Generation Companies Research Articles

Current Scenario of the Changing Role of Human Resource in a VUCA Environment: A Case Study of UPRVUNL

The modern business environment is increasingly characterized by Volatility, Uncertainty, Complexity, and Ambiguity (VUCA). In today's VUCA environment, organizations face unprecedented challenges and it is undergoing significant transformations of the role of Human Resources (HR). This paper explores how Human Resources (HR) departments adapt to these conditions, and examines the evolving role of HR in the context of VUCA with a focus on Uttar Pradesh Rajya Vidyut Utpadan Nigam Limited (UPRVUNL), a major power generation company in energy sector in India. Through a detailed case study approach, This research aims to explore the current challenges and strategies adopted by UPRVUNL's HR department in response to VUCA conditions and to understand the evolving role of HR in this context, identifying challenges and strategies that enhance organizational resilience and effectiveness.

Adaptive robust self‐scheduling for a wind‐based GenCo equipped with power‐to‐gas system and gas turbine to participate in electricity and natural gas markets

AbstractIntegrating renewable energy, particularly wind generation, into power systems brings significant uncertainty and intermittency, challenging generation companies (GenCos) to maintain economic operations. This paper proposes a strategy for a wind‐based GenCo equipped with a gas turbine and a power‐to‐gas (PtG) system to balance wind variability. The gas turbine offsets power shortfalls by consuming natural gas, while the PtG system absorbs excess wind energy, converting it to natural gas and injecting it into the natural gas network as a form of storage. The GenCo operates in day‐ahead electricity and natural gas markets and the real‐time electricity market, addressing uncertainties from wind output, energy prices, and natural gas access. A tri‐level adaptive robust optimization model is introduced for the GenCo’s short‐term operational scheduling. At the first level, decisions related to the GenCo's participation in the day‐ahead electricity and natural gas markets are made. The second level deals with determining the worst‐case realization of the uncertainties, constrained by the budget of uncertainty and the limited range of variations of uncertain variables. The third level sets the operational strategy on the actual day, considering power and gas balance, as well as constraints for the wind farm, gas turbine, and PtG unit. A column & constraint generation (C&CG) algorithm is used to solve the model. Numerical results demonstrate the model’s effectiveness in various case studies, showing that the GenCo can manage wind uncertainties, benefit from arbitrage opportunities in electricity and gas markets, and improve economic outcomes. This approach supports the broader integration of renewable energy into power systems.

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

Strategic behaviors of renewable energy generation companies participating in the electricity and carbon coupled markets based on non-cooperative game theory

Renewable energy (RE) can participate in electricity and carbon market trading in China, which can help stimulate the investment of RE. Thus, we propose a bi-level model for non-cooperative game interactions among power generation companies (GenCos), considering the electricity and carbon coupled markets. The upper-level model considers strategic behaviors driven by maximizing profits for GenCos. The lower-level model includes a two-stage electricity market: the day-ahead market and the real-time market; it implements joint clearing of energy and reserves, and continuous trading with competitive auctions in the carbon market based on the clearing results of the electricity market. Simulations are performed based on the actual data of the power system in a certain region of Guangdong Province in China. The results show that electricity and carbon coupled markets can effectively reduce market costs and carbon emissions, improve the society welfare and RE benefits. The uncertain increase in RE output and higher installed capacity reduce the total revenue of the RE in the coupled markets. The initial carbon quota decomposition optimization decision of the coal-fired GenCos and gradual reduction in the initial carbon quota will increase the revenue of the RE GenCos.

A study of electricity sales offer strategies applicable to the participation of multi-energy generators in short- and medium-term markets

Due to the increasing proportion of renewable energy, a multi-layered and multi-timescale energy market has emerged in many countries such as China. In the meanwhile, power generation companies must develop more intelligent and dynamic offer strategies to adapt to today's intricate energy trading. Because of the difficulty in describing the dynamic trading environment caused by the uncertainty of renewable energy, previous studies have not fully explored the offer strategy especially in both short-term and medium-term electricity markets. In response to this challenge, this research introduces a novel biding strategy framework leveraging a Asynchronous Advantage Actor-Critic (A3C) algorithm, which can effectively address the decision making in dynamic and uncertain energy markets. The framework focuses on intra-monthly transaction clearing mechanisms with the aim of optimally enhancing earnings. The research formulates an offer model both for thermal and renewable power generation enterprises, which is applicable to medium-term monthly and intra-monthly trading. The study then validates this framework through three distinct analyses: the returns of various bid methods under standard scenarios, the offer strategies return of power generation companies with diverse cost profiles, and the impact of varying renewable energy proportions. The multi-angle simulations confirm that the model presented in this paper offers a scientific basis for the development of offer strategies for power generation companies and enable power generating firms to effectively adopt to the current power market.

A game-based power system planning approach considering real options and coordination of all types of participants

With the ongoing advancement of global power marketization, the increasing diversity of stakeholders in the power market and their interactions have a significant impact on power system planning and operation. Traditional methods employ game theory to describe the interactions among market participants. However, these approaches fail to include all relevant participant types and overlook the long-term uncertainty value of planning schemes. Therefore, this paper proposes a game theory-based power system planning approach considering real options (RO) and coordination of all types of participants. First, the dynamic gaming interaction among supply-transmission-demand is studied, and the independent system operator is integrated into the game planning framework based on the overall perspective of power market operation. Moreover, the new power supply, grid planning, and operation of distributed generation (DG) are taken as decision variables based on the RO theory, and a game decision model of all participants in the power system planning considering the investment uncertainty is constructed. Finally, the iterative search algorithm is employed to solve the model, and the framework is validated based on the IEEE 30-bus system. The results indicate that through collaborative planning involving various market participants, Generation Companies and Transmission Companies expand their investments to maximize profits, while large power consumers reduce their investment in DG and instead increase their direct power purchases to lower costs. Moreover, these participants are inclined to pursue projects with higher asset value volatility, which further enhances their profit. By integrating RO theory with game theory, the proposed approach establishes a comprehensive planning framework that effectively addresses investment uncertainty. This integration enables market participants to make more optimal decisions, ultimately leading to an enhancement in the long-term economic performance of the power system.

Determining the optimal bid direction of a generation company using the gradient vector of the profit function in the network constraints of the electricity market

AbstractOne of the primary challenges faced by generation companies (GenCos), which operate multiple generation units within the electricity market, is the determination of the optimal bid price for these units to maximize profit. This paper proposes a novel approach to ascertain the optimal bid price direction for GenCos by leveraging the gradient vector of the profit function within the constraints of the electricity market. First, the Jacobian matrix of unit profits is computed using the electricity market structural decomposition method. This matrix highlights how the profit of generation units is affected by market input parameters, including the bid prices of the units. Then, the gradient vector of the GenCos' profit function and the optimal bid price direction are derived from the Jacobian matrix. The methodology is applied to a 24‐bus IEEE network, with results validated against those from a simulation method to confirm the efficacy of the proposed approach. The simulation results show that the highest and lowest profit changes with a step increase of 0.1$/MWh are observed for GenCo 4 and GenCo 6 with values of 60.28 and 2.20 $/h, respectively. The proposed approach can be effective in the changes of bid direction of the units of a GenCo to achieve the highest possible profit.

Estimation of Domestic Load Profile for Effective Demand-Side Management

In this article domestic base and shift based load profile are estimated for effective Demand Side Management (DSM). Domestic electricity consumption in Pakistan accounts for approximately 40% of total electricity sales. Effective DSM can reduce electricity consumption. Power generation companies are increasingly recognising the importance of analysing their customers' consumption patterns. This article proposes an agent-based modelling approach for forecasting household electricity consumption profile. AnyLogic software is used for establishing the model that uses variables and functions to estimate base and shiftable load of three hundred households. This article also estimates the weekly and 24- hour load profile for effective DSM operations by smart grid infrastructure. The results show that the agent-based simulation closely approximates real-time data, with a difference of only 2.4%.

Coordinated frequency control strategy for modern power system considering engagement willingness

The large scale integration of renewable energy sources (RES) is posing unprecedented challenges to the frequency stability of modern power systems, with system inertia being the primary concern. Under such a landscape, this paper introduces a coordinated control strategy aiming at enhancing frequency stability by swiftly adjusting the power output of flexible resources to mitigate power imbalances and further dampen frequency fluctuations. Firstly, a communication-free center of inertia (COI) frequency estimation method is proposed. An equivalent inertia estimation method tailored for modern power systems is then developed by exploiting the correlation between synchronous inertia and active power fluctuations. By integrating the COI frequency variation rate with the estimated equivalent inertia, the imbalance power is calculated and compensated through the proposed coordinated control strategy. In this work, two typical flexible resources including photovoltaic (PV) generation and electric vehicles (EVs) are modeled and integrated into the testing system in PSCAD/EMTDC. Considering the contradiction between maximizing generation profit and reducing power output to mitigate frequency variation, the Corporate Social Responsibility (CSR) of renewable Generation Companies (GENCOs) is modeled to sketch their willingness of engaging into frequency support. Moreover, the frequency modulation potential of EVs in both commercial and residential areas are quantified for practical implementation. The simulations results successfully validate the effectiveness of the proposed control strategy.

E-Waste Management in Nam Theun 2 Power Company Limited: A Review of Issues, Challenges and Solutions

This paper reviews the e-waste management practices of Nam Theun 2 Power Company (NTPC), one of the leading power generation companies in Laos. The study focuses on the company's use of Total Environmental Solutions – Asset Material Management (TES-AMM), a leading e-waste recycling company, to manage their e-waste. In this paper, a mixed-methods research design has been applied, including a survey of employees and interviews with key stakeholders at NTPC, as well as document analysis to assess the company's compliance with the relevant regulations and standards. The results of the study indicate that NTPC has an effective e-waste management system in place that is compliant with the relevant regulations and standards, but it also highlights opportunities for improvement. The study also provides an overview of TES-AMM's e-waste recycling process, which is found to be in line with international standards. The study contributes to the existing literature by providing insights into the specific challenges and opportunities for improving e-waste management in the power generation industry part in Laos.

An Evolutionary Game Model of Market Participants and Government in Carbon Trading Markets with Virtual Power Plant Strategies

The utilization of conventional energy sources commonly leads to heightened energy consumption and the generation of specific forms of environmental pollution. As an innovative power management and dispatch system, virtual power plants (VPPs) have the potential to significantly enhance the flexibility and stability of power systems, while supporting carbon reduction targets by integrating distributed energy resources (DERs), energy management systems (EMSs), and energy storage systems (ESSs), which have attracted much attention in the power industry in recent years. Consequently, it can effectively address the variability and management challenges introduced by renewable energy. Furthermore, optimizing power market dispatch and user-side power management plays a pivotal role in promoting the transition of the energy industry towards sustainable development. The current study highlights the unresolved issue of strategic decision-making among market participants, such as energy companies, generation companies, and power distribution companies, despite the potentially significant benefits of VPPs. These entities must carefully evaluate the costs and benefits associated with adopting a VPP. Additionally, governments face the complex task of assessing the feasibility and effectiveness of providing subsidies to incentivize VPP adoption. Previous research has not adequately explored the long-term evolution of these decisions in a dynamic market environment, leading to a lack of adequate understanding of optimal strategies for market participants and regulators. This paper addresses this critical research gap by introducing an innovative bilateral evolutionary game model that integrates VPP and carbon trading markets. By utilizing the model, simulation experiments are carried out to compare different strategic decisions and analyze the stability and long-term evolution of these strategies. Research findings indicate that the adoption of VPP technology by market participants, in conjunction with government policies, results in an average 90% increase in market participants’ earnings, while government revenues see a 35% rise. This approach provides an alternative method for understanding the dynamic interactions between market participants and government policy, offering both theoretical and practical insights. The findings significantly contribute to the literature by proposing a robust framework for integrating VPPs into electricity markets, while offering valuable guidance to policymakers and market participants in developing effective strategies to support the sustainable energy transition. The application of this model has not only enhanced the understanding of market dynamics in theory, but also provided quantitative support for strategic decisions under different market conditions in practice.

Development of a coupled agent-based generation expansion planning tool with a power dispatch model

Power companies need to adapt their generation expansion planning in response to changing market, climate and regulatory conditions as global warming, electrification, and technology breakthroughs continue. To fortify energy system resilience, it is critical to understand the collective effects of their autonomous decisions on power systems operations and reliability. To this end, we developed an integrated framework, an agent-based model (ABM) coupled with a power dispatch model (PDM) (referred to as ABM-PDM), tested on the Texas 123-bus transmission system in the Electric Reliability Council of Texas (ERCOT) region. Agents (power generation companies) can invest in natural gas, solar, and wind technologies to maximize profits from 2021 to 2050, using market information from the PDM based on their capital budget and perceived costs, financial incentives for renewable energy, and climate risks. We applied ABM-PDM to assess how power companies respond to future technological advancements and climate change. After demonstrating model credibility, we explored 25 combinations of cost and capacity factors reflecting a variety of technological evolution trajectories. Results indicated that to replace wind over solar for replacing existing fossil-fuel power plants due to lower costs and higher capacity factors. Additionally, as more agents invest, the energy market becomes more competitive, and system-wide electricity prices drop. We also analyzed the impacts of temperature increases on investments using seven projections, from 0 to 6 °C, during the modeling period. The results showed that as temperatures rise, agents invest more to accommodate the increasing loads. ABM-PDM incorporates risk attitude and learning into companies’ decision-making, providing additional information on generation expansion for the non-optimal future of power systems.

Electric vehicles as facilitators of grid stability and flexibility: A multidisciplinary overview

AbstractElectric vehicles (EVs), as facilitators of grid stability and flexibility, provide a critical solution to the energy infrastructure's evolving demands, underscored by the growing integration of renewable energy sources (RES) and the rapid increase in EV adoption worldwide. This trend is particularly evident in Europe which is experiencing dramatic increases in both the adoption of RES and EVs. Vehicle‐to‐grid (V2G) technology allows EVs to operate as a two‐way power flow to both draw and feed electricity into the grid. This multidisciplinary overview examines the role of V2G systems in enhancing grid performance, identifying corporate vehicle fleets as key flexibility providers, and integration with Smart Grid technologies as a key element for successful V2G implementation. In a scoping analysis of recent literature (2005–2024), we identify challenges such as privacy, security, and regulatory compliance as well as a critical gap in establishing economically sustainable models for aggregators, distribution system operators (DSOs), generation companies (GENCOs), and end‐users. Drawing from these insights, we then discuss the necessity for future research to develop models that ensure equitable benefits across stakeholders and the importance of models that can adapt to country‐specific mechanisms. The findings from our overview argue that the integration of EVs, V2G, and RES are essential components for developing future energy systems that are resilient, adaptable, decarbonized, and sustainable.This article is categorized under: Cities and Transportation > Electric Mobility Energy and Power Systems > Energy Infrastructure Energy and Power Systems > Energy Management

Does Vertical Integration Always Reduce an Electricity Retailer’s Fixed Price Offer?

We use a profit-maximizing model of forward pricing, which revealed that an electricity retailer’s fixed price offer (FPO) contains a large forward premium that encourages vertical integration of a generation company and a retailer. However, vertical integration does not always reduce FPOs, particularly when residential customers are segmented by consumption size and price sensitivity. Hence, a proposed merger of a big generation company and a big retailer requires regulatory scrutiny.

A novel development of hybrid maximum power point tracking controller for solar pv systems with wide voltage gain DC-DC converter

Now, the present power generation and distribution companies are working on renewable energy systems because their features are low-level atmospheric pollution, producing less greenhouse pollutants, more reliability, good static performance, and high robustness. In this work, the sunlight Photovoltaic (PV) system is selected because of its advantages are easily available in the atmosphere, high flexibility, zero carbon footprint, easy to maintain, and less transportation cost. However, solar networks produce nonlinear I-V characteristics. Due to the non-linear nature of the solar system, the extraction of peak voltage from the PV module is a very tough task. So, in this article, a variable modified step grey wolf method is integrated with the adaptive-neuro-fuzzy-inference-system to improve the energy production of solar systems. The features of this proposed maximum power point tracking controller are fast identification of the solar system operating point, generating the less fluctuated oriented converter load power, providing more MPP tracking accuracy, less dependence on the solar system installation, and useful for all environmental bad weather conditions. Another problem of solar systems is less voltage production which is improved by introducing a wide voltage gain-boost converter circuit. The features of this converter circuit are less development cost because it does not require more power electronics switches. Here, the proposed IGWM with an AFLC-fed sunlight system is investigated by using MATLAB/Simulink.

Solving a bilevel strategic offering model for a generation company in a hydrothermal energy market: A new convexification approach

This paper proposes a bilevel strategic offering (SO) model suitable for a strategic company (SC) that operates in a hydro-dominated day-ahead energy market. The upper-level model of the proposed SO model maximizes the SC’s profits, while the lower-level model embodies a hydrothermal market clearing (MC). This MC incorporates detailed hydro and thermal constraints for units of both the SC and non-strategic companies (NSC), in addition to the network constraints. The proposed solution approach entails replacing the lower-level model with its corresponding primal and dual constraints, along with a strong duality equality constraint. This leads to a non-convex mathematical programming with equilibrium constraints (MPEC) model, which is then reformulated to feature only a single bilinear term in its structure. Finally, the recast MPEC model is convexified around an initial point and solved iteratively until a convergence criteria is achieved. The model and solution approach proposed are evaluated on an adapted version of the IEEE 24-bus system, exploring scenarios where the SC acts strategically or non-strategically (perfect competition).

Estimating the actual emission cost in an annual compliance cycle: Synergistic generation and carbon trading optimization for price-taking generation companies

The annual compliance cycle of the carbon trading system gives generation companies (GenCos) the flexibility to buy allowances at preferred times rather than the same time that emissions happen. How to estimate the actual emission cost under fluctuating carbon prices and make better generation decisions become challenges for GenCos. A synergistic generation and carbon trading optimization method is proposed in this paper with a non-speculative dynamic (NSD) carbon trading strategy. The NSD strategy utilizes stochastic dynamic programming to make optimal carbon trading decisions and to provide accurate emission cost estimations for GenCos to make generation decisions. To control risks, the NSD strategy forbids intentional speculations in the carbon market but allows both buying and selling of allowances according to the yearly total emissions estimated by the generation strategy. A risk control scheme by adjusting the allowance-emission balancing period is proposed so that GenCos can smoothly shift between the traditional daily-balanced carbon trading strategy and the NSD strategy. Properties of the NSD strategy are analyzed and proved theoretically under some sufficient conditions, which makes the strategy interpretable and trustworthy. Numerical simulations verify that the proposed method can help GenCos lower their emission costs and improve their overall profits in a year.

How to Construct a Carbon Asset Management System for Chinese Power Enterprises: A Survey-Based Approach

The greenhouse effect of atmospheric pollution is globally concerning. China is transitioning to market-driven emission reduction from policy-driven efforts. In 2021, key power industry emitters were included in the national carbon trading market. However, many companies lack willingness and understanding of carbon assets, hindering progress. Research on power companies in Beijing, a political and carbon market pilot region, is valuable. This study obtained data on the participation of Beijing’s power generation companies in the carbon market and the construction of their carbon management systems during the first compliance period through the distribution of surveys. The findings revealed that the implementation and preparation of carbon inventory, Chinese Certified Emission Reduction (CCER) development, the allocation of carbon management personnel, and training are key factors influencing the actual effectiveness of carbon management within companies. Based on the survey results and the impact pathways, this study outlines the preparatory work, system content, and construction steps for power companies to build a carbon management system. It summarizes five key areas of work for power companies in managing carbon assets: carbon inventory, carbon management personnel and mechanisms, carbon trading, carbon emission reduction, and carbon finance. This provides guidance to help power companies fulfill their obligations smoothly, add value to their carbon assets, and achieve low-carbon development goals. Additionally, it offers a reference for other industry enterprises that are about to enter carbon trading.

Informer model with season-aware block for efficient long-term power time series forecasting

With the development of electricity spot markets, accurate electricity load forecasting enables power generation companies to supply the right amount of electricity, greatly avoiding power waste. As a result, time series forecasting in the field of power can bring great benefits. Previously, the Informer model successfully introduced the Transformer into long time series forecasting(LTSF) by proposing the ProbSparse self-attention mechanism, which solved the inherent problem of high memory complexity in self-attention. Recent research has further demonstrated the potential of the self-attention for mining complex dependencies. However, the limited amount of historical data has become one of the main challenges in applying deep learning techniques to power LSTF tasks. Previous researches often add a large number of time covariates to provide more information. In this paper, to address this issue, (i) we design a simple but effective Season-aware Block to enhance the model's ability to mine artificial prior information in temporal covariates; (ii) we conduct experiments using the provincial power data of Zhejiang Province, China, from 2019 to 2022, and our model outperforms other models, achieving a 19 percent MSE relative improvement, which can help power generation companies reduce costs and further reduce prices to benefit consumers; (iii) we conduct ablation experiments to assess the efficacy of the Season-aware Block in extracting temporal periodic features. Furthermore, we elucidate the underlying reasons for the effectiveness of both the self-attention mechanism and the Season-aware Block through visualization experiments through visualization techniques.

E-maintenance in hydropower energy generation: A case study of Enel Colombia

Traditionally, maintenance in the hydropower industry has been a labour-intensive and time-consuming process. It often relies on scheduled inspections and manual intervention. E-maintenance in hydropower plants can help to address this challenge by allowing remote monitoring and control of plant equipment, enabling timely detection and diagnosis of potential problems. This paper presents a case study of the implementation of an e-maintenance strategy for hydropower infrastructure at one of the largest generation companies in the Colombian electricity market. A machine learning model, implemented by Enel Colombia, is fed with recorded data on turbine bearing temperature and active power generation to predict problems in hydropower generators. The results show how e-maintenance can reduce operating costs and avoid breakdowns in hydro generation.