Generation Companies Research Articles

Exploration on the scientific rationality of low-carbon investment projects from the perspective of supply chain: a case study of CS thermal power plant

Climate warming has gradually become a major problem threatening human survival, and countries have begun to pay attention to carbon emissions. Energy conservation and emission reduction has become a central task in China’s economic development since the 14th Five-Year Plan. As the main force of carbon emissions in China, thermal power industry is bound to become the focus of attention in China’s low-carbon development strategy and energy conservation and emission reduction. Moreover, with the marketization of the power industry, the state has joined the market competition at the power generation sectors and the power sale sectors, and implemented the “opening the middle of the two pipes.” Therefore, the coverage of influence of carbon emissions and carbon investment behavior of power generation companies is not limited to itself, but will also be extended to the supply chain level. Based on the above background, this paper evaluates the scientific rationality of low-carbon investment projects of thermal power enterprises from the perspective of low-carbon supply chain, which not only can help enterprises achieve a win–win situation of economic and environmental benefits, but also contribute to the carbon emission reduction of the entire supply chain, thereby promoting China’s entire social and economic energy conservation and emission reduction work.

Optimizing Power Market Clearing with Segmented Electricity Prices: A Bilevel Model

The energy sector plays a critical role in reducing emissions by transitioning to renewable energy. However, integrating renewable energy into the electricity market impacts market clearing. This article investigates market clearing decisions of renewable and traditional energy companies, considering electricity price segmentation. We propose an interprovincial bilevel market clearing decision-making model. The upper level optimizes the provincial market through a unilateral bidding model, minimizing the operating cost of the power market as the objective function. Meanwhile, the lower level optimizes power purchases by minimizing the cost of acquiring interprovincial renewable energy. A case study was conducted to determine the optimal interprovincial renewable energy purchasing power for multiple energy generation companies, considering the minimum market operating cost and segmented electricity prices. The proposed bilevel market clearing model captures the interplay between intraprovincial and interprovincial transactions, integrating electricity price and ecological attributes. By solving this model, we can attain optimal interprovincial electricity purchase of renewable energy, minimizing the overall market operating cost in the province. Through simulation, 18% of interprovincial transactions are needed to absorb 7.21 KWH of renewable energy from other provinces. When considering price segmentation, the cross-provincial two-level market clearing model can reduce the market cost by CNY 1,554,700.

Multiplayer Nash–Stackelberg Game Analysis of Electricity Markets With the Participation of a Distribution Company

The integration of a large number of distributed energy resources into the distribution system poses operational challenges and creates opportunities to generate profits in the electricity market. In this study, a multileader one-follower model is built to elaborate the Nash–Stackelberg game of the electricity market, where a distribution company participates in a competition with a generation company. The distribution company, as one leader, decides on the bidding price and quantity to maximize its profits in the market by integrating the distributed energy resources of multiple distribution systems after satisfying the local demand, whereas the generation company, as another leader, participates in the competitive market. As the follower, the independent system operator clears electricity markets. Moreover, a tailored algorithm is proposed to obtain the global Nash equilibrium point of the multileader one-follower Nash–Stackelberg game model. First, the proposed game model is transformed into a generalized Nash equilibrium game model; then, the transformed game model is converted to a potential game model for a solution. Finally, case studies on a constructed transmission and distribution joint system and a practical transmission and distribution joint system are performed to demonstrate the effectiveness and scalability of the proposed model and algorithm.

Modeling Strategic Behaviors for GenCo With Joint Consideration on Electricity and Carbon Markets

With the development of carbon markets, conventional generators are undergoing electricity markets and carbon markets at the same time. To fulfill the carbon market requirements and cope with the emerging cross-market arbitrage, the multi-market strategic behaviors of the generation company (GenCo) should be carefully studied. In this paper, the strategic behaviors of a GenCo with joint consideration of the electricity market and carbon market are modeled. First, the dynamic carbon emission intensity (CEI) is introduced to achieve the numerical relationship between the power output and carbon emissions, by which GenCo is demonstrated as a prosumer in the carbon market. Then, the strategy model is built in a bi-level structure. GenCo's multi-market profits maximization model is built at the upper level to determine the best bidding curves. The lower level is made up of two market-clearing models: the day-ahead electricity market for economic power dispatch and the bi-directional carbon market accounting for prosumers' variable trading positions. Then, the high dimensional nonlinearity bilevel model is reformulated and part-linearized by the Karush-Kuhn-Tucker and strong duality theorems. Case studies based on an IEEE 30-bus system indicate that dynamic CEIs have a significant impact on GenCo's cross-market arbitrage by determining its trading volume and positions in the carbon market.

(Invited) Current Status of SOFC Deployment and Technology Developments in Korea

In Korea, the supply of stationary fuel cells for power generation is being promoted by the mandatory RPS program. The deployment of fuel cells in Korea began in 2012. Currently, fuel cells of about 880 MW have been supplied. Among them, the amount of SOFC system is about 220 MW, and the SOFC installation started in 2014. About 40 MW in 2021 and 50 MW in 2022 were installed. The deployment of residential SOFCs has just begun, and a small number of systems have been deployed. In Korea, fuel cell deployment is accelerated by the mandatory supply amount allocated to power generation companies by the RPS policy, and in addition, the clean energy supply promotion regulation granted to public buildings partially contributes to fuel cell supply. Several Korean companies have developed the SOFC and SOEC technologies under the national program, and major projects are the development of a 200 kW SOFC and a 20 kW SOEC systems. The 2~8kW class SOFC products have been developed already and are in deployment. In Korea, SOEC demonstration is being also promoted to store electricity generated from renewable energy, and about 1.5MW SOEC is scheduled to be demonstrated by 2024. The Korean government enacted the Hydrogen Law in 2019, and under this law, development and deployment of hydrogen and fuel cell-related technologies are in progress. In addition, a hydrogen roadmap was established as an implementation plan to encourage achievement of the deployment targets. In this talk, the achievements of the SOFC R&D and deployment, and national hydrogen roadmap in Korea are introduced in more detail.

Multicounty fuel constraint‐based dynamic economic dispatch problem using horse herd optimization

AbstractDue to the diminution of fossil fuels, the profitable usage of fuel for the generation of power is reducing. It is an extremely vital concern for power generation companies. The fossil fuel‐based power plant must operate within its fuel limits and contractual restrictions. Therefore, economic optimization is required to optimize the fuel cost for generation under fuel constraints. To address this issue, in this work, a horse herd optimization algorithm (HOA) is developed to solve multicounty fuel‐constrained dynamic economic dispatch with demand‐side management incorporating wind turbine generators, solar photovoltaic plants, and pumped hydro storage plants. The efficiency of the suggested HOA algorithm has been revealed on a selected test system. The HOA technique shows better convergence as compared with the other methods. Moreover, the computation time requirement of the HOA technique is also lower than that of other compared methods. From numerical results, it is observed that the consumption of fuel is sufficiently high if constraints are incorporated into the problem. Simulation results are matched up to those obtained from hierarchical particle swarm optimization with time‐varying acceleration coefficients, fast convergence evolutionary programming, and differential evolution. After comparison, it is observed that the recommended HOA offers a better‐quality solution.

Finding Nash equilibrium based on reinforcement learning for bidding strategy and distributed algorithm for ISO in imperfect electricity market

Finding Nash equilibrium in an imperfect market with active and strategic resources is key to the successful operation of the electricity market. A hierarchical Nash distributed reinforcement learning (HNDRL) framework is proposed to realize the interaction between the market participants and independent system operator (ISO). In the bidding stage, multi-agent Nash policy reinforcement learning is utilized to update the bidding strategies for market participants, i.e., generation company (GenCo) and distributed company (DisCo), which maximizes their own profit with imperfect information. The bidding action is updated by the probability iteration to deal with the continuous bidding strategy. And in the market clearing stage, the distributed dynamic-average consensus optimization algorithm is proposed at the ISO level to obtain the locational marginal price (LMP) and transaction quantities. With the participation of the demand-side in the electricity market, the proposed HNDRL framework can reach a Nash equilibrium with the optimal bidding strategy and handle global resource constraints in a distributed way. In addition, theoretical analysis is provided to ensure that the participants’ strategy exponentially converges to the Nash equilibrium for both convex and non-convex problems. Finally, the IEEE 30-bus system is employed to illustrate the efficiency. Compared with the existing method, the simulation results show that the proposed HNDRL framework has a faster convergence rate with the range of 2.667–3.333 times and can obtain higher profit with the range of 1.4286%–7.1429% at the Nash equilibrium point.

Scenario Planning of PLN Indonesia Power in 2030: To Be a Leading Green and Sustainable Power Generation Company

Following the company's transformation in 2023, PLN IP's generating capacity now reached 21 GW, establishing it as the largest power generation company in Southeast Asia. However, most of PLN IP's power plants rely on fossil fuels. Therefore, the purpose of this study is to formulate a strategy that enables PLN IP to become a leading sustainable company and support Indonesia's target of achieving a renewable energy mix of 24.8% by 2030. The scenario planning method is used in this study to anticipate and prepare for future critical uncertainties that may impact the business of PLN IP. Critical uncertainties are obtained from analyzing the company's business, which is then elaborated with the results of semi-structured interviews from internal and external stakeholders. Five scenarios have been identified, namely: Parking the Bus, Kick & Rush, Tiki Taka, Gegenpressing, and Catenaccio. Each scenario is driven by a different critical uncertainty, consisting of electricity supply and demand, fuel prices, regulations, technological advancements, and investments. Key leading indicators of driving factors have been determined to detect potential scenario trends as early warning signals. This study is expected to serve as a reference for policymakers and stakeholders in the electricity sector to apply a balanced strategy for providing clean, reliable, and affordable electricity in Indonesia.

Evaluating long-term profile of demand response under different market designs: A comparison of scarcity pricing and capacity auction

Demand response (DR) is widely acknowledged as a promising solution to maintain the capacity adequacy of the power system. In China, DR is now shifting from government pricing to market-oriented pricing. However, the market pricing mechanism to be adopted is still inconclusive and the long-term influence of different pricing mechanisms on incentivizing DR remains unknown. Moreover, the market contains multiple participants, various strategic behaviors, and non-linear clearing procedures while traditional optimization models cannot capture the complex dynamics. This paper adopts a system dynamic (SD) method to evaluate the long-term profile of DR under different market designs. Firstly, an investment decision model of generation companies is formulated to simulate the growth tendency of capacity supply in a long-term period. Secondly, in the context of unmatured marketization, the growth tendency of DR and retail market users are modeled by Bass diffusion. Finally, scarcity pricing and capacity auction are applied to investigate the incentive influence of different market designs on DR. Through the simulation to 2035, the results show that compared with the energy-only market, scarcity pricing, and capacity auction can raise the capacity of DR from 3.12% to 7.58% and 9.21%, respectively.

Floating offshore wind projects development in South Korea without government subsidies

The South Korean government is encouraging the active participation of power generation companies in the offshore wind power project by announcing the renewable energy certificates (REC) weighting plan. However, from a long-term perspective, the offshore wind power must be able to generate profits without government support to demonstrate its business feasibility and attract the voluntary participation of power generation companies. This is because government support may be subject to change, depending on the internal and external political circumstances of the country. This report calculates the expected costs for a 495 MW floating offshore wind farm in South Korea’s market environment and examines how the feasibility of the project shifts depending on the country’s current REC weights. Furthermore, this study intends to determine whether floating offshore wind power can generate profits without the Korean government’s support by calculating the expected profit in combination with the green hydrogen project. The net present value, levelized cost of energy and internal rate of return (IRR) indexes are calculated according to the project’s specific particularities, such as power purchase agreement, REC weighting, distance from shore and sea depth. Based on this, an index-based comparison is revealed and the margin for profitability for such an investment is discussed. The calculation results revealed that with a decrease in capital expenditures and operating expenditure or an increase in the system marginal price under specific assumptions, the value of IRR increased.

Project Management Dynamics and Performance of Kenya Electricity Generating Company Projects

Kenya continues to face challenges regarding the actualization and timely implementation of electricity generation projects aimed at increasing the electricity capacity required in the quest for attaining industrialization status by the year 2030. Out of the projected flagship electricity generation projects, less than 20 percent of the projects had been translated into the national grid by the end of the year 2022. Such trends call for an in-depth study of project dynamics that determine the performance of electricity generation projects. To research the problem, the focus was given to Kenya Electricity Generation Company (KenGen) Plc, which is a state-owned electricity generation company accounting for 60 percent of the electricity generation market share in Kenya. The purpose of the study was to determine the project management dynamics that determine the performance of electricity generation projects. Specifically, the study sought to evaluate the influence of project financing, project procurement, organization dynamics, individual dynamics and stakeholder management dynamics on the performance of electricity generation projects in KenGen. The study was anchored on system theory, constraints theory, and competence theory of project management. The earmarked population was 10 electricity generation projects implemented by sixty-eight employees who had formal project management appointments in the power plant construction projects that ranged from hydroelectric, geothermal, thermal and wind generation modes. A census survey method was used, targeting the sixty-eight respondents. Descriptive and inferential statistics were used to analyze the data. Multiple regression analysis was used to determine the relationship between the variables. The study established that all the independent variables had positive regression coefficients, indicating that a unit rise for each independent variable facilitated a consequent rise in the dependent variable. However, only finance dynamics revealed a statistically significant effect, indicating that project finance dynamics had a significant effect on the performance of electricity generation projects. The study recommended that priority should be given to finance dynamics while scoping the electricity projects above other project dynamics. Financial analysis of the project financier's capacity to ensure uninterrupted project milestones financing was identified as an aspect requiring consideration in project financing. This outcome will be helpful to stakeholders in the energy sector, academicians and electricity project financiers. Suggestions were made for further studies on other financial dynamics not accounted for in the study and non-financial dynamics that were not established to have a significant effect on generation projects

Appraisal and Comparative Analysis of the Privatized Nigerian Electricity Sector: Pre and Post Reforms Experience

The Nigeria’s electric power sector has undergone several policy reforms to address the myriads of issues and challenges confronting the sector for several years. The reforms were implemented in order to establish a highly competitive market to improve management performance and efficiency, increase generation, and attract private investors and enhancing reliable and cost-reflective power supply system. It is noticeable however, that there seems to be no significant improvement in the sector, even after the privatization process. This work assessed and compared the performance of the electric power sector in the pre and post-reforms eras, reviewed its implications and impacts on the living standard of the people. It also identified and discussed some critical issues and challenges confronting the sector, and made useful recommendations in ensuring, a reliable and effective electric power supply system. The study used both quantitative and qualitative descriptive, survey design approach. Relevant data were collected from both primary and secondary sources and field survey mechanism. The research instrument used was a self-designed questionnaire titled “Questionnaire on Appraisal and Comparative Analysis of the Privatized Nigerian Electricity Sector (QACAPNES)” to elicit information from the sample that served as data for the study). Three experts validated the instrument. The four research questions raised were descriptively answered. The data were collected and analyzed descriptively (using weighted mean and standard deviation. The internal consistency of the questionnaire items was ascertained through the split-half method, and the responses were subjected to correlation analysis at a 0.05 significance level, yielding a coefficient of 0.81, a value high enough for the instrument to be considered reliable. For the research questions, a mean mark of 3.5 and above was recorded as "True", and a mean mark below 3.5 was assumed as "False". The result of the analysis showed that no significant improvement has been made that is totally different from the pre-privatization era except the successful unbundling of defunct Power Holding Company of Nigeria (PHCN) into generation, transmission and distribution companies. The study recommended full implementation of a cost-reflective tariff system, diversification of energy generation sources and a holistic review of the privatization policies for optimum performance.

Liberalized automatic generation control of interconnected thermal‐hydro‐gas system using firefly algorithm optimized PID controller

AbstractIn liberalized power system, the distribution companies are free to choose the contracts with all available generating companies (GENCOs), while the independent system operator acting as regulatory authority. In this article, the liberalized automatic generation control problem is studied for a two‐area interconnected power system. The studies are divided into two parts. In first part, the investigations are carried out with only thermal generations in each area. In second part, the multi‐diverse sourced thermal‐hydro‐gas GENCOs are assumed in each control area. For damping out the system frequency and tie‐line power dynamics, the popularly known PI and PID controllers are employed considering poolco, bilateral and contract violation transactions. In both the parts, the comparative performance between PI/PID controllers is made. Studies reveal that PID outperforms PI controller. A case study with introduction of time delay in the system is performed as the open communication infrastructure introduces inherent time delay in the smart grid scenario. To understand the impact of frequency sensitive loads on system dynamics, the effect of variation in damping coefficient (D) is studied and it is shown how the D is influencing the dynamics. The effect of double reheat turbines is also studied and found that they deteriorate the dynamics when compared to single reheat turbines. In all the above studies, the powerful nature inspired firefly algorithm is employed to optimize the parameters of controllers using the integral squared error method.

Green transition and gender bias: An analysis of renewable energy generation companies in Latin America

This study analyzes how the energy transition might change gender bias in power-generating industries. We employ a sample of 102 renewable energy generation companies from six countries in Latin America and the Caribbean: Bolivia, Chile, Costa Rica, Panama, Mexico, and Uruguay. The analysis shows that renewable generation companies with the highest relative efficiency in the labor-capital ratio are those with the highest participation of women. In addition, the results show that renewable companies are incrementing recruitment of women in energy generation; however, their participation is still lower than the sectorial average. Considering the companies' size, bigger renewables companies tend to hire more women, but those women occupy mostly non-technical positions. In addition, women's participation decreases in positions requiring higher education. Women represent 38 % of STEM employees, 54 % of non-STEM employees, and 55 % of non-qualified employees among the total of people employed in generation companies. This trend is even more acute for renewable energy generation companies, where women represent 36 % of STEM employees, 39 % of non-STEM employees, and 48 % of non-qualified employees. Concerning the role of women in decision-making roles within energy companies, wide gender gaps exist in executive and management positions; the proportion of females in the boardroom and in management roles for renewables generation companies was 24 % and 22 %, respectively. Furthermore, 68 % of surveyed companies did not have a gender policy in place. This study confirms that a change in technology alone does not generate qualitative changes in the labor market from a gender perspective.

Impacts of previous revenues on bidding strategies in electricity market: A quantitative analysis

With the development of electricity markets, generation companies (GenCos) generally participate in multiple markets at different time steps to earn profits. GenCos’ previous revenues obtained from earlier markets affect their judgement on risks, and thus influence their strategic bidding behavior. However, this influence on bidding behaviors caused by previous revenues from earlier markets is generally neglected in existing studies. This paper proposes a methodology to quantitatively analyze the effect of previous revenue on a GenCo’s bidding strategy. A bidding behavior model based on the prospect theory is developed to establish the relationship between previous revenue and bidding strategy. Then, the bidding behavior characteristics under the influence of previous revenue is derived and analyzed based on the established relationship. Finally, several typical bidding modes are summarized from the bidding behavior characteristics. Simulation results show that previous revenue has an obvious influence on the GenCo’s bidding behavior. The GenCo shows different bidding tendencies under different ranges of previous revenues due to the change in risk preference. The results of this paper validate the necessity of considering previous revenue in market equilibrium analysis and can help regulators to coordinately design the electricity market considering the coupling bidding behaviors of GenCos.

Assessing the policy synergy among power, carbon emissions trading and tradable green certificate market mechanisms on strategic GENCOs in China

The establishment of carbon emissions trading (CET) and tradable green certificate (TGC) markets in China will cause great alterations in the bidding behavior for generation companies (GENCOs) and affect the low-carbon transformation of power industry. In this paper, a Nash bargaining game model is constructed to assess the synergy of power, CET and TGC markets on decision behavior of non-renewable energy GENCOs, renewable energy GENCOs and power purchasers. The simulation results show that there is transition incentive effect, price interaction effect and profit blocking effect between three markets. With the introduction of emissions trading scheme and renewable portfolio standard (RPS), the scheduled power of renewable energy GENCOs will increase by 21.43%, and transaction prices will reduce to 449.58 CNY/MWh; while the scheduled power of non-renewable energy GENCOs will reduce by 4.66%, and transaction prices will increase to 443.71 CNY/MWh, which make all power generation types compete at equivalent price level. Under the current installed capacity distribution and load demand, it is the critical point when RPS is 18.7% and the benchmark value of free carbon quota for units above and below 300 MW are 0.857 tCO2/MWh and 0.959 tCO2/MWh. Once exceeded, it will affect social benefits and emission reduction efficiency.

Self-Scheduling of a Generation Company with Carbon Emission Trading

A carbon emission trading self-scheduling (CETSS) model was proposed. The proposed model considered not only carbon emission allowance constraints but also carbon emission trading. A new method was presented for solving CETSS problems based on piece-wise linearisation and second-order cone linearisation. The effectiveness and validity of the proposed model and method were illustrated by 10-100 unit systems over 24 hours.

Low-carbon transition model for power generation companies in China: A case study

The power sector is the largest emitter of carbon dioxide in China, and the coal-based energy structure presents an additional challenge to its decarbonization. Several studies have been conducted to find cost-optimal pathways for the low-carbon transition of the power sector or individual power plants, but few studies have taken power generation companies as a research topic. In this paper we addressed the issue by building an optimization model that can characterize power generation companies, and selected a big company as a case to study how economic and technological factors drive the company to adjust its plant allocation and affect its profitability. We find that the transformation trajectory of the company is similar to that of the power sector as a whole. The share of coal power plants will remain stable at 53% during this decade and then decline rapidly after 2030. At the same time, the proportion of renewables will rise rapidly from 35% in 2020 to 90% by 2060. The total cost of achieving the carbon neutrality target is approximately RMB 4.6 trillion during 40 years, and the annual cost is approximately RMB 114.9 billion, which is equivalent to about 40% of its total revenue in 2021. Analysis of different scenarios suggests that achieving carbon neutrality 5 to 10 years ahead of time is financially and technologically feasible. Energy storage will play a significant role whereas CCS will have little impact on this company.

Low carbon and efficiency oriented day-ahead joint electrical energy and ancillary services market clearing model for generation-side in China

China's market-oriented reform of electricity transaction and carbon neutrality target will bring new challenges to thermal power generators. Under the joint clearing mechanism of the energy and ancillary services markets, this paper proposed a bi-level model to characterize the individual-oriented behavior of the power generation company and the system-oriented collaborative decision-making behavior of generation companies. The bi-level optimization model is converted to a mathematical program with equilibrium constraints and further transformed into mixed-integer linear programming models which can be solved directly by using linearization techniques. We evaluated the impact of generation companies' behavior decisions in a regional power system under different levels of renewable energy penetration and deep-peaking compensation prices. Compared with the individual-oriented mechanism, a lower emission level, a lower average daily clearing price and higher unit efficiency would be achieved under system-oriented mechanism, even if there will be some loss of profit. The system-oriented decision-making behavior that will bring environmental benefits deserves more attempts. Therefore, for a power generation group with multiple generating units, the adoption of system-oriented decision is more conducive to realize the low-carbon transformation, which is an alternative way to promote the low-carbon transition of the power system in China.

Valuing carbon quota assets of power generation companies based on Lasso-Back propagation neural network

Carbon quota assets have become an increasingly important new type of asset in the production and operation of power generation companies, and evaluating carbon quota assets for the sustainable development of power generation companies is an urgent issue. Herein, we propose a method for evaluating carbon quota assets based on the Lasso- Back Propagation Neural Network model (Lasso-BPNN). Firstly, we further consider the impact of company operations on the value of carbon quota assets, and analyze the ways in which company size, profitability and emission reduction capacity affect the value of carbon quota assets. Secondly, we innovatively introduce intelligent algorithms into the field of carbon asset value assessment, scientifically reflecting the function mapping between carbon quota assets value and influencing factors, and build a Lasso-BPNN model to improve the accuracy of carbon quota asset value assessment. Finally, by valuing the carbon quota assets of company H in the secondary market of the Hubei pilot, power generation companies can improve their carbon quota assets value in terms of three aspects: company size, profitability, and emission reduction capacity. The study provides an effective way to assess the value of carbon quota assets and optimize the carbon assets management of power generation companies.