Competitive Electricity Market Research Articles

Stochastic Optimal Strategies and Management of Electric Vehicles and Microgrids

This study combines the Nash–Cournot competition model and the stochastic optimization model to examine the impact of electric vehicle (EV) quantity fluctuations on microgrid operations, aiming to optimize energy usage in a competitive electricity market. Integrating distributed energy resources and bidirectional charging, microgrids offer a novel approach for energy optimization, aiding in renewable energy generation, peak demand management, and emission reduction. Empirical evidence highlights benefits in Taiwan’s electricity market and net-zero emissions target by 2050, with a case study demonstrating enhanced local renewable energy generation due to EVs and microgrid integration. As the number of EVs increases, electricity sales from microgrids decline, but electricity purchases remain stable. The degree of electricity liberalization also influences the supply and demand dynamics of the electricity market. Microgrids selling electricity only to the main grid increases total power consumption by 65.55 million MWh, reducing the market share of the state-owned utility (Taipower). Conversely, allowing retailers to purchase from microgrids increases total consumption by 30.87 million MWh with a slight market share decrease for Taipower. This study contributes to providing an adaptable and flexible general model for future studies to modify and expand based on different scenarios and variables to shape energy and environmental policies.

Competitive Analysis at the Cournot-Nash Equilibrium of an Interconnected Network

The electric power network has long been subject to monopolization, encompassing production, transportation, and distribution sectors. However, recent liberalization efforts have introduced competition into the electricity market. To understand and manage this competition, game theory, a prominent tool in economics, is frequently employed. Specifically, competition within the electricity market has been analyzed through various game-theoretical models, including Bertrand's atomicity, Cournot's homogeneity, and Nash's research competition. These models aim to achieve the Cournot-Nash equilibrium, where each participant in the market makes optimal decisions given the strategies of others. To effectively allocate production and ensure a balance between supply and demand, as well as to maintain the stability of the interconnected network, one has adopted a method that combines Load Flow techniques with game theory principles. This hybrid approach enables a strategic distribution of power production, taking into account the competitive dynamics of the market. By integrating these methodologies, one can address the complexities of competition while ensuring efficient and stable operation of the power grid. This innovative approach not only enhances the management of electricity production and distribution but also fosters a more competitive and resilient power network. Moreover, the application of game theory in this context allows for a deeper understanding of strategic interactions among market participants. It helps in predicting behaviors, formulating strategies, and anticipating market changes, thus providing a robust framework for decision-making. This is particularly crucial in a liberalized market where multiple entities vie for market share and profitability. By employing game-theoretical insights, one can simulate various market scenarios, optimize resource allocation, and enhance overall market efficiency. Furthermore, this approach supports the integration of renewable energy sources by ensuring that their variable nature is accommodated within the grid's operational dynamics. In summary, the intersection of game theory and load flow methods offers a comprehensive solution to the challenges posed by a competitive electricity market, paving the way for a sustainable and efficient energy future.

Price responsiveness of solar and wind capacity demands

Accurate estimates of the price responsiveness of residential, commercial, and industrial electricity demands are essential for energy policy modelling, integrated resource planning, and determining a competitive wholesale electricity market's generation levels, prices, and capacity investments. Hence, we estimate the own-price elasticities of solar and wind capacity demands of a load serving entity (LSE) that provides retail electricity service, thereby answering two interrelated research questions: (1) does solar capacity demand far exceed wind capacity demand? and (2) are solar and wind capacity demands price-elastic? Inspired by the theory of input demand under input price uncertainty, our innovative methodology integrates (a) wholesale spot energy price forecasts by time of day; (b) pseudo data found by minimizing a LSE's annual risk-adjusted budget for procuring solar and wind capacities; and (c) econometric analysis of (b) to estimate the extent of substitutability between solar and wind capacities and the own-price elasticities of solar and wind capacity demands. Using Texas as an illustrative example, we find that when solar and wind power purchase agreements have similar energy prices, solar capacity demand is approximately four times wind capacity demand. Further, the own-price elasticity estimates are −5.34 for solar capacity demand and −5.65 for wind capacity demand. As a result, solar and wind capacity demands tend to substantially grow (shrink) in response to declining (rising) solar and wind energy prices. This lends support to proposals to raise solar and wind energy prices for mitigating the adverse effects of large-scale variable renewable energy development on an electric grid's efficient operation and system reliability. However, adopting such proposals also slows the grid's pace of decarbonization, thus underscoring the policy and regulatory challenges in the quest for a clean and sustainable electricity future. Hence, our policy recommendation of price managing solar and wind capacity demands is a topic of policy debate that deserves the attention of an electric grid's stakeholders.

Optimization model of monthly continuous bidding transaction in electricity market considering risk hedging

In order to further alleviate the problems of large assessment deviations, low efficiency of trading organisation and difficulties in system optimisation in medium- and long-term market trading, the article proposes an optimisation model for continuous intra-month bidding trading in the electricity market that takes into account risk hedging. Firstly, the current situation of market players’ participation in medium and long-term trading is analysed; secondly, the impact of contract trading on reducing operational risks is analysed based on the application of hedging theory in the primary and secondary markets; finally, the continuous bidding trading mechanism is designed and its optimisation effect is verified. The proposed model helps to improve the efficiency of contract trading in the secondary market, maintain the stability of market players’ returns and accelerate the formation of a unified, open, competitive and well-governed electricity market system.

Business decision-making of power generators in competitive electricity market

This paper presents a theoretical framework for the business decision-making process of the power generators as price takers when considering the participation of energy storage. The framework assesses rational valuation, optimal sales strategies, and hedging options for power plants with and without a gross sales constraint. The valuation and optimal sales strategy problems are analyzed using a risk-neutral pricing approach, dynamic programming principles, and the trinomial tree model suitable for the regime switching model. The formulation of a price risk hedging scheme flexible and widely used over-the-counter electricity derivative, the electricity contract for difference, as a tool for hedging electricity spot price risk. The minimum variance hedge ratio and its corresponding hedging efficiency formula are derived. In the section of numerical simulations, we first use the EM algorithm to calibrate the electricity spot model based on electricity spot price data of Nord Pool. Numerical simulations are then conducted on the operational decision-making of power generators under three different forms of energy storage. The results of the simulations provide a basis for power generators to evaluate the real-time value of power plants, to select optimal real-time power sales, and to determine the optimal timing of power plant transfer and storage methods.

The Application of Digital Technology in Tesla Automobile Industry: Key Factors and Global Market Expansion

This study aims to explore Tesla’s success factors and its key moves in its global market expansion. Tesla is unique because it is not just a car manufacturer but a software-driven technology company. This article first introduces Tesla’s application in a single operating system, integrating all software into a logical system to improve vehicle operating efficiency and user experience. Secondly, this study discusses Tesla’s Supercharger network and how this network can provide users with convenient charging solutions to promote the popularity of electric vehicles. Next, this article explores Tesla’s strategy of building a factory in China and how this move helps Tesla maintain a competitive advantage in the global market. Finally, this article highlights Tesla’s closed loop of hardware and software and how it continuously improves the performance and functionality of the car through continuous iterations of software. The findings of this study will help understand Tesla’s business model and success and provide useful inspiration for other companies in the increasingly competitive electric vehicle market.

Power generation mix and electricity price

Electricity prices are influenced by the composition of the power generation mix. Firms that operate in sectors that are energy-intensive, such as manufacturing, metallurgy or oil and gas, are vulnerable to variations in electricity price. Everyday citizens, faced with increasing costs of living, also experience a strain on their finances when electricity prices rise. As many countries move towards a net-zero energy transition, energy policy enacted to change the power generation mix of electricity markets is under increased scrutiny. Little research has evaluated both the effect of all modes on electricity price and the interactions between the modes in the context of a competitive electricity market. Using hourly data from the Canadian province of Ontario between 2015 and 2022, we examine the effect of the variation in outputs of the different modes of electricity generation in a competitive electricity market. We find that increased solar and wind electricity production induces lower electricity prices.

Modified Genetic Algorithm for the Profit-Based Unit Commitment Problem in Competitive Electricity Market

Modified Genetic Algorithm for the Profit-Based Unit Commitment Problem in Competitive Electricity Market

Electricity trading of multiple home microgrids through V2X based on game theory

Currently, a significant number of householders are contentedly participating in energy markets integrated with renewable energy sources (RES) to control global warming. Further, the high penetration of e-mobility influences the net zero concepts globally. However, uncoordinated scheduling of electric vehicles (EV) battery charging/ discharging parked in residential areas would result in critical issues in the power system in terms of energy availability. Thus, running intelligent charging/ discharging approaches can tackle these obstacles while managing green energy efficiently. This paper proposes a novel smart EV charging/ discharging strategy for multiple home microgrids (H-MG) based on user behavior analytics patterns with a non-cooperative game theory approach named Nikaido–Isoda/Relaxation algorithm (NIRA). In the proposed structure, EVs are active loads besides responsive loads in H-MGs and are involved in demand-side management (DSM) to achieve peak shaving. Furthermore, electricity retailers (ER) are encountered with distinct properties following opposite intentions and could participate in the competitive retail electricity market (REM) in residential areas. Therefore, households can maximize their profit by reducing varied costs, such as EV charging and battery degradation, while minimizing their carbon footprint. In addition, this study works on the distributed decision-making process that could provide optimal power-sharing and the best payoff in an unbiased procedure. After crucial investigations, the simulation outcomes demonstrate the effectiveness of the proposed approach in encouraging various households to participate actively in the REM with other agents to increase their profits. According to acquired results, active residents saved their budget 28%–45% through employing intelligent EV charging/ discharging strategies in the REM with dynamic pricing compared to the fixed tariff.

Revenue-optimized photovoltaic orientation in a northern competitive electricity market with carbon offsets

Solar photovoltaic (PV) farms are typically designed to maximize energy generation; however, their impacts on market prices can create conditions where revenues may be maximized by orienting the system to produce more energy at more valuable times of the day. PV generation offers its energy at a zero-dollar price in competitive electric markets, which tends to result in a drop in hourly price (“merit-order effect”) and reduction of revenues (“cannibalization effect"). We present an analysis of optimizing the orientation of PV systems for revenues, accounting for cannibalization in three pricing dynamics, with flat, medium, and steep merit-order curves. We employ a dataset of historical price-quantity offers from 2011 to 2019 from Alberta, Canada, a market that also has significant opportunities to generate carbon credits (CC). The market has an average maximum hourly load of ∼9 GW; we introduced 15, 400, 1000, and 3000 MW PV-capacity inputs, using tilt angles from 0° to 90° and azimuths from 90° to 270°. Our results show that for small PV capacities compared to the load, the market revenue-optimized orientations are close to the energy-optimized orientations, as would be expected. At higher levels of PV capacity, market revenue-optimized orientations deviate from the energy-optimized orientation as price cannibalization increases. The extent of this deviation depends on the slope of the historical merit order. When CC are introduced into the simulation, the total revenue-optimized orientation moves back towards the energy-optimized orientation as the CC overcome cannibalized market losses but exasperate overall market price depression.

Is shifting from Li-ion NMC to LFP in EVs beneficial for second-life storages in electricity markets?

While electric vehicles are promising to reduce carbon emissions on the road, from a holistic life-cycle view, further environmental considerations in the production and end-of-life management of their batteries are required. Recently, circular end-of-life thinking has been promoted with strategies to increase retired batteries' lifetime through second-life as lifetime extension is typically favoured in life cycle assessment. However, standardization of these strategies toward recycling or repurposing paths is recommended for different Li-ion chemistries. This categorization mainly concerns the cobalt-containing cathode Li-ion batteries i.e., NMC which is the dominant technology for transportation, and the alternative technology i.e., LFPs with a more recent attention toward them in automobile sector due the cobalt scarcity in the supply chain. This technology shift will impact their end-of-life management at the retirement. In this arrangement, the economic priority of repurposing such battery chemistries needs quantification. This study evaluated the financial return of repurposing retired Li-ion NMC and LFP batteries for energy arbitrage applications in power systems. The feasibility of repurposing is examined in the Irish and Queensland's markets. Results show that retired Li-ion LFPs respond to price fluctuations more frequently with a higher financial return compared to NMCs; thus, they have higher potential for repurposing as such their greater integration in new vehicles is promising from a circular economy perspective. Different rates of return have been observed for various sizes of systems and battery durations. The financial benefits are more prominent for a one-hour battery in a medium system compared to half and two-hour durations and a smaller system. A sensitivity analysis shows that even spending the same capital cost as a new system for a repurposed system results in a marginal financial return in a competitive electricity market like Queensland's, whereas further incentives toward circular-enabling business models from local authorities will effectively make such investments feasible.

State and federal nuclear support schemes in dynamic electricity market conditions: Insights from NYISO and PJM

Since 2017, several U.S. states have put in place out-of-market financial support schemes for nuclear power plants operating in deregulated electricity markets. In late 2021, the federal government announced the introduction of two new support schemes to secure the continued operation of nuclear power plants. This policy perspective evaluates the profitability of state subsidized nuclear power plants in the NYISO and PJM markets over a five-year period between 2017 and 2021. Results indicate that apart from 2019, nuclear power plants were financially robust, relying solely on market revenues without the need for state support schemes. More importantly, the recent upswing in competitive electricity market prices suggests that additional federal support schemes are not economically justified in current market conditions. I provide several suggestions to reconfigure the support schemes to reflect dynamic market conditions and ensure only vulnerable plants are granted out-of-market support.

A new framework for electricity price forecasting via multi-head self-attention and CNN-based techniques in the competitive electricity market

Due to recent technical improvements, the smart grid has become a feasible platform for electricity market participants to successfully regulate their bidding process based on demand-side management (DSM) perspectives. At this level, practical design, implementation, and assessment of numerous demand response mechanisms and robust short-term price forecasting development in day-ahead transactions are all critical. The accuracy and effectiveness of the day-ahead price forecasting process are crucial concerns in a deregulated market. In this market, the reason for low accuracy is the limitation of electricity generation compared to the electricity demand variations. Hence, this study proposes a suitable technique for forecasting electricity prices using a multi-head self-attention and Convolutional Neural networks (CNN) based approach. Further, this study develops a feature selection technique using mutual information (MI) and neural networks (NN) to choose suitable input variable subsets significantly affecting electricity price predictions simultaneously. The combination of MI and NN reduces the number of input features used in the model, thereby decreasing the computational complexity of the NN. The actual data sets from the Ontario electricity market in 2020 are acquired to verify the simulation results. Finally, the simulation results proved the efficiency of the proposed method by demonstrating increased accuracy by attaining the lowest average value for MAPE and RMSE with a value of 1.75% and 0.0085, respectively, and compared to results obtained by recent computational intelligence approaches. By attaining accurate electricity price results, the significance of this study can be summed up as aiding the electricity industry's operators in administering effective energy management, efficient resource allocation, and informed decision-making.

A tri-layer stochastic framework to manage electricity market within a smart community in the presence of energy storage systems

In recent years, with the restructuring of the electricity market, the main goal of all market participants is to maximize their profits. Among these, the stability of the system as well as the consumers' comfort index (CI) are two important points that should be considered in the scheduling. Hence, this paper presents a tri-layer model for managing a competitive electricity market in the presence of microgrids and demand response (DR) aggregators, in which microgrids determine their strategy for market participation by considering the CI of consumers. In the first layer of the proposed model, the DR aggregator buys part of the consumers load through shiftable and curtailable demand response programs (DRPs), and offers it for sale in the market. The second layer, microgrids do their scheduling with the aim of minimizing operating costs and maximizing the CI, and submit their offers/bids into the market. Finally, in the third layer, the market-clearing price (MCP) is determined by the market manager with the aim of maximizing social welfare. This model is formulated in mixed-integer linear programming (MILP) format and is solved through CPLEX solver in GAMS environment. The simulation results show that the two-objective modelling of the operation problem, despite a 12.18 % increase in total operating costs, increases the average CI by 5.57 %. The results also demonstrate that DR aggregator through the implementation of DRPs leads to a reduction in MCP during the peak period and thus a 5.01 % reduction in total operating costs.

ОПТИМИЗАЦИЯ РЕЖИМОВ РАБОТЫ ГЭС НА ОСНОВЕ ТЕОРИИ ПРЕДЕЛЬНОЙ ПОЛЕЗНОСТИ И ОЦЕНКИ СТОИМОСТИ ГИДРОРЕСУРСА

The purpose of this article is to solve the problem of optimizing the operation of a hydroelectric power station based on the theory of marginal utility and assessing the cost of a hydro resource, taking into account the characteristic modes of operation of a hydroelectric power station. In this work, a universal method, which combines an optimization method and a method for assessing marginal utility. The method developed by the authors involves use the adequate representation of the incremental rate characteristic and the determination of the cost of the hydro resource for the control of the operating modes of the HPP. Using the example of the Novosibirsk HPPs and TPPs, an assessment of the energy efficiency, proposing the concept of a developed methodology for determining the price of water for HPPs, will be obtained. Based on the results of the implementation of the developed approach at Novosibirsk HPPs, the electricity sales price competitive electricity market can be matched with the electricity sales price generated at TPP, which will be approximately 370 ¢/kWh

Construction of Mixed Derivatives Strategy for Wind Power Producers

Due to the inherent uncertainty of wind conditions as well as the price unpredictability in the competitive electricity market, wind power producers are exposed to the risk of concurrent fluctuations in both price and volume. Therefore, it is imperative to develop strategies to effectively stabilize their revenues, or cash flows, when trading wind power output in the electricity market. In light of this context, we present a novel endeavor to construct multivariate derivatives for mitigating the risk of fluctuating cash flows that are associated with trading wind power generation in electricity markets. Our approach involves leveraging nonparametric techniques to identify optimal payoff structures or compute the positions of derivatives with fine granularity, utilizing multiple underlying indexes including spot electricity price, area-wide wind power production index, and local wind conditions. These derivatives, referred to as mixed derivatives, offer advantages in terms of hedge effectiveness and contracting efficiency. Notably, we develop a methodology to enhance the hedge effects by modeling multivariate functions of wind speed and wind direction, incorporating periodicity constraints on wind direction via tensor product spline functions. By conducting an empirical analysis using data from Japan, we elucidate the extent to which the hedge effectiveness is improved by constructing mixed derivatives from various perspectives. Furthermore, we compare the hedge performance between high-granular (hourly) and low-granular (daily) formulations, revealing the advantages of utilizing a high-granular hedging approach.

Microgrids in Competitive Market Environment: An Indian Perspective

Microgrids are believed to be the befitting green solution to the ever-rising energy crisis in the developing nations. The resource rich nations are rapidly shifting their dependence on microgrids to meet the reduced carbon footprint target at a global level. This paper addresses this issue and presents a detailed technical review on microgrid integration in competitive electricity market scenario. An in-depth coverage of the need for and importance of microgrid deployments across India is presented in this work. The relevant literature that highlights the significance of various energy storage systems is reviewed. The authors present the perspective of microgrid integration in a competitive market scenario covering both day ahead and real time market scenarios thereby discussing the concept of maximizing social benefit in a grid integrated microgrid framework. This work also focuses on microgrid optimization techniques and outlines a detailed comparison showing merits and demerits of all the techniques implemented by the researchers so far. The authors highlight the fact that green energy holds the potential to not only outgrow the fossil fuels but it shall do so by fetching the requisite economic benefit to the investors as well.

Hybrid intelligence strategy for techno-economic reactive power dispatch approach to ensure system security

This work consolidates chaotic maps into a contemporary heuristic approach called Chimp Optimization Algorithm (COA). Augmentation of ten chaotic maps to COA forms the hybrid Chaotic COA (CCOA) which generates solutions in search space with enhanced mobility. A bi-level strategy of validating the robustness of the proposed hybrid method is adopted by first benchmarking it on eighteen assorted benchmark test functions and later by performing statistical analysis. The result obtained candidly establishes that merging of chaotic maps (dominantly Chaotic 4 Iterative type-CCOA4) enhances the performance of COA. The inferences congregated from the benchmark functions and statistical analysis facilitates the application of the recommended algorithm to obtain techno-economic solution in reactive power dispatch (RPD) problem which is manifested as a challenging issue in power system operation. The RPD problem is analyzed implementing the proposed approach with series-shunt compensation in a competitive electricity market for secured operation and the proposed Iterative type-CCOA4 reduces the total operating cost by 3.3450 × 103$ with respect to initial operating cost.

Uniform purchasing price approach for Vietnam wholesale electricity market: modeling and discussing

In Vietnam, the reform of the electricity sector is an urgent need, unbundled and striking to begin a Competitive Electricity Market. The Vietnam electricity day-ahead market model is implemented based on the gross pool model with specific market rules and network constraints. Today, the Vietnam electricity market is operating at the Vietnam Wholesale Electricity Market step but is not yet completed. Due to the long shape of Vietnam territory, an implicit cross-compensation mechanism is applied to create a balance between customers leading to an obstruction to allowing multi-buyers in the electricity market. Therefore, the main aim of this paper is based on the official documents to propose the current mathematical model of the Vietnam electricity day-ahead market using a Mixed Integer Linear Problem, called the Single Buyer Day-Ahead Market model. Then, the Multi Buyer Day-Ahead Market model is implemented to allow multi-buyers to trade in the market. The idea of purchasing uniform prices for multi-buyers using in Italy could be also introduced in the Integrated European Electricity Market in case of large price variations between countries as a mechanism to uniformize the price of energy in Europe for consumers. In this paper, purchasing uniform prices is proposed to provide an explicit cross-compensation mechanism for Vietnam. Eventually, the proposed models are evaluated with four scenarios in two years representing typical days in Vietnam. The results confirm that, on one hand, the proposed market-clearing models can be applied in a real application for any expansion of the market, i.e. the number of suppliers, demands, and bidding areas; on the other hand, the purchasing uniform prices provide an explicit cross-compensation between bidding areas helping to enhance the fairness and the transparency of the market.

Competitiveness of Slovak power plants in connection with global changes in the development of electric energy prices

A cost framework we applied to provide an overview of bioenergy issues related to the process with renewable fuels and focuses on several possible profitability scenarios and the future development of Slovak power plants in a competitive electricity market. The aim of the paper is to propose alternative scenarios and forecast the development of Slovak power plants. The economic benefits of transitioning to alternative biofuel were analysed within the research in cooperation with the Vojany Black Coal Power Plant (EVO). To confirm the most economically efficient types of fuels used in the combustion process was used Data Envelopment analysis (DEA). Within the methodology used were also identified the factors influencing the price of electric energy (EE) on the market and subsequently the profitability of power plants. Scenarios for the future development of EE prices were identified using the statistical method FORECAST.FUNCTION. Despite the predicted increase in the market price of EE we conclude that the use of co-combustion fuels is socially beneficial compared to the use of biofuels under a wide range of assumptions regarding technical, economic and environmental characteristics. The relationship and extent of influence between coal, emission allowance and oil prices, determining EE prices were quantified by regression analysis and Analysis of Variances (ANOVA). The paper contains current data from available databases and market forecasts and the absence of analyses for economic development, sustainability, and competitiveness of Slovak power plants in the unfavourable period of the ongoing COVID-19 pandemic and war conflict Russia-Ukraine.