Thermal Power Generation Units Research Articles

A Building Energy Management System Based on an Equivalent Electric Circuit Model

In recent decades, many EU and national regulations have been issued in order to increase the energy efficiency in different sectors and, consequently, to reduce environmental pollution. In the building sector, energy efficiency interventions are usually based on the use of innovative insulated materials and on the installation of cogeneration and tri-generation units, as well as solar technologies. New and retrofitted buildings are more and more commonly being called “smart buildings”, since they are characterized by the installation of electric and thermal power generation units, energy storage systems, and flexible loads; the presence of such technologies determines the necessity of installing Building Energy Management Systems (BEMSs), which are used to optimally manage their operation. The present paper proposes a BEMS for a smart building, equipped with plants based on renewables (photovoltaics, solar thermal panels, and geothermal heat pump), where the heating and cooling demand are satisfied by a Heating, Ventilation and Air Conditioning System (HVAC) fed by a geothermal heat pump. The developed BEMS is composed of two different modules: an optimization tool used to optimally manage the HVAC plant, in order to guarantee a desired level of comfort inside rooms, and a simulation tool, based on an equivalent electric circuit model and used to evaluate the thermal dynamic behavior of the building. The paper describes the two modules and shows the main results of the validation phase that has been conducted on a real test-case represented by the Smart Energy Building (SEB) located at the Savona Campus of the University of Genoa, Italy.

Prediction technology and application of primary frequency regulation capability of thermal power unit

As one of the important functions of grid connected thermal power generation units, the primary frequency regulation function aims to increase and decrease load rapidly when the grid frequency changes, so as to achieve the stability of grid frequency. The primary frequency regulation capability of the unit is easy to be affected by the current main parameters of the unit operation and the operation mode of the equipment, which will lead to the phenomenon that the primary frequency regulation capability of the unit is insufficient. This paper introduces the basic principle of primary frequency regulation of thermal power units, and puts forward the main technical methods for prediction of primary frequency regulation capacity of units. Through the real-time prediction of the primary frequency regulation capability of the thermal power generation unit, the operation parameters and equipment operation mode of the thermal power generation unit can be changed in time, so as to avoid the lack of primary frequency regulation capability when the grid frequency changes. Finally, a typical industrial case is used to verify the effectiveness of this technology.

Assess and Optimization of Automatic Generation Control Performances for Thermal Power Generation Units

Automatic generation control (AGC) plays an important role to maintain real time power generation and load balance, and to ensure the quality of power supply. Power system requires each power generation unit to have satisfactory AGC performance. In addition to the design of AGC control strategy, the characteristics of power generation equipment also affect its performance in AGC control. This paper introduces the assess index of AGC performance of power unit, and analyzes the reasons for the poor AGC performance. Based on the AGC control strategy of thermal power units, the paper proposes an optimization method to improve assess index of AGC performance. Through the implementation of typical industrial case studies, the effectiveness of the proposed method is validated.

A coordinated dispatch method for energy storage power system considering wind power ramp event

In response to the impact of wind power ramp events on power system, a forecast and coordinated dispatch method for wind power ramp events is proposed. Firstly, the LSTM neural network is utilized to multi-step forecast the wind power, which can identify the features such as amplitude and duration of wind power ramp event in advance. Then, an ahead-delay adjustment method (ADAM) for wind power ramp events is proposed, which coordinates the thermal power generation units (referred to as units) and the energy storage system (referred to as ESS) to make two-step adjustments during the period before and after wind power ramp event. Thereby reducing the ramp rate of wind power, while reducing the requirement for the output power and capacity of ESS. Based on the above methods, a coordinated dispatch model of units and ESS considering wind power ramp events is established. The units are coordinated in advance to ensure sufficient adjustable reserves during wind power ramp events. A more economical and reliable dispatch plan can be achieved by coordinating the units and ESS according to the judging conditions. Finally, the forecast performance test and the ramp dispatch simulation are carried out in case study to verify the effectiveness of the forecast and dispatch method.

Multi-Footprint Constrained Energy Sector Planning

Fossil fuels have been heavily exploited since the Industrial Revolution. The resulting carbon emissions are widely regarded as being the main cause of global warming and climate change. Key mitigation technologies for reducing carbon emissions include carbon capture and storage (CCS) and renewables. According to recent analysis of the International Energy Agency, renewables and CCS will contribute more than 50% of the cumulative emissions reductions by 2050. This paper presents a new mathematical programming model for multi-footprint energy sector planning with CCS and renewables deployment. The model is generic and considers a variety of carbon capture (CC) options for the retrofit of individual thermal power generation units. For comprehensive planning, the Integrated Environmental Control Model is employed in this work to assess the performance and costs of different types of power generation units before and after CC retrofits. A case study of Taiwan’s energy sector is presented to demonstrate the use of the proposed model for complex decision-making and cost trade-offs in the deployment of CC technologies and additional low-carbon energy sources. Different scenarios are analysed, and the results are compared to identify the optimal strategy for the energy mix to satisfy the electricity demand and the various planning constraints.

Steady-State Interval Detection and Nonlinear Modeling for Automatic Generation Control Systems

Automatic generation control systems are designed to adjust electric power outputs of multiple generators simultaneously in accordance with the current load. However, the control instruction and the main steam pressure have significant impacts on the resulting active power generation of a conventional thermal generator, and the impacts may be associated with nonlinear characteristics. As a result, the control instruction requires an accurate modeling of the relationship between these three variables for a satisfactory control performance. This paper proposes a method to build a piecewise linear model for the nonlinear relationship from steady-state data hidden in historical data samples. The proposed method is composed by two main steps of steady-state interval detection and steady-state data segmentation. Historical data samples are grouped using the k-means clustering algorithm, and the time domains of each cluster are merged in a specific way to obtain the steady-state intervals. The steady-state data are taken as the samples means of data in the steady-state intervals. A bottom-up algorithm is utilized to partition the steady-state data into numbers of sets iteratively, and the parameters of the piecewise linear model for each data set are estimated by the least squares algorithm. The effectiveness of the proposed method is illustrated via industrial applications to two thermal power generation units.

Performance assessment of primary frequency control responses for thermal power generation units using system identification techniques

Primary frequency control (PFC) responses for thermal power generation units are concerned about whether changes of turbine rotating speeds lead to proper responses of generator active powers. A new method is proposed to estimate performance metrics for PFC responses via system identification techniques. Dynamic models are identified from data samples of the turbine rotating speed and the main steam pressure as two inputs, and the generator active power as the output. Hypothesis tests are formulated to evaluate the qualities of identified models. If model qualities are satisfactory and the contribution from the turbine rotating speed to the generator active power is significant, then performance metrics are estimated from a noise-free unit step response of the identified model. The proposed method removes three major limitations of the most widely-used method in contemporary industrial practices, namely, (i) performance metrics are obtained from all data samples of PFC responses, instead of some specific data samples being sensitive to noises; (ii) the proposed method is applicable to arbitrary-type PFC responses, not limited to step changes of the turbine rotating speed; (iii) noticeable effects of the main steam pressure are separated to avoid erroneous performance assessment results. Examples from thermal power generation units are provided to illustrate the effectiveness of the proposed method.

Practices of detecting and removing nuisance alarms for alarm overloading in thermal power plants

Alarm overloading refers to the most noticeable phenomenon in existing alarm systems: there are far too many alarms to be promptly handled by industrial plant operators. A large number of occurred alarms are nuisance alarms that are not associated with any actual abnormalities and are extremely detrimental to important roles of industrial alarm systems. This paper presents long-term industrial applications of three techniques on detecting and removing nuisance alarms to a thermal power generation unit. By deploying these techniques, the severity of alarm overloading phenomenon has been significantly alleviated. The average number of alarm occurrences per day has been reduced from 18,280 to 359 in the year of 2015, so that about 98% alarm occurrences have been removed without affecting true alarms.

Optimal operation of smart multi-energy hub systems incorporating energy hub coordination and demand response strategy

This paper presents the operation and optimisation of a smart multi-energy hub system network using the energy hub concept. The case study system network includes integrated solar photovoltaic and thermal power generation units and natural gas combined heat and power unit systems. A demand response-dynamic economic emission optimisation model is applied in the case study and allows for a comparison of energy hub control strategies including the evaluation of economic and environmental criteria and power import between energy hubs. The results show a significant reduction of more than 50% in both the total generation cost and amount of emission when different energy hub control strategies are employed. The results also show that load shifting capabilities of different energy hub loads cannot be ignored as they reduce the electricity bill of energy hub customers.

Optimal placement of fast cut back units based on the theory of cellular automata and agent

The thermal power generation units with the function of fast cut back could serve power for auxiliary system and keep island operation after a major blackout, so they are excellent substitute for the traditional black-start power sources. Different placement schemes for FCB units have different influence on the subsequent restoration process. Considering the locality of the emergency dispatching rules, the unpredictability of specific dispatching instructions and unexpected situations like failure of transmission line energization, a novel deduction model for network reconfiguration based on the theory of cellular automata and agent is established. Several indexes are then defined for evaluating the placement schemes for FCB units. The attribute weights determination method based on subjective and objective integration and grey relational analysis are combinatorically used to determine the optimal placement scheme for FCB unit. The effectiveness of the proposed method is validated by the test results on the New England 10-unit 39-bus power system.

Emission‐economic dispatch of thermal power generation units in the presence of hybrid electric vehicles and correlated wind power plants

With the integration of plug-in hybrid electric vehicles (PHEVs) and renewable energy resources into the power system, the environmental-economic dispatch of conventional power generation units takes new dimensions due to several ineluctable uncertain factors. This study presents a probabilistic optimisation framework for the emission, economic dispatch of thermal power generation units, considering the stochastic charging demand of PHEVs, intermittent wind power generation and uncertain load demand. To characterise the uncertainty in the output variables, the point estimate method and Nataf transformation are employed, considering the correlation between wind power plants. To find Pareto optimal solutions concerning minimisation of the emission and fuel cost, heuristic fitness sharing mechanisms and a meta-heuristic algorithm are proposed. Monte Carlo simulation together with state-of-the-art optimisation techniques (NSGA-II and SPEA-2) are employed in a quantitative performance appraisal to scrutinise the effectiveness of the proposed method in the uncertainty quantification and multi-objective optimisation.

Utilization of District Heating Networks to Provide Flexibility in CHP Production

Increasing penetration of intermittent renewable energy production in power systems will remarkably increase the need of flexible and controllable power generation. As numerous existing thermal power generation units have been closed down, all the possible flexibility available in power systems should be harnessed to stabilize the power systems. Combined heat and power (CHP) generation is widely used in district heating (DH) systems. As total heat production into the DH network needs to be balanced with the total heat consumption, this sets significant limitations to the long-term power production. However, the heat load and electric production can be decoupled temporarily by using the heat storage capacity of DH networks and heat accumulators. This paper presents an analysis of dynamic operability of interconnected CHP plants and district heating networks. The flexibility of generation capacity was compared with the requirements set when attending the Automatic Frequency Restoration Reserve (FRR-A) market. For that, two case studies were presented that include FRR-A tests in two municipal CHP plants. The results indicate that both cases fulfil the requirements and that the DH network operation is affected only slightly. However, the rapid power level changes are disturbances to CHP boilers and DH networks that the process components and automation systems must adapt to. Therefore, these aspects must be considered carefully when applying such new operation practises in existing CHP plants.

Optimization of emission/economic dispatch using euclidean affine flower pollination algorithm (eFPA) and binary FPA (BFPA) in solar photo voltaic generation

Economic dispatch is the optimal output for a number of electricity generating units, to meet the system load, at the lowest possible cost, subject to transmission and operational constraints. The different Economic Dispatch (ED) methods have been developed in order to deal with the challenge of continuous and sustainable power at an improved cost. The increase in cost depletion of non-renewable sources has forced us to use the renewable energy resource as an alternative energy source. For optimizing the Economic Dispatch problem, a new algorithm is proposed based on Combined Emission Economic Dispatch (CEED) for Photo Voltaic (PV) plants and thermal power generation units. In CEED approach for improvising the Economic Dispatch Euclidean Affine Flower Pollination Algorithm (eFPA) and Binary Flower Pollination Algorithm (BFPA) has been used for solving the optimization problem for twenty PV and five thermal generators are done with full solar radiations and with reduced solar radiation. Power demand data has been taken from India in the district of Tuticorin Thermal Power Station (TTPS). Simulation results are tested with IEEE 30 bus and IEEE 57 bus systems and the comparison has been made with existing algorithm to provide better results.

Transient Characterization of Multiple Parabolic Trough Collector Loops in a 100 MW CSP Plant for Solar Energy Harvesting

Abstract Parabolic trough solar collection technology has been widely deployed in concentrated solar power (CSP) generation plants all over the world. In a large-scale CSP plant, a solar field consists of hundreds of parabolic solar collector loops. Due to the parallel loop arrangement (Fig. 1) and non-unifoum solar collection, mal-distributions of heat-transfer-fluid flow (HTF, synthetic oil) among different loops commonly occur during transient operation. Many solar collector loops have to be pushed out of tracking phase to avoid oil overheating and degradation, which causes significant loss in solar energy collection and power generation. Such collector defocusing and refocusing operation results in large transient variation of HTF temperature, which further brings challenges to energy-efficient operation and maintenance of the coupled thermal power generation unit. Advanced transient analysis, control and fault diagnosis tools are desired to achieve safer CSP plant operation and transient solar power harvesting. In this paper, a distributed transient optical-thermal-fluid model of parabolic trough collector is developed and validated with field data from a 100MW th CSP plant. Transient model predictions are in fair agreement with experimental field data, which provide a quantitative tool for solar energy harvesting. Conceptual active flow control strategies are further introduced to regulate the HTF temperature and maintain high solar collection efficiency.

Application of Artificial Intelligence Technique to Economic Load Dispatch of Thermal Power Generation Unit

Economic Dispatch(ED) is one of the main problem of power system operation and control which determines the optimal real power settings of generating units with an objective of minimizing the total fuel cost, subjected to limits on generator real power output & transmission losses. In all practical cases, the fuel cost of generator can be represented as a quadratic function of real power generation. This paper describe and Introduce a new nature Inspired Artificial Intelligence method called Firefly Algorithm(FA). The Firefly Algorithm is a stochastic Meta heuristic approach based on the idealized behavior of the flashing characteristics of fireflies. The aim is to minimize the generating unit’s combined fuel cost having quadratic cost characteristics subjected to limits on generator real power output & transmission losses. This paper presents an application of the FA to ED with valve point loading for different Test Case system. The obtained solution quality and computation efficiency is compared to another artificial intelligence technique, called Genetic algorithm (GA) . The simulation results show that the proposed algorithm outperforms previous artificial intelligence method.

Stochastic Multiperiod OPF Model of Power Systems With HVDC-Connected Intermittent Wind Power Generation

This paper presents a new model for a stochastic multiperiod optimal power-flow (SMP-OPF) problem which includes an offshore wind farm connected to the grid by a line-commutated converter high-voltage dc link. The offshore wind farm is composed of doubly fed induction generators (DFIGs), and the DFIG's capability curve is considered in order to obtain a more realistic dispatch for wind farms. The uncertainties of wind power generation are also taken into account using a scenario-based approach which can be adopted by the system operator to obtain the optimal active and reactive power schedules for thermal and wind power generation units. To illustrate the effectiveness of the proposed approach, it is applied on the IEEE 118-bus test system. The obtained results demonstrate the capability of the proposed SMP-OPF model for the determination of the optimal operation of power systems.

Multi-objective self-scheduling of CHP (combined heat and power)-based microgrids considering demand response programs and ESSs (energy storage systems)

Today's, policy makers, governments, and academic experts in flourishing societies are interested in employing power systems considering high reliability, quality, and efficiency factors. Moreover, climatic concerns force power system appliers to utilize these systems more environmental friendly. To obtain the mentioned aims, MGs (microgrids) act as key solutions. MGs are invented not only to operate power systems more reliable and efficient but also to penetrate CHP (combined heat and power)-based DG (distributed generation) into power systems with an optimal control on their generation. This paper presents a new optimal operation of a CHP-based MG comprising ESS (energy storage system), three types of thermal power generation units, and DRPs (demand response programs). In this paper, DRPs are treated as virtual generation units along with all of realization constraints. In a multi-objective self-scheduling optimization problem of a MG, the first objective deals with minimizing total operational cost of the CHP-MG in an OPF-based formulation and the second refers to the emission minimization of DGs. The proposed model implements a simple MIP (mixed-integer programming) that can be easily integrated in the MGCC (MG central controller). The effectiveness of the proposed methodology has been investigated on a typical 24-bus MG.

Evaluating the effect of frequency regulation modes on the reliability and economic efficiency of thermal power generation units

Tests for evaluating the effect of primary and automatic secondary frequency control on the reliability and economic efficiency of thermal condensation power generator units with flow-through boilers are described briefly. The results of these tests serve as initial data for the development of a method of estimating the consequences and additional expenses involved in bringing thermal power generator units into operation in these frequency regulation modes.

Real coded genetic algorithm for stochastic hydrothermal generation scheduling

The intent of this paper is to schedule short-term hydrothermal system probabilistically considering stochastic operating cost curves for thermal power generation units and uncertainties in load demand and reservoir water inflows. Therefore, the stochastic multi-objective hydrothermal generation scheduling problem is formulated with explicit recognition of uncertainties in the system production cost coefficients and system load, which are treated as random variable. Fuzzy methodology has been exploited for solving a decision making problem involving multiplicity of objectives and selection criterion for best compromised solution. A real-coded genetic algorithm with arithmetic-average-bound-blend crossover and wavelet mutation operator is applied to solve short-term variable-head hydrothermal scheduling problem. Initial feasible solution has been obtained by implementing the random heuristic search. The search is performed within the operating generation limits. Equality constraints that satisfy the demand during each time interval are considered by introducing a slack thermal generating unit for each time interval. Whereas the equality constraint which satisfies the consumption of available water to its full extent for the whole scheduling period is considered by introducing slack hydro generating unit for a particular time interval. Operating limit violation by slack hydro and slack thermal generating unit is taken care using exterior penalty method. The effectiveness of the proposed method is demonstrated on two sample systems.

Gasification of high-ash Indian coals for power generation

Coal gasification as a route for power generation has several advantages including environmental protection and higher overall efficiency as compared to the conventional pulverised fuel system of firing. The inherent nature of Indian coals have resulted in low plant availability and wear and tear in the pulverizer and downstream sections of the thermal power generation units. Gasification of high-ash coals abundantly available in India is foreseen to be an alternate viable proposition for power generation. Fundamental and pilot plant research on gasification of these coals as conducted in moving bed, entrained bed and fluidised bed are reported and the results of tests carried out with coals of varying ash content have been briefly summarised in this paper. The tests indicate the operability of the gasification system with Indian coals. Loss in efficiency with increased oxygen consumption was observed with increased ash content of coals. Use of beneficiated coal with ash content around 25% has been envisaged to improve the overall power generation efficiency and possible beneficiation scheme of high-ash coals prior to gasification have been proposed. Further research and development activities in India on gasification of high-ash coals have also been emphasised for adopting clean coal technology programmes in India.