Optimization Of Power Plant Operation Research Articles

Use of Data Analytics for Power Plant Operation Optimization

Optimized operation and maintenance of a power plant to improve the KPIs is the key to success. Forced outages not only make the unit unavailable but also increase the number of start-ups and hence high cost and deteriorated plant life. Fault diagnostics and prediction of future behavior of equipment by data analytic techniques will be very handy in improving reliability. On the other hand, the cost of generation depends upon how efficiently the plants are operated. Any deviation from the optimized design point, results in losses and hence as far as possible the deviations should be minimized. However, the deviations are unavoidable due to various reasons. Along with efficient operation, Environmental compliance, and flexibility is also to be ensured to meet the statutory requirements. With the advancement of computing techniques, data analytics, and Artificial Intelligence are being used for the optimization of many industrial processes. Given the background, the objective of this paper is to study and suggest how Data Analytics can be utilized for the optimization of power plant operations to improve reliability, efficiency, and flexibility, and minimize the impact on the environment. Many statistical methods are available to identify the hidden pattern in the data which can be used for optimisation of processes. AI is used for process control, diagnosing faults, and predicting of future behaviors so that advanced action can be taken to avoid surprises. The domain expertise along with data analytic methods can be utilized to find solutions to a variety of problems. In this paper, a comprehensive data analytic tool with four modules i.e., “Efficiency optimization”, “Plant Health monitoring and reporting”, “Optimization of life consumption” and “Environment protection” has been conceived for application. These Modules work in an integrated manner and shall monitor, optimize, control, and report/advise. The tool shall have a digital replica of the individual equipment for simulation individually as well as in combination with other related equipment for whole plant performance prediction and diagnosis. The replica shall use its database for machine learning and for running of diagnostic process.

ПОВЫШЕНИЕ ЭФФЕКТИВНОСТИ РАБОТЫ ТЭЦ ПАО «ГАЙСКИЙ ГОК» ЗА СЧЕТ ДОЗАГРУЗКИ ТУРБОАГРЕГАТОВ

Competitiveness of Gaysky GOK depends on energy costs, including the purchase of electricity for technological needs. Turbine units additional load is often considered in the context of optimization of power plant operation under changing conditions of electricity demand. By increasing the turbine load at certain times or when necessary to respond to changes in grid load, it is possible to improve resource utilization efficiency and reduce power generation costs. This article describes the methods of increasing the efficiency of steam-turbine units of thermal power plant of PJSC "GAYSKiy GOK", due to additional loading of operating turbine units up to nominal capacity. At low cost of electricity produced by the CHPP, compared to electricity purchased on the wholesale electricity market, which will reduce the company's costs for electricity purchase. 
 Steam, which is produced during the operation of turbine units in nominal mode and in the winter period is used for heating the city, in the fall-summer-spring periods it is proposed to use for the operation of evaporator plant, which will be used for desalination of clarified water, which after processing will be used in the technological process of the CHPP itself, as feed water for boilers, which will eliminate the need for water treatment of the CHPP.

The low-carbon economic operation strategy of virtual power plant under different electricity-gas-heat-carbon multi-market synergy scenarios

Studying the grid integration of renewable energy power generation is crucial for achieving the goal of carbon neutrality since it may have a significant influence on the secure and reliable functioning of the power system. In order to solve the problem of deviation impact caused by renewable energy fluctuations and the optimal scheduling of VPP (Virtual Power Plant), the study divides the internal aggregation unit of the virtual power plant into two parts to model. One part is the source equipment, including wind power generation equipment, gas turbine, gas boiler and waste heat boiler. And the other part is the generalized Energy storage, including electric vehicles, air conditioners and alternative response loads. Ultimately, a generalized energy storage-based virtual power plant operation optimization model is developed under multi-market coordination of electricity-gas-heat-carbon. According to the study’s findings, adding more power-to-gas technology boosts revenue in the carbon trading market by 25.24 percent. The energy market’s revenue is equal to that in the absence of a carbon trading market, and the income of the natural gas market increases by $ 32.96. The income of the carbon trading market is $ 181.51, and the final operating cost is reduced by $ 180.80, a drop of 7.81%. To sum up, the suggested approach may more effectively achieve the best distribution of different energy sources, increase the dependability of VPP operation, and make it more low-carbon.

Urban virtual power plant operation optimization with incentive-based demand response

Urban virtual power plant (VPP) shows great potential in alleviating urban power shortage or power supply-demand imbalance. This study proposes a bi-layer optimization model considering incentive-based demand response (IDR) to optimize the operation scheduling of urban VPP. The bi-layer optimization model includes the lower-layer IDR model and the upper-layer urban VPP optimal operation scheduling model. In the lower-layer IDR model, user satisfaction, comfort and preference are considered. Users mainly participate in the scheduling of urban VPP by IDR. The demand response resources obtained by urban VPP through providing incentives for users can effectively alleviate the energy supply pressure. In the upper-layer urban VPP optimal operation scheduling model, the scheduling plans for generating units and trading plans for the electricity wholesale market can be determined. Through upper-layer scheduling, it can not only meet the energy demand of users, but also minimize the urban VPP operation cost. The experimental results show that the proposed bi-layer optimization model for urban VPP operation considering IDR can achieve urban energy resources optimal allocation and support urban energy supply-demand balance, on the premise of ensuring economic benefits.

An in-situ calorimeter for coal powder: A numerical investigation

Timely monitoring of the calorific value of coal is beneficial to combustion regulation during the operation of coal-fired power plants. An in-situ measurement system applied to power plants was proposed. The related model was described in detail and validated through bomb calorimetry. The influences of multiple model parameters with different thermometer and flowmeter accuracy on measurement time and uncertainty have been analyzed. Measurement properties of the bomb calorimeter and this improved calorimeter were compared at different conditions. The results reveal the increases in the gas volume flow rate and the water mass flow rate have a positive effect on measurement time while coal mass and the mass ratio of water in two cylinders show a negative effect. Relative standard uncertainties are reduced to some extent with all of these factors. It is evidenced the proposed calorimeter has the potential for fuel calorific value monitor for power plant operation optimization.

A multi-energy complementary virtual power plant operation optimization model for distributed energy aggregation is considered

In this paper, a stochastic scheduling optimization model of virtual power plant (VPP) is established to maximize the net operating income of VPP considering the loss of uncertain risks. Firstly, the model of conventional scheduling optimization of virtual power plant is presented. Then, the risk conditional value (CVaR) theory is introduced to quantify the operational risk level of VPP, and the constraint conditions containing random variables are converted by the confidence method to establish the risk avoidance scheduling optimization model of VPP. Finally, an improved IIEEE30 node system is used as a simulation system to verify the validity and applicability of the proposed model.

Managing the risk of emergency situations caused by staff at thermal power plants

Optimization of power plant operation in start and stop modes is an important task aimed at increasing the reliability and efficiency of its operation. The purpose of development of the new organizational mechanism is reduction of losses connected with incorrect actions of personnel during launching and shutdown operations of Steam-Gas furnaces of thermal power stations. The work is done on the example of one of the power plants of a large generating company. Implementation has confirmed the high efficiency of innovation. Total cost of project implementation at one station is estimated at the rate of 1.4 million rubles and the economic effect of the pilot project implementation in two years was estimated at 5.6 million rubles. It is also noted that further research is required to perform economic calculations for clarification as to which one of the new methodological approaches should be developed in the future. Conclusion was made that, first, regulation and control of personnel actions at the workplace is actually the first example of the distribution of the well-known Kaizen system and its modifications to a new specific area – energy generation. Second, the presented development can be a technological platform for further increase of the level of automation of operation of steam-gas and steam-power plants in non-stationary modes, up to full digitalization of control operation of thermal power plants.

Hybrid marine power plants model validation with strategic loading

Abstract: Recent advances in Energy Storage Devices (ESD) technology has enabled new supervisory control strategies for power generation and distribution on hybrid marine power plants, supported by new class society regulations. A marine hybrid power plant is characterized by the presence of both a traditional power producer, i.e. diesel Generator-set (genset), and an ESD, i.e. batteries. In this paper, two models to estimate strategic loading average fuel consumption were derived and compared. The first model is a higher fidelity dynamic model formulated as a hybrid system, in which the simplified engine dynamics is included, accounting for both continuous-time and discrete-time dynamics representing gensets and ESD as well as the switching. The second model consists of a steady state weighted average, where transient effects are not taken into account. Both models disregard thermal effects on the engine as well as assume that the engine fuel consumption is only a function of the engine speed and power output. The motivation to derive two models is due to the fact that the static model, due to its steady state nature, can estimate the expected weighted average fuel consumption very fast, being a new tool on the hybrid power plant operation optimization. The hybrid model, due to its dynamic nature and accounting for continuous-time and discrete-time dynamics, takes a longer time to simulate the system, which is unfeasible when it comes to real time operations. It is thus required to estimate the errors in the weighted average model due to the simplifications, asserting for which cases it is valid. The results show that the steady state model gives the exact solution in cases where dynamic effects are not relevant. It is a good approximation for systems where the switching period is much higher than the engine time constant, and the load dynamics time constant. The switching frequency is directly related to the ESD energy storage capacity, thus, systems with a large ESD will be better approximated by the steady state model than systems with relatively small ESD.