Competitive Electricity Market Research Articles

Chaotic Sea-Horse Optimizer for Profit-Based Unit Commitment to Maximize the Profit of GENCOs in Restructured Power System Considering Reserve Allocation

Objectives: The primary objective of this research work is to solve the Profit Based Unit Commitment (PBUC) problem in Power Generation Companies (GENCOs) in the competitive Electricity market. The energy and spinning reserve allocation are considered to maximize the profit of power generation companies. Methods: A new and recently developed optimization technique of Chaotic Sea-Horse Optimizer (CSHO) is planned for an optimal solution PBUC problem. The control parameters of CSHO are the number of Population is 50, the Chaotic map parameter is 4, the dimensions are 10, and the maximum number of iterations is 100 are considered to achieve the global optimal solutions. The competitive study with standard golden approaches of the LR method and other soft computing techniques such as GA, PSO, etc. are considered for performance evaluations of CSHO. The proposed CSHO is mainly based on the natural behavior of Sea-Horse in the ocean. The MATLAB 15.0 platform is utilized for the expected problem. Findings: The CSHO was tested on a standard IEEE-39 bus system (10 generators and 24 hours) with reserve allocation. The CSHO technique improves the total profit of GENCOs with various test loads and reserve demand. The simulation results like Optimal UC schedule, power generation of thermal plants, fuel cost, startup cost, revenue, and profit are displayed. The percentage of total profit of 29.76% is improved when compared with the mathematical approach of the LR method with 102 second time period. Novelty: The CSHO has currently developed an effective algorithm that easily reaches the global optimal solutions. The CSHO mimics the movement, hunting, and breeding behavior of sea-horses in nature. Implementation of chaotic maps helps to improve the convergence speed of the proposed method. Keywords: Deregulation, Profit Based Unit Commitment, Energy and Reserve Allocation, Cost Minimization, Profit of GENCOs, Chaotic Sea-Horse Optimizer

A profit driven optimal scheduling of virtual power plants for peak load demand in competitive electricity markets with machine learning based forecasted generations

The operation of generating resources connected to VPPs during peak demand is crucial for determining the economic sustainability of participants in a competitive electricity market and minimizing the need for expensive peaking power plants. This paper proposes a profit driven strategy for scheduling of resources associated with VPP during peak load demand in competitive electricity market followed by different machine learning approaches for predicting the pattern of generation in DAM. The MILP has been developed by using GAMS integrated with MATLAB. The Intra-VPPs consisting TGs and DERs have been designed by using IEEE 33 bus distribution system. The performance of this system aligns with cases which investigate the interplay of EM dynamics, DERs contribution and scheduling of TGs, in order to optimize the operation of VPPs and assured the profits along with generating power amount for given time and load demand. Additionally, the generation pattern for future load in DAM operations has been forecasted using different neural network fitting models for enhancing the system's efficiency. The proposed work reduced the operating cost, nodal prices of the system followed by considerable improved profit for designed VPPs, in which more than 50 % profit driven by DERs itself of the whole system.

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.

The effect of PV generation's hourly variations on Israel's solar investment

Limited by sunniness and conversion efficiency, one installed MW of photovoltaic (PV) capacity can only produce ωt MWh of electricity in daytime hour t, where ωt = positive fraction that we call PV's hourly effective capacity (HEC). Based on a sample of commercially operating PV plants in Israel, HEC rises throughout the 08:00–11:00 period, peaks in the 11:00–14:00 period and then declines in the 14:00–18:00 period. Further, HEC values in spring and summer exceed those in autumn and winter, thus motivating our research question: do HEC variations by hour and season adversely affect the plans for and assessment of PV investment in Israel? An affirmative answer would cause concerns regarding Israel's announced policy of relying on increasing market penetration of PV generation transitionally supported by merchant natural-gas-fired generation (NG) to achieve a low-carbon electricity future. Hence, we propose a two-stage model of Israel's forthcoming Cournot wholesale electricity market with independent power producers (IPPs) operating PV and NG power plants. By comparing the model's solutions under alternative HEC scenarios, we find that the level of detail in modelling the HEC variations by hour and season does not materially affect the optimal PV and NG investments, thus lending support to the use of relatively simple models with only two daily non-seasonal HEC values to assess Israel's announced policy of deep decarbonization. Our methodology is general and real-world relevant because it can be readily adapted for applications in other regions with ample sunshine and wholesale electricity market competition (e.g., California, Texas and Australia).

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.

Optimal Placement and Sizing of DG for Loss Reduction,Voltage Profile Improvement and Voltage Sag Mitigation

Increasing trend in using distributed generation has motivated power sector planners to consider approaches that result in benefiting the most from investments in this field. Meanwhile subjects such as power loss reduction, voltage profile improvement and voltage sag mitigation has significant role in lessoning imposed expenditures to utility companies. Loss reduction is important for utility companies as it directly increases company benefit in a competitive electricity market, beside reaching power quality standards is too important as it has vital effect on customer orientation. In this paper an optimization algorithm is developed to gain these goals all together. In order to evaluate sag mitigation capability of the proposed algorithm, voltage in voltage sensitive buses is investigated. An existing 20KV network has been chosen as test network. Results show the efficiency of the proposed algorithm in reducing power loss, improving voltage profile and mitigating voltage sags in voltage sensitive buses.

Unit commitment and generation dispatch of a hydropower plant in a competitive electricity market

This paper presents a model to solve the short-term scheduling problem of a hydropower plant in a deregulated system, based on dynamic programming techniques. The objective of this model is to maximize the revenue obtained by selling energy in a competitive electricity market. The time horizon of the model is divided into hourly periods and ranges from one day to one week. The proposed model determines both the unit commitment and the power to be generated in each hour of the time horizon. The power is considered as a nonlinear function of the water discharge and the reservoir volume; the dependence of the units and plant operating limits on the available gross head has been taken into account; and the water discharged through the spillway has been also allowed for in the model. This approach has been applied to a practical case study, the results of which are analyzed in detail.

Provision And Costs of Ancillary Services in a Restructured Electricity Marquet.

Power industry has been experiencing dramatic changes from a vertically integrated industry into a deregulated competitive electricity market over the last decade. In a restructured electricity industry, markets govern the operation and expansion of electricity generation, and power plants make profits from markets for energy and ancillary services. In the last years there has been a worldwide trend toward restructuring the electricity supply industry and consequently energy markets were created. Ancillary services are defined as all those activities on the interconnected grid that are necessary to support the power transmission and withstand the security operation as well as the power quality level. This paper presents the most important ancillary services provided by the independent system operator in a restructured power system. It will be also described the Simultaneous Optimisation algorithm to performer the active power reserves dispatch in a competitive environment.

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.

Research on Power Investment and Expansion Planning Considering Coal-fired Power Capacity and Electricity Price in Market-Driven Environment

The market-oriented environment and the shift away from the coal-fired power are inevitable trends in the future development of electric power energy. Based on this, this article establishes a dual-level model for power investment expansion planning with wind power system in an oligopoly competitive electricity market. In the upper-level model, power generation companies make investment decisions and formulate their bidding strategies in market transactions, while the lower-level model simulates the day-ahead market clearing process under the centralized bidding and trading mode. To encourage the transition from coal-fired power plants, a model for expected annual operating revenue of coal-fired power companies under a dual pricing system is proposed, and the conditional value-at-risk (CVaR) is used to measure the investment risk brought by the uncertainty of competitors in the investment planning game for power generation companies. The numerical results verify the effectiveness of the proposed model and method.

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.