Electricity Market Research Articles (Page 1)

This paper explores the techno-economic implications of Iranian policy instruments designed to promote large-scale photovoltaic (PV) power plants. As global energy demands rise and environmental concerns intensify, transitioning from conventional fossil fuels to renewable energy sources has become imperative. This study investigates the current state of Iran's electricity market and the effectiveness of its power purchase policies in facilitating PV development. Despite possessing substantial solar energy potential, Iran faces significant challenges, including financial constraints and inconsistent energy policies, which hinder the swift adoption of renewable technologies. The research utilizes a comprehensive approach to assess these barriers and proposes strategic financial solutions to enhance investor confidence and participation in the solar energy sector. Notably, this study contributes to the existing literature by providing a detailed analysis of Iran's unique socio-economic context and its impact on the implementation of renewable energy policy. The findings underscore the necessity for cohesive governmental support and innovative financing mechanisms to unlock Iran's vast solar resources, ultimately paving the way for sustainable energy solutions that align with global carbon neutrality goals.

The article presents the results of empirical research into expenditure on electricity and the dependencies of the share of these expenses with regard to the features of farming households in Poland. The source material came from empirical research conducted on a random sample of 480 farming households in Poland (each exceeding 5ha of UAA), with multiple correspondence analysis (MCA) used in the analyses. Through a combination of survey methods and MCA, this study aims to assess electricity usage in farming households, with particular emphasis on identifying the portion of energy costs directly linked to agricultural operations. Statistical analysis demonstrated the existence of strong dependencies between the share of expenditure on electricity from agricultural production and the economic size of a farm (φ2 = 0.2655), the district (φ2 = 0.2561), and the agricultural production system (φ2 = 0.1070). The research shows that expenditure on energy constitutes a considerable percentage of total expenses on energy in the studied farming households. The research results may become a point of reference for other techniques and tools used in energy measurements at the micro-economic level, including the combining of various approaches and the modifying of techniques and tools developed earlier. The results can also be an important source of information for the economic and institutional sphere, including operators on the electricity market.

ABSTRACT Current policies facilitate the involvement of prosumer aggregators in the wholesale market on the distribution side. However, the wholesale market’s disregard for distribution network security constraints may lead to potential security issues when aggregators deliver the awarded power. Furthermore, the electricity market operates within a multi‐party offer framework, rendering the competitive behaviour of aggregators’ rivals unpredictable. The relationship between locational marginal prices and the offer of prosumer aggregators is further complicated by the coupling of transmission and distribution systems. These factors contribute to the complexity of the decision‐making process for prosumer aggregators. This paper introduces a comprehensive offer model for prosumer aggregators that incorporates network security and the intricate market environment. Initially, we develop optimisation decision‐making models for various market participants, providing examples of their decision‐making behaviours. Subsequently, we explore the interactive dynamics among market entities and formulate a mathematical optimisation model for prosumer aggregators that integrates network security constraints and the complexities of market decision‐making. Additionally, we establish a multi‐party game model that considers offer strategies of all participants. Finally, we propose simplified solution strategies to address the challenges associated with diverse application scenarios. Case studies conducted on a 69‐bus distribution network validate the effectiveness of the proposed model.

The ever-growing load demand and irregularity in the electricity load profile, especially in residential areas, have led to a surge in electricity prices. Rapid advancements in the electricity market and Renewable Energy Systems (RES) have spurred extensive research into energy management through demand side management (DSM) expedited by Smart Home Energy Management Systems (SHEMS). In countries such as Taiwan, where Real-Time-Pricing (RTP) tariff schemes are used, efficient energy management can be achieved by utilizing optimization algorithms. The focus of this study was to use Genetic Algorithm (GA), a nature-inspired optimization algorithm, to achieve efficient energy management in smart homes via Multi-Objective Optimization (MOO). Three objectives are optimized for the home user: namely electricity cost, user comfort, and peak-to-average ratio (PAR). The scheduling problem not only aims for maximum user satisfaction but also considers two user interruption parameters: with penalty and without penalty. The results have shown a 14.56% cost reduction in scheduling without user interruption, 18.62% cost reduction in scheduling considering user interruption (with penalty), and 15.69% cost reduction in scheduling considering user interruption (without penalty). The maximum user comfort was improved by 67.48% (without user interruption), 62.62% (user interruption with penalty) and 41.65% (user interruption without penalty), and the PAR was reduced by up to 51.53% on average. Despite the stochastic nature of electricity consumers, with an optimization system, the cost and peak demand can be curtailed significantly while still maximizing their comfort level.

A game-based coupled electricity and carbon trading market considering competitive bidding strategy and uncertainty of demand response

Integration of data-driven T-spherical fuzzy mathematical models for evaluation of electric vehicles: Response to electric vehicle market demands

Electric vehicle market dynamics: A multi-agent approach to policy, infrastructure, and consumer behavior

Demand and supply curve forecasting using a monotonic autoencoder for short-term day-ahead electricity market bid curves

Learning-Based inter-area trading strategies for transmission system operators in two-tier regional electricity market

A restructured Moroccan electricity market and its interaction with the Iberian power market

Selective futures hedging in the Nordic electricity market

Reveal branch flow scarcity from limited electricity market information

Incorporating financial entities into spot electricity market with renewable energy via holistic risk-aware bilevel optimization

What drives consumers to switch retailers? Evidence from the Alberta electricity market

With the rapid development of electricity–carbon markets and global renewable energy deployment, accurately quantifying and fairly compensating carbon reduction benefits of energy has become crucial for low-carbon energy transformation. Current methods suffer from mechanism, price signal fragmentation, and unclear carbon responsibility attribution. To address these challenges, this paper proposes a novel renewable energy carbon emission reduction benefit evaluation method considering electricity–carbon coupling. Firstly, a Copula-based methodology to construct typical electricity–carbon price scenarios is developed, revealing significant correlation between electricity and carbon prices (Spearman coefficient: 0.730, Kendall coefficient: 0.620). Second, a power dispatch model incorporating BEKK-GARCH-based price linkage analysis is established, quantifying the coupling risk coefficient at 0.54. Third, an improved Shapley value method with correction factors including responsiveness, output stability, and marginal carbon reduction benefits is introduced to accurately evaluate renewable energy contributions. Case study results demonstrate that the proposed method achieves 20.3% system cost reduction, and 7.10–9.90% carbon reduction improvements with energy storage. Practical testing directions include pilot implementations in regional power grids followed by scaling to larger networks, with subsequent applications in regulatory carbon market design, utility optimization planning, and renewable energy project evaluation. This work provides essential tools for global electricity–carbon market integration and carbon neutrality achievement in power systems.

Are environmental regulations the primary driver of rising electricity prices? Evidence from the Regional Greenhouse Gas Initiative (RGGI) suggests a more nuanced reality. This paper examines the impact of RGGI on wholesale and retail electricity prices using a difference-in-differences framework. We analyze three key policy events—the 2005 announcement, the 2009 implementation, and the 2014 adjustment of the emissions cap—drawing on detailed panel data from power plants in both RGGI and non-RGGI states. Our results indicate that wholesale electricity prices in RGGI states did not increase following the 2005 announcement relative to non-RGGI states. By contrast, retail electricity prices rose by about 11% in the short run, coinciding with electricity market restructuring, though this retail price gap declined over time. Over the subsequent decade, RGGI states achieved substantial reductions in CO2 emissions alongside a transition to cleaner generation technologies. Importantly, the industry’s response to environmental regulation did not immediately affect electricity prices. However, as the emissions cap tightened, price effects became more pronounced: following the 2014 adjustment that reduced the cap to roughly 50% of its 2008 level, wholesale prices increased by 0.68 to 5.57 cents/kWh. These findings suggest that while the short-run effects of environmental regulation on electricity prices are limited, more stringent caps over time can lead to measurable price impacts.

This study presents a scenario analysis of Kazakhstan’s electricity market using the PyPSA-KZ model, with a focus on the integration of renewable energy sources (RES). As Kazakhstan transitions towards a low-carbon economy, this study evaluates the technical and economic implications of increasing RES penetration under various scenarios, ranging from 10% to 60% RES shares, with projections targeted for the year 2030. The study simulates system behavior across scenarios and analyzes key indicators, including total system cost, electricity tariff, generation mix, thermal ramping, and CO2 emissions. Results indicate that up to 30% RES integration is feasible without significant structural changes, delivering reduced system costs and emissions. However, scenarios beyond 30% reveal growing flexibility challenges, necessitating investment in grid modernization, energy storage, and flexible backup capacity. The model outcomes are benchmarked against the International Energy Agency’s 2030 carbon neutrality scenarios and show strong alignment, particularly at 45% RES share. Comparative insights are also drawn from international experiences in Denmark and China. This research demonstrates that the PyPSA-KZ model is a powerful tool for planning Kazakhstan’s energy transition and offers data-driven recommendations to support national energy security and climate goals.

With the increasing penetration of distributed energy resources (DERs) in distribution networks, traditional passive distribution systems are evolving into active and flexible systems capable of participating in the transmission-level energy market. Integrating distribution networks into a transmission-centric market-clearing model introduces challenges, such as capturing internal operational constraints and reflecting the economic features of distribution systems. To this end, this paper proposes a market integration method for distribution networks based on a feasible region and an accompanying bidding strategic bidding method to enable their efficient participation in the transmission-level electricity market. With a two-stage adaptive robust optimization framework, the feasible region that preserves operational characteristics of the distribution system and ensures the satisfaction of operational constraints within the distribution system is first depicted. The feasible region appears as time-coupled box-shaped regions. On this basis, a strategic bidding method is proposed based on the nested segmentation of the feasible region, jointly considering power and reserve. With it, the bidding prices of energy and reserve can be prepared, and then, together with the feasible region, can be smoothly integrated into the transmission-level market model. Numerical case studies demonstrate the effectiveness of the proposed method.

With the continuous development of China's rural economy and the deepening of power system reform, the stability and popularity of power supply in rural areas have significantly improved, providing solid guarantees for the lives of rural residents and agricultural production. However, the collection of electricity bills in rural areas faces many challenges and has become an important factor affecting the healthy development of the power industry and the stability of the rural electricity market. Rural areas have vast territories and scattered residential areas, which brings great inconvenience to the collection of electricity bills. Electricity workers need to spend a lot of time and energy traveling through villages and households to collect fees, resulting in low work efficiency and high costs. At the same time, some rural residents are influenced by traditional concepts and have insufficient understanding of the attributes of electricity commodities, lacking awareness of timely payment, and even delaying payment or maliciously owing fees. In addition, the level of rural economic development varies greatly, and some impoverished areas or economically disadvantaged families find it difficult to pay electricity bills on time and in full, which increases the difficulty of electricity bill collection. Moreover, information in rural areas is relatively closed, and communication between the power sector and users is not smooth, resulting in insufficient understanding of electricity policies, charging standards, etc. by users, which can easily lead to misunderstandings and disputes, further hindering electricity fee recovery. As a core business link of power enterprises, electricity fee recovery is directly related to the company's capital turnover and economic benefits. The difficulty in recovering electricity bills in rural areas not only affects the normal operation of power enterprises, but also hinders the continuous investment and improvement of rural power infrastructure. Therefore, in-depth exploration of the difficulties in electricity fee recovery in rural areas and the proposal of effective basic risk management methods are of great practical significance for safeguarding the legitimate rights and interests of power enterprises and promoting the healthy and orderly development of the rural electricity market.

Accurate medium-to-long-term electricity price forecasting constitutes a critical prerequisite for electricity market participants to formulate optimal bidding strategies and mitigate energy procurement expenditures. However, medium-to-long-term electricity price forecasting encounters challenges including high-dimensional data acquisition difficulty, forecasting mode complexity with poor adaptability, and high volatility of the forecasted prices. In this manuscript, a data-driven-model-based approach for medium-to-long-term electricity price forecasting is proposed. Firstly, the key data sets are screened from the importance assessment based on the decision tree, and the historical electricity price, the coal price, and the natural gas price are chosen as the key price data to establish the medium-to-long-term electricity price forecasting model to reduce the forecasted data dimension effectively. Secondly, the key data sequences are denoised with the volatility reduction through combining Fast Fourier Transformation (FFT). Then, GWO-CNN-LSTM-Attention model, combining Gray Wolf Optimization (GWO) algorithm, Convolutional Neural Network (CNN), Long-Short-Term Memory (LSTM) network and Attention mechanism, is constructed for better forecasting performances and enhanced adaptability. Finally, the price time series are separated into the primary and residual frequencies. The primary frequency of the historical local electricity price is fed into GWO-CNN-LSTM-Attention algorithm to forecast the trend of the electricity price, while the primary frequencies of the historical coal price and the natural gas price are fed into GWO-CNN-LSTM-Attention algorithm to forecast the fluctuation of the electricity price, contributing to the high accuracy of the price forecasting in long term. The proposed FFT-GWO-CNN-LSTM-Attention (FGCLA) algorithm facilitates the dimension reduction of the forecasting dataset, the adaptability enhancement of the forecasting model, and the effective suppression of the high volatility, so as to improve the forecasting accuracy of the electricity price. The proposed algorithm is compared to the traditional LSTM and Transformer algorithms through simulation cases, with forecasting accuracy improvements of 57.21% and 49.69% respectively on average, concluding that the proposed algorithm could effectively reduce the forecasting errors of the medium-to-long-term electricity price.