Generation Companies Research Articles

Evaluating The Impact of ESG Integration on The Financial Performance of Pertamina Geothermal Energy: A Sustainable Growth Perspective

The global energy landscape is undergoing a significant transformation driven by the urgent need to mitigate climate change, increased regulatory pressures, and rising stakeholder expectations. This study aims to explore the impact of ESG integration on the financial performance of Pertamina Geothermal Energy (PGE), a subsidiary of PT Pertamina (Persero), which plays an important role in the energy transition in Indonesia. This study uses secondary data from financial statements, prospectuses, annual reports, and information from the PGE website. In addition, historical data on the stock prices of power generation companies and comparative analysis of the industry are used. The analysis was carried out using the Discounted Cash Flow (DCF) method to calculate the intrinsic value of the company, as well as an assessment of sustainability policies, community engagement programs, and improvement of corporate governance. The results show that ESG integration has a positive impact on PGE's sales growth, profitability, and return on investment. Improved sustainability policies, community engagement programs, and corporate governance contribute significantly to better financial performance. The integration of ESG principles in PGE not only improves financial performance but also shows that sustainability and profitability can go hand in hand. These findings provide valuable insights for the broader energy sector, suggesting that ESG integration can drive positive change and facilitate an era where financial growth and environmental responsibility complement each other.

Research on the Coordinated Trading Mechanism of Demand-Side Resources and Shared Energy Storage Based on a System Optimization Model

With the development of the economy and society, the importance of a secure and stable electricity supply continues to increase. However, the power grid is facing the test of excess installed capacity, the waste of renewable energy, and a low comprehensive utilization rate. This problem stems from the inconsistent peak–valley differences between power production and consumption, and the lack of clear electricity price signals, which disrupts the safe and stable operation of the power market. This paper combines the interactive transactions among clean energy power generation companies, users, and energy storage, explores how the system optimization model can be reflected in the power market through regulatory measures, and formulates the optimal output scheme of the system under the constraints of clean energy power generation forecast data, user base load forecast data, demand-side resource regulation ability, and energy storage system regulation ability to achieve the goals of comprehensive clean energy power consumption and minimum cost for users. A comprehensive analysis of the proposed model was conducted using actual data from a certain province in China, the results show that the consumption of clean energy will increase by 3% to full consumption and the total cost of users will be 32% lower than that of time-of-use (TOU) power prices, which proves the potential of the proposed joint optimization model in absorbing clean energy and the effectiveness of the market mechanism.

Identification of oil contamination in process water using fluorescence excitation emission matrix (FEEM) and parallel factor analysis (PARAFAC).

Fuel oil is widely used within Eskom, a power generation company in South Africa. Eskom's coal-fired power stations use up to 30,000 L of fuel oil per hour during a cold start-up, a consequence of which results in oil leaks to the dams. Oil contamination in water treatment plants causes irreversible membrane fouling, requiring costly replacement. This research work focused on the development of a rapid method for the identification of low concentrations of the water-soluble oil component fraction of crude fuel oil. For the developed method, known volumes of the water-soluble fraction of crude oil were spiked into various matrices of process water. FEEMs were collected using the patented HORIBA Aqualog spectrometer and data were modelled with PARAFAC. The results were well described with a four-component model, which included an oil component and three natural organic matter components, with a split-half validation match of 90%. The oil component was verified using linear regression of the PARAFAC component scores yielding an R2 value of 0.98. From the scores, a qualitative pass/fail test was developed such that process water can be analysed and subjected to the model to indicate the presence of oil contamination beyond a damaging threshold.

Optimal day-ahead offering strategy for large producers based on market price response learning

In day-ahead electricity markets based on uniform marginal pricing, small variations in the offering and bidding curves may substantially modify the resulting market outcomes. In this work, we deal with the problem of finding the optimal offering curve for a risk-averse profit-maximizing generating company (GENCO) in a data-driven context. In particular, a large GENCO’s market share may imply that its offering strategy can alter the marginal price formation, which can be used to increase profit. We tackle this problem from a novel perspective. First, we propose an optimization-based methodology to summarize each GENCO’s step-wise supply curves into a subset of representative price-energy blocks. Then, the relationship between the resulting market price and the energy block offering prices is modeled through a probabilistic forecasting tool: a Distributional Neural Network, which also allows us to generate stochastic scenarios for the sensibility of the market towards the GENCO strategy via a set of linear constraints. Finally, this predictive model is embedded in the stochastic optimization model employing a constraint learning approach. Results show how allowing the GENCO to deviate from its true marginal costs renders significant changes in its profits and the marginal price of the market. Additionally, these results have also been tested in an out-of-sample validation setting, showing how this optimal offering strategy can effective in a real-world market context.

Different Demand Response Programs with its Implementation in Various Countries and the Role of TOU DR in the Context of Nepal

With the increasing trend of electricity consumption worldwide, it creates a cumbersome, reliable power system and necessitates the construction of new generator companies, transmission lines, and other infrastructures. Despite investing in new infrastructures to manage occasional peaks, utilities can use the demand response concept to effectively handle demand during such peak time intervals, and in doing so, it enhance the grid stability. This assists in balancing the supply and demand for electricity, making the grid more resilient to fluctuations, and reducing the risk of blackouts. This paper delves into the diverse DR programs across different countries and comparative analysis for finding the suitable DR method to make a more reliable electric power network in Nepal.

Optimal bidding strategy for generation companies alliance in electricity‐carbon‐green certificate markets

AbstractTo promote the profit of generation companies and reduce carbon emissions from the generation sector, a two‐layer decision‐making model based on the cooperative game is proposed. Based on the analysis of a renewable‐fossil energy generation alliance to participate in electricity‐carbon‐green certificate markets, a robust optimization model considering the price uncertainty in the electricity spot market is first established based on the information gap decision‐making theory as the upper part of a two‐layer optimization model. Subsequently, the clearing models of the electricity spot, carbon emission trading, and green certificate markets are established. This two‐layer optimization model is transformed into a single‐layer model based on the well‐established KKT conditions. A profit allocation model for the members of the generation alliance is then presented based on the Shapley value and an improved nucleolus kernel method. Finally, the effectiveness of the proposed model is demonstrated by the IEEE 14‐bus power system.

The Optuna–LightGBM–XGBoost Model: A Novel Approach for Estimating Carbon Emissions Based on the Electricity–Carbon Nexus

With the challenge posed by global warming, accurately estimating and managing carbon emissions becomes a key step for businesses, especially power generation companies, to reduce their environmental impact. Optuna–LightGBM–XGBoost, a novel power and carbon emission relationship model that aims to improve the efficiency of carbon emission monitoring and estimation for power generation companies, is proposed in this paper. Deeply exploring the intrinsic link between power production data and carbon emissions, this model paves a new path for “measuring carbon through electricity”, in contrast to the emission factor method commonly used in China. Unit data from power generation companies are processed into structured tabular data, and a parallel processing framework is constructed with LightGBM and XGBoost, and optimized with the Optuna algorithm. The multilayer perceptron (MLP) is used to fuse features to enhance prediction accuracy by capturing characters that the individual models cannot detect. Simulation results show that Optuna–LightGBM–XGBoost can achieve better performance compared to existing methods. The mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), and coefficient of determination (R2) of the model are 0.652, 0.939, 0.136, and 0.994, respectively. This not only helps governments and enterprises to develop more scientific and reasonable emission reduction strategies and policies, but also lays a solid foundation for achieving global carbon neutrality goals.

A new wide input voltage DC-DC converter for solar PV systems with hybrid MPPT controller

The present working conventional power generation systems utilization is reducing day by day because of their demerits are more functioning cost, high carbon dioxide emission, more complexity in handling, and required high installation area. So, the current power generation company focuses on Renewable Energy Sources (RES) which are wind, tidal, and solar. Here, the solar power network is utilized for supplying electricity to the electrical vehicle battery charging system. The Solar photovoltaic (PV) modules supply nonlinear power which is not useful for automotive systems. To maximize the supply power of the solar PV system, an Adaptive Step Genetic Algorithm Optimized (ASGAO) Radial Basis Functional Network (RBFN) is utilized for tracking the working point of the solar PV module thereby enhancing the operating efficiency of the overall system. The features of this proposed hybrid Maximum Power Point Tracking (MPPT) controller are quick system dynamic response, easy operation, quick convergence speed, more robustness, and high operating efficiency when equalized with the basic MPPT controllers. The major issue of solar PV modules is low supply voltage which is increased by introducing the wide input voltage DC-DC converter. The merits of this introduced converter are low-level voltage stress on diodes, good quality supply power, high voltage gain, plus low implementation cost. Here, the introduced converter along with the AGAO-RBFN controller is analyzed by selecting the MATLAB/Simulink environment. Also, the proposed converter is tested with the help of a programable DC source.

Evaluation of the financial performance of electricity generation companies using the Grey relational analysis method

In the study, 10 electricity generation companies on the Fortune 500 list were examined using nine criteria. These companies are listed in the top 40 in terms of installed power. The aim of the study was to determine the financial performances of the companies with the entropy-weighted Grey Relationship Analysis (GRA) method by obtaining the 2021 data of the criteria from the websites of the relevant companies. As a result of the weighting made by the entropy method, it was determined that the most important criterion was the export income. These criteria were determined as equity, total assets, net sales, asset turnover, equity turnover, profit before interest/tax , change in profit before interest/tax, and as change in net sales respectively. After the weights of the criteria were calculated with the entropy method, the GRA method was used to rank the alternatives. Çelikler company was determined to be the best electricity generation company with the highest degree of relationship. This company was followed by Odaş, Gama, İçdaş, Limak, Akenerji, Zorlu, Enerjisa, Aksa and Enka respectively.

Integrating an Ensemble Reward System into an Off-Policy Reinforcement Learning Algorithm for the Economic Dispatch of Small Modular Reactor-Based Energy Systems

Nuclear Integrated Energy Systems (NIES) have emerged as a comprehensive solution for navigating the changing energy landscape. They combine nuclear power plants with renewable energy sources, storage systems, and smart grid technologies to optimize energy production, distribution, and consumption across sectors, improving efficiency, reliability, and sustainability while addressing challenges associated with variability. The integration of Small Modular Reactors (SMRs) in NIES offers significant benefits over traditional nuclear facilities, although transferring involves overcoming legal and operational barriers, particularly in economic dispatch. This study proposes a novel off-policy Reinforcement Learning (RL) approach with an ensemble reward system to optimize economic dispatch for nuclear-powered generation companies equipped with an SMR, demonstrating superior accuracy and efficiency when compared to conventional methods and emphasizing RL’s potential to improve NIES profitability and sustainability. Finally, the research attempts to demonstrate the viability of implementing the proposed integrated RL approach in spot energy markets to maximize profits for nuclear-driven generation companies, establishing NIES’ profitability over competitors that rely on fossil fuel-based generation units to meet baseload requirements.

Research on Dynamic Reactive Power Cost Optimization in Power Systems with DFIG Wind Farms

As the power market system gradually perfects, the increasingly fierce competition not only drives industry development but also brings new challenges. Reactive power optimization is crucial for maintaining stable power grid operation and improving energy efficiency. However, the implementation of plant–grid separation policies has kept optimization costs high, affecting the profit distribution between power generation companies and grid companies. Therefore, researching how to effectively reduce reactive power optimization costs, both technically and strategically, is not only vital for the economic operation of the power system but also key to balancing interests among all parties and promoting the healthy development of the power market. Initially, the study analyzes and compares the characteristic curves of synchronous generators and DFIGs, establishes a reactive power pricing model for generators, and considering the randomness and volatility of wind energy, establishes a DFIG reactive power pricing model. The objective functions aimed to minimize the cost of reactive power purchased by generators, the price of active power network losses, the total deviation of node voltages, and the depreciation costs of discrete variable actions, thereby establishing a dynamic reactive power optimization model for power systems including doubly-fed wind farms. By introducing Logistic chaotic mapping, the CSA is improved by using the highly stochastic characteristics of chaotic systems, which is known as the Chaotic Cuckooing Algorithm. Meanwhile, the basic cuckoo search algorithm was improved in terms of adaptive adjustment strategies and global convergence guidance strategies, resulting in an enhanced cuckoo search algorithm to solve the established dynamic reactive power optimization model, improving global search capability and convergence speed. Finally, using the IEEE 30-bus system as an example and applying the improved chaotic cuckoo search algorithm for solution, simulation results show that the proposed reactive power optimization model and method can reduce reactive power costs and the number of discrete device actions, demonstrating effectiveness and adaptability. When the improved chaotic cuckoo algorithm is applied to optimize the objective function, the optimization result is better than 7.26% compared to the standard cuckoo search algorithm, and it is also improved compared to both the PSO algorithm and the GWO algorithm.

Evolutionary game analysis of violation regulation in the electricity market based on blockchain technology

Electricity market violations affect the overall operations of the electricity market. This paper explores the evolutionary stability strategies of electricity generation enterprises and electricity consumers under two modes: traditional regulation and blockchain regulation to analyze blockchain technology’s mechanism and conditions in solving electricity market violations. The experimental results indicate that the likelihood of consumers accepting electricity and the regulatory capacity of regulatory agencies play a crucial role in determining the violation approach adopted by electricity generation enterprises. Under traditional regulatory models, due to information asymmetry, regulatory agencies may not be able to detect violations promptly. Meanwhile, electricity consumers may choose to accept violations by power generation companies due to high appeal costs. Blockchain technology enables regulatory agencies to improve their regulatory capabilities by eliminating information asymmetry, reducing the cost of complaints from electricity consumers, thereby elevating the risk for enterprises engaging in market violations and optimizing the evolutionary game towards an optimum state.

Evaluating the Impact of Transitioning Maintenance Strategy from Reactive to Proactive in Power Generation Companies: An Empirical Analysis

This empirical study rigorously investigates the impact of transitioning from a reactive maintenance strategy to a proactive approach within the context of power generation companies. The central aim is to quantify and provide a comparative analysis of the efficiency, cost implications, and overall operational impact of adopting proactive versus reactive maintenance strategies in a power plant setting. Drawing on meticulously collected data, the research considers an array of key performance indicators, including maintenance costs, equipment breakdowns, downtime duration, total power output, equipment lifespan, safety incidents, regulatory compliance violations, and investment in staff training and predictive maintenance tools. The findings of this study are both revealing and quantitatively substantial. A transition to a proactive maintenance strategy has demonstrated a reduction in maintenance costs by approximately 20%, coupled with a 35% decrease in the number of equipment breakdowns. Downtime duration was significantly reduced by 40%, enhancing operational efficiency and power output. Notably, the total power output increased by 15%, and the equipment lifespan was extended by an average of 25%. Furthermore, a marked decrease of 50% in safety incidents was observed, reflecting the profound impact of proactive strategies on enhancing safety protocols. However, these improvements are juxtaposed with an initial investment surge, where staff training costs increased by 30%, and expenditure on predictive maintenance tools rose by 25%. This research underscores the critical importance of a comprehensive and quantified understanding of maintenance strategies and their broader impacts on power plant performance. The study illustrates that while proactive maintenance demands initial investments, the long-term benefits significantly outweigh these costs, leading to enhanced operational efficiency, safety, and cost-effectiveness. The insights gleaned from this study provide invaluable guidance for power plant operators, stakeholders, and policymakers in their pursuit to optimize operations, improve safety standards, and achieve economic efficiencies, thereby advocating for a strategic shift towards more proactive maintenance approaches in power plant operations.

Adaptation of Employee Development with Artificial Intelligence Virual Reality in a Power Generation Company

Human resource development and training are essential to improve the quality and skill level of employees. The field of artificial intelligence focuses on developing the ability of computers to accomplish tasks that can currently be completed faster than humans can. To meet higher standards, Power Generation Companies are improving the quality of Virtual Reality (VR) images. VR can be used as a training medium. In addition, it can improve student understanding, information retention, and skills, and provide an immersive and deep learning experience. The purpose of this study is to determine the process of adaptation or application of AI VR in human resource development in the workplace, the contribution of development, and its utilization for work productivity, especially in the power plant company PT PLN Indonesia Power Suralaya banten province.. The method used is descriptive qualitative with a phenomenological approach to the adaptation of AI VR application by explaining the utilization, including the use of design, and AI VR procedures so that it can be adapted in the application of HR development effectively. Reputable national and international journals are used as references for the foundation of the development of this article. The adaptation results in different training modules for employees based on individual skills, job levels, job titles, and desired competencies. The AI tool can then match new projects with employees who have completed training together

Economic enhancement and grid‐frequency control of a wind farm‐CAES‐fuel cell‐based hybrid system

AbstractIn the present era, the electrical network faces difficulty owing to the restricted supply of conventional energy resources. Solar and wind power are supremely acceptable renewable energy around the globe due to their cheap availability, zero fuel costs, and environmental safety features. Maintaining power balance in a renewable combined day‐ahead power network becomes challenging owing to the uncertainty of renewable sources, resulting in economic instability. An energy‐storing device is essential for combining renewable sources with existing thermal power plants to control the energy supply and maintain system stability. Any government agency in the deregulated market does not set up energy pricing. The ISO (independent system operator) is the primary organization in the day‐ahead power network that is responsible for collecting energy bids from DISCOs (distribution companies), TRANSCOs (transmission companies), and GENCOs (generation companies). The energy pricing is set by the market regulator at a rate known as Location‐based marginal pricing (LBMP), which provides benefits to GENCOs, and DISCOs as well as the customers. In a deregulated market, the unpredictability of renewable sources affects system profit owing to the creation of instability cost (ISC), induced by mismatching the bidding power production from the renewable plant. To lessen these concerns, the presented approach has proposed an innovative model that employs ideal scheduling of the WF (wind‐farm)‐CAES (compressed‐air‐energy‐storage)‐fuel cell (FC) hybrid systems to enhance system economic profitability while keeping the grid frequency (GF) in the safe zone. The fuel cell acts as the energy‐storing device that can be utilized during high‐demand times on the grid, with the ultimate goal of maximizing profits for the overall system. The CAES system's energy level is separated into four dissimilar points, and this study presents an optimum approach for efficiently using it to maintain grid frequency. The new strategy outperformed the previous methods in all categories. The study was performed using the IEEE 30 bus test system.

How feed-in-tariff subsidies affect renewable energy investments in China? New evidence from firm-level data

The feed-in-tariff (FiT) policy is a commonly applied policy instrument aimed at fostering renewable energy (RE) investments, yet its actual incentive effect remains controversial, and there is a dearth of micro-level evidence. This study utilizes data from 39 Chinese RE generation companies from 2014 to 2021 to investigate the effect of FiT subsidies on RE investments at the corporate level. Our outcomes are as follows: (1) FiT subsidies exert a significantly positive influence on the investments of RE firms, wherein cash flow, corporate profitability and financing constraints serve as intermediating factors. (2) The economic policy uncertainty (EPU) tends to diminish the positive impact of FiT subsidies on RE investments. Conversely, environmental regulation (ER) levels can enhance the FiT subsidies' positive effect. (3) The stimulating effect of FiT subsidies on investments in large state-owned RE firms exhibits a greater magnitude. Based on this, we put forward targeted policy recommendations.

Wind power deviation charge reduction using long short term memory network

High penetration of variable generation like wind in modern power systems results in frequent load-generation imbalances. Additional spinning reserves and balancing services are deployed to reduce or avoid such imbalances. However, these additional balancing services result in increased total operation costs and it is generally shifted to Wind Power Generation Companies (WPGC) as Deviation Charges (DCs). The high DCs diminish the profitability of WPGCs. DCs are directly proportional to forecasting errors and can be reduced by enhancing forecasting accuracy. This paper proposes a Long Short-Term Memory (LSTM) network to reduce DCs. LSTM is a special kind of recurrent neural network and it is capable of learning long-term and short-term dependencies effectively. Therefore, the LSTM network can produce minimal error wind power forecasts and this helps to reduce DCs. The performance of the proposed LSTM network is realized using the real system data. Results obtained from the proposed LSTM model are compared with various forecasting models like ARIMA, ANN, and SVR. The comparison shows that the proposed LSTM model shows the least total DCs on most of the days followed by ANN, SVR, and ARIMA. The proposed LSTM, ANN, SVR, and ARIMA models show an average total DC of 6194.25$, 8459.32$, 15822.22$, and 38269.89$ respectively. Thus, the proposed LSTM model is capable of reducing 517.82%, 155.43%, and 36.56% DCs from ARIMA, SVR, and ANN models respectively, and helps the WPGCs to enhance their profitability.

Optimal bidding strategy for integrated CHP system by modified heuristic algorithm considering CO2 emission in energy and reserve markets

PurposeThis study aims to solve optimal pricing and power bidding strategy problem for integrated combined heat and power (CHP) system by using a modified heuristic optimization algorithm.Design/methodology/approachIn electricity markets, generation companies compete according to their bidding parameters; therefore, optimal pricing and bidding strategy are solved. Recently, CHP units are significantly operated by generation companies to meet power and heat, simultaneously.FindingsFor validation, it is shown that profit is improved by 0.04%–48.02% for single and 0.02%–31.30% for double-sided auctions. As heat price curve is extracted, the simulation results show that when CHP system is integrated with other units results in profit increase and emission decrease by 3.04%–3.18% and 2.23%–4.13%, respectively. Also, CHP units significantly affect bidding parameters.Originality/valueThe novelties are pricing and bidding strategy of integrated CHP system is solved; local heat selling is considered in pricing and bidding strategy problem and heat price curve is extracted; the effects of CHP utilization on bidding parameters are investigated; a modified heuristic and deterministic optimization algorithm is presented.

A NEW SOLUTION FOR MAINTENANCE SCHEDULING OF DISTRIBUTED GENERATIONS BASED ON MONTE-CARLO SIMULATION AND GAME THEORY

This paper presents a new solution for maintenance scheduling of Distributed Generation (DG) in deregulated power markets by applying an independent market for DG maintenance scheduling based on game theory. Maintenance scheduling is one of the important issues in restructured power electricity markets, because each distribution generation company desires to maximize its own payoffs and independent system operator (ISO) has its own reliability concerns that mostly, these two points of view are against with each other. The paper presents a dynamic Distribution Resources Maintenance Market (DRMM) based on dynamic game theory for resolving maintenance scheduling problem of distributed generation resources in deregulated environment. In this market, distribution generation companies are set their strategies to participate in DRMM by considering load, primary resources and seasonal forced outage rate uncertainties and behaviour of other DG companies that participate in market. On the other hand, ISO manages the maintenance market based on reliability and offering incentives /penalties for DG companies. This paper studied the ISO acts by using Monte-Carlo simulation for reliability indices assessment relying on load and resources uncertainties and interactions between DG companies. Numerical results are determined by using an applicable case study for testing accuracy and applicability of the new proposed solution.

Mutual conversion mechanisms for environmental interest products to jointly enhance synergistic effect between power, CET and TGC markets in China

The construction of mutual conversion mechanisms for environmental interest products (EIP) is crucial to enhance connectivity and promote the synergistic development among multiple market systems including power, carbon emissions trading (CET) and tradable green certificate (TGC). In this paper, static and dynamic TGC‑carbon allowance (CA)-Chinese certified emission reduction (CCER) conversion mechanisms are designed from the perspective of physical and economic values. Based on the multi-agent dynamic game model considering Weber-Fechner's law, the transaction behavior for various generation companies (GENCOs) and power purchasers in power, CET and TGC markets under different conversion mechanisms are analyzed. The results demonstrate that TGC-CA-CCER conversion mechanisms further expand the transaction space for low-carbon GENCOs, and effectively reflect the differentiated value of each EIP in diverse scenarios and time periods. Compared with other conversion mechanisms, the dynamic TGC-CA-CCER conversion mechanism based on physical values has better economic as well as environmental benefits, and enhances the competitiveness and liquidity of markets, which is the optimal solution to realize the mutual conversion of EIP. These insights can make up for the research gap of mutual conversion mechanisms on different EIP and provide mechanism options for realizing favorable interaction among power, CET and TGC markets.