more accurate tool for power market price prediction, but also provides solid technical support for the efficient operation and sustainable development of smart renewable energy systems. Experimental results also show that the combination of deep learning models with traditional time series methods and the introduction of Stacking strategies is crucial to improving the performance of power market price prediction, and also helps us better understand and design smart renewable energy systems, price and energy management strategies, thereby providing an effective method for achieving efficient and reliable power and energy transmission.

Distributed Optimization Strategy for New Energy Stations and Energy Storage Stations Considering Multiple Time Scales

The “dual carbon” goal has made it a mainstream trend for new energy stations (NESs) and energy storage stations (ESSs) to jointly participate in market regulation. This paper proposes a multiple time scale distributed optimization method for NESs and ESSs based on the alternate direction multiplier method (ADMM). By first considering the uncertainty of new energy output and the volatility of electricity market prices, a multi time scale revenue model is constructed for day-ahead, intraday, and real-time markets. Then, the objective function is built by maximizing the comprehensive market revenues and is simplified using the synergistic effect of NESs and ESSs. Next, the simplified objective function is solved by the ADMM, and the revenues are maximized while each energy meets the relevant constraints. Lastly, the 33-node network topology is used to illustrate the feasibility of the proposed method. The simulation results show that after optimization, the output of NESs and ESSs can coordinate work in day-ahead, intraday, and real-time markets, while the abandonment power of wind and light is significantly improved.

The impact of metering methods on collective self-consumption: Insights from multi-dwelling buildings in Finland

Globally, legislative reforms have significantly promoted the adoption of solar photovoltaic systems. In many European countries, updated legislation encourages the formation of energy communities and collective self-consumption in residential buildings. This study investigates the financial viability of solar photovoltaic systems in Finnish multi-dwelling buildings, focusing on the impact of recent legislative enhancements supporting collective self-consumption through virtual net-metering, known as the credit calculation model. By analyzing hourly consumption data from two properties in 2018 and 2020, and incorporating simulated photovoltaic production with electricity market prices, our findings highlight the advantages of virtual net-metering. Self-consumption ratios increased from 15% to 38 %, and annual revenue improved by 10 EUR/kWp to 29 EUR/kWp. Comparing the credit calculation model with behind-the-meter approaches using joint electricity subscriptions revealed self-consumption ratio differences of 5 % to 15 % for identical system sizes. Additionally, electricity purchasing cost savings of 16 % to 19 % were identified with behind-the-meter, increasing further compared to costs from other Finnish distribution system operators. This underscores the economic benefits of such arrangements and highlights potential savings across different regional pricing models. These insights demonstrate the financial benefits of collective self-consumption and joint electricity subscriptions in multi-dwelling contexts, signaling the need for extended research across diverse settings to validate these findings.

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.

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.

Optimal Power Flow of Power System with Unified Power Flow Controller Using Moth Flame Optimization with Locational Marginal Price

Consideration of transmission line capacity and the optimal power flow (OPF) determines the locational marginal price (LMP), which in turn determines the performance and profitability of a producing unit. Reducing the total cost of the generators can lead to a drop in the market price of electricity. It is recommended to use numerical and repetition-based approaches for solving power flow equations due to their nonlinear nature. In order to achieve the ideal power flow at an affordable price, this paper employs a Moth Flame Optimization (MFO) to solve the equations. We then enhance the MFO’s structure to make it more effective at performing the simultaneous calculations of power passing through transmission lines. Flexible AC transmission systems (FACTS) tool that has been utilized to overcome this problem is the Unified Power Flow Controller (UPFC). Lastly, the proposed MFO algorithm would include the following parameters in its output: bus voltages, line losses, generated power, total generating expenditures, and generator profits. In this work, the proposed method is tested on the IEEE 57-bus network also shows that it improves upon the OPF problem.

Mapping the Wholesale Day-Ahead Market Effects of the Gas Subsidy in the Iberian Exception

Amidst the global energy crisis in 2022, the Spanish and Portuguese governments introduced a subsidy to natural gas (“the Iberian exception”), attempting to lower the wholesale electricity market prices, with the understanding that gas-fired-combined cycle gas turbines (CCGTs) are price-setting technologies most of the time, directly or indirectly. The subsidy succeeded in lowering the market price but induced several other effects, such as (1) the increase in cleared energy in the Spanish market (mostly produced with gas), (2) the bias in the import/export cross-border position between Spain and France (Spain became a net exporter to France immediately), or (3) the consequent increase in congestion rents, which serve to lightly finance the subsidy, among other effects. This paper provides a framework for clustering the different effects based on the market participation phases: the subsidy, the market bidding, the market results, and surplus and rents. Moreover, this paper builds on the theoretical market models, with and without subsidies, and with and without cross-border exchanges. Based on the real market bids, the subsidies, and the generators’ data, we reconstruct the supply and demand curves and simulate the counterfactual market scenarios in order to illustrate and quantify the effects. We highlight the quantification of the theoretical effect of the transfer of rents, from non-fossil to fossil fuel producers, induced by the gas subsidy.

The risks of electrified district heating in Finland's cold climate

Decarbonizing fossil fuel-dependent district heating systems is essential for achieving carbon neutrality, particularly in cold climates. In Finland, district heating operators are concentrating on electrifying these systems. However, the 2022 energy crisis in Europe has highlighted concerns about heat production costs and the security of heat supply with this approach. This study examines the economic feasibility and risks associated with electrified district heating systems and the early decommissioning of thermal power plants in the interconnected district heating systems of Helsinki, Espoo, and Vantaa. The case study is simulated and optimized to find the least-cost solution while meeting heat demand for various 2025 scenarios, assuming high energy market prices as in 2022 and more normal circumstances. Simulation results indicate that shutting down fossil fuel-based combined heat and power plants in Helsinki and Espoo would create a shortfall in base-load heat production, increasing dependency on heat imported from Vantaa. Both cities are expected to employ more cost-competitive biomass boilers to mitigate the reduction in coal-based heat production, which would decrease operational costs but also reduce revenue from electricity sales due to reduced combined heat and power capacity. Consequently, Vantaa is likely to benefit from its substantial storage and waste and biomass combined heat and power capacity, enabling efficient heat production at reduced costs. Across both scenarios, the analysis demonstrates a significant decrease in emissions and less reliance on imported fuels, highlighting the potential benefits of electrified district heating systems even amidst high electricity market prices.

Optimal modes of operation and product cost allocation in sugarcane steam cogeneration plants

Sugar and ethanol production from sugar cane is one of the most competitive sectors of Brazil’s economy. The bagasse generated during the production process is used as fuel in cogeneration plants that provide thermal and electrical energy to the process. In the last decades, many sugar cane factories have produced a surplus of electricity that may be sold to the grid as a new product. This paper applies energy billing optimization and thermoeconomic analysis to a sugarcane steam cogeneration plant to determine the optimal operating mode of the plant, unveils the cost formation process of its internal products (refinery heat, process heat, and consumed electricity), and examines how the results are affected by: (i) the demand for the plant’s energy services, (ii) the availability of bagasse, and (iii) the selling price of surplus electricity. The thermoeconomic analysis involves a detailed study of the cost formation process, which is achieved through the decomposition of the steam cycle of the cogeneration plant into subcycles. Three main subcycles, in addition to the deaeration cycle and other auxiliary subcycles, have been identified: the cogeneration cycle generating work in the high-pressure turbine and refinery heat (subcycle one), the cogeneration cycle generating work in the high- and medium-pressure turbines and process heat (subcycle two), and the condensing cycle that generates only work in the high-, medium-, and low-pressure turbines (subcycle three). These subcycles make up the productive structure of the steam cogeneration plant and explain how water/steam goes through energy conversion processes from the bagasse energy to the heat and electricity produced. Both the optimization model and the thermoeconomic analysis serve as valuable tools for planning in response to potential changes in bagasse and electricity market prices, as well as fluctuating product demand conditions. In the base case, combining optimization with thermo-economic analysis, the unit monetary cost of the final products has been determined: heat for refinery (8.85 R$/MWh), electricity sold (183.60 R$/MWh), internally consumed electricity (41.51 R$/MWh), and process heat (8.85 R$/MWh).

Improvement of Economic Integration of Renewable Energy Resources through Incentive-Based Demand Response Programs

The integration of renewable generation presents a promising venue for displacing fossil fuels, yet integration remains a challenge. This paper investigates Demand Response (DR) as a means of economically integrating Renewable Energy Resources (RERs). We propose Incentive-Based DR (IBDR) programs, particularly suitable for small customers. The uncertainties in the electricity market price pose a challenge to IBDR programs, which is addressed in this paper through a novel and robust IBDR approach that considers both the electricity market price uncertainties and customer responses to incentives. In this paper, scenarios are simulated premised on the Western Electricity Coordinating Council (WECC) 240-bus system in which coal-fired power plants become inactivated, while the RER contribution increases in the span of one year. The simulation results indicate that the proposed IBDR program mitigates the issues associated with renewable expansion, such as utility benefit loss and market price volatility. In addition, the proposed IBDR effectively manages up to 30% of errors in day-ahead wind forecasts that significantly reduce financial risks linked to IBDR programs.

Solving large-scale electricity market pricing problems in polynomial time

In centralized wholesale electricity markets worldwide, market operators use mixed-integer linear programming to solve the allocation problem. Prices are typically determined based on the duals of relaxed versions of this optimization problem. The resulting outcomes are efficient, but market operators must pay out-of-market uplifts to some market participants and incur a considerable budget deficit that was criticized by regulators. As the share of renewables increases, the number of market participants will grow, leading to larger optimization problems and runtime issues. At the same time, non-convexities will continue to matter, e.g., due to ramping constraints of the generators required to address the variability of renewables or non-convex curtailment costs. We draw on recent theoretical advances in the approximation of competitive equilibrium to compute allocations and prices in electricity markets using convex optimization. The proposed mechanism promises approximate efficiency, no budget deficit, and computational tractability. We present experimental results for this new mechanism in the context of electricity markets, and compare the runtimes, the average efficiency loss of the method, and the uplifts paid with standard pricing rules. We find that the computations with the new algorithm are considerably faster for relevant problem sizes. In general, the computational advantages come at the cost of efficiency losses and a price markup for the demand side. Interestingly, both are small with realistic problem instances. Importantly, the market operator does not incur a budget deficit and the uplifts paid to market participants are significantly lower compared to standard pricing rules.

Optimization and analysis of methanol production from CO2 and solar-driven hydrogen production: A Danish case study

This paper presents a comprehensive techno-economic analysis of the world's first large-scale commercial Power-to-X (PtX) plant in Kassø, Denmark. This innovative facility combines solar photovoltaic (PV), an electrical grid, green hydrogen production, methanol synthesis, and district heating in an integrated system. We conduct an in-depth analysis to explore optimal operating conditions, the potential for sector coupling within the power and heat sectors, and the economic feasibility of the integrated system. In particular, we study different scenarios considering changes in solar PV power generation, electricity market price, CO2 price, and methanol price to explore their impacts on the system performance. Our results show that the electrolyzer's operation, a pivotal component of the system, heavily depends on the electricity spot price as well as the methanol price. Based on the green production of methanol, a premium price of 4800 DKK/t (0.64 €/t) is considered in this study (selling price of 8400 DKK/t). The results show that in case of high electricity prices (2022 spot market) or no premium price for methanol, the optimum annual production of methanol decreases by 58% and 67%, respectively. Furthermore, we observe that the variability of solar power output significantly impacts the plant's performance and economic viability, as it supplies about 30% of the plant's power consumption. Although the results are unique to the Kassø plant, the study's techniques and insights can be applied to other comparable systems around the world. The study also highlights how crucial it is to take market, climate, and technical factors into account when developing and running integrated energy systems.

Energy, economic, and environmental impacts of electricity market-oriented reform and the carbon emissions trading: A recursive dynamic CGE model in China

Electricity market-oriented reform and carbon emissions trading, as the main policies implemented in China's power sector, have a profound impact on China's green and low-carbon transition by promoting resource allocation efficiency and internalizing external environmental costs, respectively. Therefore, this paper constructed a dynamic recursive CGE model to measure the energy, economic, and environmental impacts of carbon emissions trading and electricity market-oriented reform from electricity market structure and pricing mechanism. The results show that the two policies have a synergistic effect on industrial structure optimization, energy consumption structure optimization, and carbon emission reduction. Specifically, the share of value-added of the tertiary sector, the growth rate of coal consumption, the share of thermal power consumption, and carbon emissions in 2030 are reduced by 5.70 %, 18.29 %, 2.55 %, and 2.838 trillion tons, respectively, compared with the scenario without carbon emissions trading and electricity market-oriented reform. However, imperfectly competitive market structures outperform perfectly competitive market structures in energy consumption structure and carbon emission reduction. Electricity price regulation is also more beneficial to GDP growth and increase in residential disposable income compared to price liberalization. To this end, returning emission trading scheme revenues to residents can effectively mitigate the adverse impacts caused by market-oriented reforms.

Proposal and Study of a Pumped Thermal Energy Storage to Improve the Economic Results of a Concentrated Solar Power That Works with a Hybrid Rankine–Brayton Propane Cycle

This work proposes a pumped thermal energy storage (PTES) integrated into the power block of a concentrated solar power plant. The power block operates under a Hybrid Rankine–Brayton (HRB) cycle using propane as the working fluid. During PTES charging, some thermal energy is obtained from a dedicated compressor (additional to that of the HRB cycle), which is stored. During discharge, both compressors (HRB and PTES) are off, restoring the consumed energy and resulting in about a 13% increase in nominal power output. The system is also able to store thermal energy that would otherwise be rejected through the condenser if the PTES were turned off, leading to efficiency improvements in some cases. Considering the 2022 Spanish electricity market prices, the proposed PTES integration with 4 h of storage is feasible. The levelized cost of storage is calculated and compared to those of other PTES systems, achieving around a 40% reduction compared with an equivalent PTES Rankine. These results encourage future studies where the proposed PTES could be integrated into other power cycles that include a recompression process.