Planning Of Power Plants Research Articles

Energy and Environment Implications of Long-Term Power Development Involving Renewable Energy: A Case of Timor Island, Indonesia

This paper presents a long-term electricity energy supply-demand model of Timor Island, Indonesia. Implications of projected demand growth within the observed area towards the available supply as well as the amount of CO2 emission is taken into account. As its main objective is to review and to present initial comparison of the long-term electricity planning prepared by the utility, the analysis is carried out using the bottom-up energy system model. Unlike the common electricity long-term demand projection that is usually constructed based on the factors related to the electricity growth, the model is developed based on the simple projection considering historical electricity demand users. According to the analysis, the planned power plants would not able to meet the elecricity demand in the case of high growth demand scenario. The variation of CO2 emission that is obtained from the considered scenarios is also shown.

Sustainable Power Plant Planning Using Pinch Analysis Approach

Growing economic and population expansion has posed great stress in power plant planning as energy consumption is increased every year. Power plant expansion or new power plant construction require years of planning before implementation. Together with the aid of energy storage (ES) for improving power plant capacity factor, this work applied Electric System Cascade Analysis (ESCA) to identify the total power plants that are needed to fulfil energy demand in an on-grid power system. A hypothetical Case Study has created to consider the factors of ES charging and discharging efficiency, increment of energy demand, integration of existing with new power plant. The results obtained by ESCA are 929.22 MW of total power plants capacity, 1,073.44 MW of ES real capacity, and 2,614.39 MWh of ES energy capacity, as well as 298.98 MWh of initial content in the ES.

Game Model of Thermal Power Capacity Planning Considering Environmental Constraint

Rational planning of thermal power capacity is one of the main measures to reduce emissions. According to air pollution control plan, different environmental capacity regions provide different constraints to the scale of thermal power plants. With the development of electric market, unified relationship among thermal power plants has been changed to gambling one. To solve the above new problems emerged in the area of thermal power capacity planning, this paper pre- sents a game model for thermal power capacity planning considering the constraint of environmental capacity. In this game-theory-based model, the thermal environmental adaptability factor, that reflecting the adaptability of environmental capacity to thermal power in different regions, is brought into the traditional profit function. Consequently, proper thermal power planning capacity adapting to environmental capacity could be obtained to provide support and basis for govern- ment regulation and enterprise decision. The effectiveness of the proposed model is validated by a series test results of different scale power planning examples. based on its own actions and its competitors. The traditional thermal power planning model taking all power plants as a whole to optimize is no longer valid today, while game theo- ry provides a method to solve the above problems (10, 11). The game model of thermal power planning can reflect in- fluence and restriction among all the thermal power plants. To solve the above problems, this paper starts with estab- lishment of a game model of thermal power plants without environmental constraint, indicating an inevitable trend of excessive expansion of the scale of thermal power plant planning capacity. Then according to the Nash equilibrium solution of the model, environmental adaptability scale fac- tor for thermal power plant is proposed to prevent excessive expansion. Finally a game model for thermal power plants considering constraint of environmental capacity is set up to ensure the adaptability between thermal power planning ca- pacity and restriction of environment. Test results of differ- ent scale power planning examples show the effectiveness of this new proposed model.

Game Model of Thermal Power Capacity Planning Considering Environmental Constraint

Rational planning of thermal power capacity is one of the main measures to reduce emissions. According to air pollution control plan, different environmental capacity regions provide different constraints to the scale of thermal power plants. With the development of electric market, unified relationship among thermal power plants has been changed to gambling one. To solve the above new problems emerged in the area of thermal power capacity planning, this paper pre- sents a game model for thermal power capacity planning considering the constraint of environmental capacity. In this game-theory-based model, the thermal environmental adaptability factor, that reflecting the adaptability of environmental capacity to thermal power in different regions, is brought into the traditional profit function. Consequently, proper thermal power planning capacity adapting to environmental capacity could be obtained to provide support and basis for govern- ment regulation and enterprise decision. The effectiveness of the proposed model is validated by a series test results of different scale power planning examples. based on its own actions and its competitors. The traditional thermal power planning model taking all power plants as a whole to optimize is no longer valid today, while game theo- ry provides a method to solve the above problems (10, 11). The game model of thermal power planning can reflect in- fluence and restriction among all the thermal power plants. To solve the above problems, this paper starts with estab- lishment of a game model of thermal power plants without environmental constraint, indicating an inevitable trend of excessive expansion of the scale of thermal power plant planning capacity. Then according to the Nash equilibrium solution of the model, environmental adaptability scale fac- tor for thermal power plant is proposed to prevent excessive expansion. Finally a game model for thermal power plants considering constraint of environmental capacity is set up to ensure the adaptability between thermal power planning ca- pacity and restriction of environment. Test results of differ- ent scale power planning examples show the effectiveness of this new proposed model.

Eco-efficiency change in power plants: using a slacks-based measure for the meta-frontier Malmquist–Luenberger productivity index

Malmquist–Luenberger index is a popular index deployed for evaluating eco-efficiency and productivity change over a certain period when both desirable and undesirable factors are jointly produced in some production processes. However, conventional data envelopment analysis (DEA) models used for computing this index are unable to distinguish the various technologies employed by the decision making units. When heterogeneity exists in the technology, the meta-frontier Malmquist–Luenberger approach is adopted to evaluate eco-efficiency and productivity change over a period when undesirable outputs are incorporated in the analysis of efficiency. In this paper, a new slacks-based DEA measure is developed and used to compute the meta-frontier Malmquist–Luenberger productivity index. This approach is then deployed to evaluate 48 Iranian thermal power plants productivity change in three different categories – steam, gas, and combined cycle – over an eight year period of restructuring in the power industry. This technique enables the calculation of eco-efficiency change of thermal power plants operating under different technologies as well as the technology gap ratios and measures the extent to which the efficiency frontiers of individual categories of power plants deviate from the meta-frontier. The results reveal that the last years of the restructuring period saw a greater rate of productivity growth compared to the initial years; and evidence of significant eco-efficiency improvements over the period in all the three types of the thermal power plants. Finally, it is expected that the findings in this study aid policy makers and power industry regulators in developing sustainable energy policies towards environmental protection and planning power plant operations.

Operational planning of combined heat and power plants through genetic algorithms for mixed 0–1 nonlinear programming

This paper is concerned with short-term (up to 24h) operational planning in combined heat and power plants for district energy applications. In such applications, heat and power demands fluctuate on an hourly basis due to changing weather conditions, time-of-day factors and consumer requirements. Plant energy efficiency is highly dependent on ambient temperature and operating load since equipment efficiencies are nonlinear functions of these parameters. In operational planning strategies, nonlinear equipment characteristics are seldom taken into account, resulting in plants being operated at sub-par efficiencies. In order to operate plants at highest possible efficiencies, scheduling strategies which take into account nonlinear equipment characteristics need to be developed. For such strategies, a mixed 0–1 nonlinear programming formulation is proposed. The problem is nonconvex and hence global optimality conditions are unknown. Classical techniques like branch-and-bound may not produce integer feasible solutions, may cut off the global optima and have an exponential increase in CPU time for a linear increase in planning horizon size. As an alternative, a solution method through genetic algorithms is proposed in which genetic search is applied only on 0–1 variables and gradient search is applied on continuous variables. The proposed method is a nonlinear extension of the one originally developed by Sakawa et al. [Sakawa M, Kato K, Ushiro S. Operational planning of district heating and cooling plants through genetic algorithms for mixed 0–1 linear programming. Eur J Operat Res 2002;137(3):677–87]. Numerical experiments show the proposed genetic algorithm method is more consistent in finding integer feasible solutions, finds solutions with lower optimality gaps and has reasonable CPU time as compared to branch-and-bound. From an application perspective, the proposed scheduling strategy results in 5–11% increase in plant energy efficiency.

Planning of carbon capture storage deployment using process graph approach

Carbon capture and storage (CCS) is an emerging technology to mitigate carbon dioxide (CO2) emissions from industrial sources such as power plants. However, retrofitting a power plant for carbon capture causes an increase in unit power cost due to parasitic power losses as well as capital outlays for additional process equipment. Mathematical optimisation and pinch analysis techniques have been used to systematically plan for the retrofit of power plants. In this work, the planning of power plants retrofit along with CO2 source-sink matching is analysed using process graph (P-graph) optimisation technique. CO2 sources are assumed to be characterised by fixed flowrates and operating lives; while CO2 sinks are characterised by storage capacity limits and earliest time of availability. Illustrative case studies are solved to demonstrate the approach.

Impacts of, and adaptation options to, extreme weather events and climate change concerning thermal power plants

Extreme weather events and changed climate parameters have impacts on power plants and their connected infrastructures. Therefore, adaptation, especially in the context of a changing climate and a resulting shift in the intensity and frequency of extreme events, is necessary. Thermal power plants are subject to a diversity of extreme weather impacts, making them vulnerable if not adapted. In this paper, the impacts of extreme weather events on thermal power plants are first identified and structured. Then selected adaptation options for thermal power plants are presented. Three major types of adaptation option are identified: adaptation of cooling, infrastructure, and sites. The Supplementary Material introduces a GIS-based (Geographic Information System) decision-support tool for power plant adaptation and planning.

Accommodation Capacity Analysis of the Large Scale PV Power Generation Access to Regional Power Grid

In this paper, the factors are analyzed,which affect the accommodation capacity of large scale PV power generation access to the grid . The analysis method mainly consider the peak load adjustment ability of the whole power system. Then a real case based on provincial grid is calculated and analyzed. The results show that: As an important technical style ,the generation and acommodation of the large scale photovoltaic power generation are influenced by the light resource, load level, adjustment ability, transmission channel. The planning of large-scale photovoltaic power plants must keep pace with the conventional power and grid planning.

Optimal CO<sub>2</sub> Reduction Investment of Power Plants under Uncertainties

According to the current situation of domestic carbon dioxide (CO2) reduction and the abatement planning of power plants, the decision-making model for optimal CO2 abatement investment in power plants has been built concerning uncertainties on CO2 policies and investment return rate. Under the framework, a case study on power plant was introduced. It is found that (i) higher permits price may stimulate power plants to increase abatement investment under certain return rate, (ii) whether power plants choose to invest more or not depends on the value of returns at fixed carbon price, and (iii) investment and its returns will change in the same directions with the investment return rate. It is suggested that for the mitigation goals to be met in a more efficient way, more stringent CO2 control measures and policies to lower investment costs should be taken into account.

Multi-Objective Model Research with Clean Energy Technologies in Low Carbon Power Planning

With the increasing development of clean energy technologies, the application of new energy and carbon capture technology would have a positive effect for CO2 emissions reduction. In this paper, planning of power system model including clean energy technology facing low carbon targets established, considering the carbon emission rights allocation and carbon trading. By studying coal-fired power plant planning model with wind power and carbon capture technology for the future planning period, establish the multi-objective model of minimum comprehensive cost and biggest carbon trading gains, considering constraint conditions of power generating capacity, wind power integrated capacity and carbon emissions reduction targets. Use the bacterial colony chemotaxis algorithm for optimization calculation of the power planning, to give a planning scheme that can meet the requirements of economic development trend and emission reducing requirements.

Analysis of the sulfur content in the Gubin lignite deposit for assessing the need for sorbent and the quantity of REA gypsum produced

The paper refers to the problem of complex management of mineral resources, especially to the long-term lignite deposit management. Based on data from the geological documentation of the Gubin deposit, this article analyzes the variability of total sulfur content in lignite seams planned for exploitation. The purpose of this analyzes is the assessment of the amount of sorbent required for desulfurization process and the quantity of byproduct of this process. For modeling of the deposit’s parameters, geostatistical methods were used including ordinary and universal kriging. Given the mining design, the total amount of sulfur in the lignite was determined within the proposed five-year advances of the exploitation fronts. The analysis took into account the planned, simultaneous mining of two lignite seams. For the purposes of this article, it was assumed that in the planned power plant by the Gubin deposit wet limestone scrubbing method will be used. Using the equation for the reaction that takes place in the flue gas desulfurization process, the mass of sorbent (CaCO3) was calculated which will be needed for total gas desulfurization, as well as themass of the gypsum which will be the product of this process. The lower lignite seam contains increased amount of the sulfur. Mining of this seam will cause significant increase of the demand on the desulfurization sorbent and, consequently, the higher supply of REA gypsum. The results of the analysis may be useful to determine the amount of sorbent required in the long term and for the development of a desulphurization byproducts management plan at the power plant located by the Gubin deposit. They can also be used for evaluating the impact of synthetic gypsum supply on the market for this commodity in the country.

Electricity Options and Additional Information

Electricity markets feature a non-storable underlying, which implies the break down of traditional cash-and-carry arguments as well as the well-known spot-forward relationship. We introduce the notion of information premium to describe the influence of future information—such as planned power plant maintenance—on the relationship between forward contracts and the spot market. In a recent paper we designed a statistical test to show the existence of the premia. Here, we examine how the presence of an information premium alters the prices of options on forwards. Also, we apply the technique of enlargement of filtrations to show how to calculate the premium specifically for certain types of information and delivery periods. Furthermore, we illustrate the results in various stylised examples.

A multi-model approach for long-term runoff modeling using rainfall forecasts

This work presents the development of a rainfall–runoff model for the Iguaçu River basin in the south of Brazil. The model was developed to support the operational planning of hydroelectric power plants and is intended to compute natural flow predictions based on meteorological rain forecasts. A recurrent fuzzy system model was employed, with parameters estimated by a genetic algorithm using observed rainfall as input. This work presents the recurrent fuzzy model within a multi-model approach, where the input data are furnished as an envelope, resulting in a prediction envelope that has demonstrated the ability to produce robust results.

The promise of carbon capture and storage: evaluating the capture-readiness of new EU fossil fuel power plants

To what degree are recently built and planned power plants in the EU ‘capture-ready’ for carbon capture and storage (CCS)? Survey results show that most recently built fossil fuel power plants have not been designed as capture-ready. For 20 planned coal-fired plants, 13 were said to be capture-ready (65%). For 31 planned gas-fired power plants, only 2 were indicated to be capture-ready (6%). Recently built or planned power plants are expected to cover a large share of fossil fuel capacity by 2030 and thereby have a large impact on the possibility to implement CCS after 2020. It is estimated that around 15–30% of fossil fuel capacity by 2030 can be capture-ready or have CO2 capture implemented from the start. If CCS is implemented at these plants, 14–28% of baseline CO2 emissions from fossil fuel power generation in 2030 could be mitigated, equivalent to 220–410 MtCO2. A key reason indicated by utilities for building a capture-ready plant is (expected) national or EU policies. In addition, financial incentives and expected high CO2 prices are important. The implementation of a long-term regulatory framework for CCS with clear definitions of ‘capture- readiness’ and policy requirements will be important challenges.

Locating new coal-fired power plants with Carbon Capture Ready design–A GIS case study of Guangdong province in China

Abstract Making new coal-fired power plants carbon capture ready (Carbon Capture Ready) in China has been recognised as a crucial by a number of stakeholders academics, energy companies and regional government, based on a study in EU-UK-China NZEC project. A number of publications have investigated the definition, engineering requirements, economic and finance of CCR for China. However there remain a number of questions regarding the extent to which a plant’s physical location might constrain the feasibility of CCS retrofit. To address this issue, a Geographical Information System (GIS) has been used as a tool for mapping current and planned large carbon dioxide sources in Guangdong, also illustrating potential storage sites and calculating possible carbon dioxide transportation route. This paper investigates the location factors that should be considered when locating new build CCR power plants and demonstrates the methodology of using GIS software with spatial analysis in planning new build power plant in Guangdong. A preliminary study has identified over 30 large power plants within the region, with plant locations and historical emission data collected and presented in ArcGIS. Factors such as distance to potential storage site, route of CO 2 pipeline, extra space on site and potential development plan etc. were investigated in the modelling and calculated the potential source and sink solution. The study then moves on to suggest possible new build plant locations which can be easily fitted in to the current network, based on economic optimisation. The scope for future coal plant development combined with a possible nuclear plant siting plan is discussed towards the end of the paper. Guangdong province, which owns the third largest coal-fired power installed capacity out of 31 provinces, generated over 8% of China’s total electricity every year for the past 15 years. CO 2 storage opportunities could be found in the surrounding South China Sea, where Guangdong has a total of 4,300 km of coastline and some small scale oil fields on shore within the region. It is also among the first places to start the national open and reform policy in China. The province is one of the richest in China, with the highest GDP among all other provinces since 1989, and the foreign trade accounts for more than a quarter of China’s total amount. It also contributes around 12 of the total national economic output. Currently, the provincial government is proposing a low carbon roadmap, which is the first of its kind in China. The work has created a totally new thinking on capture ready power plant planning. This differs from existing studies (e.g., which aim to investigate the existing carbon dioxide emission sources at specified location and provide source and sink matching analysis. Instead the study focuses on policy implementation for new build capture ready power plants. Three clusters within Guangdong province are identified as potential temporary CO 2 storage hubs before transporting the gas to a long term storage site. When officials are planning new power plant locations from a capture ready perspective, the plants should not necessarily be close to storage sites in straight line, but rather should be within a reasonable distance of a cluster. Transport of the captured CO 2 will not be limited to pipelines, but could be extended to road and rail tankers. Power plant parameters and storage site data were collected for this research. Public transportation, utilities, landscapes, river, land used and population data were referenced from various sources; therefore, some of the data could be out of date. Nevertheless, it should still provide enough information when deciding the location of the transport cluster. Any future work could build on the existing model with updated data. Moreover, it could fit in with the national natural gas transportation network and utility planning network to provide long term integrated energy system analysis. The paper could provide policy makers, investors and urban planning officials with a view on how conventional thermal power plant investment and planning could be optimised, using Carbon Capture Ready designs, to keep the CCS retrofitting option open.

Long-Term Runoff Modeling Using Rainfall Forecasts with Application to the Iguaçu River Basin

This work presents the development of a rainfall-runoff model for the Iguacu River basin in southern Brazil. The model was developed to support the operation planning of hydroelectric power plants and is intended to predict the natural flow based on meteorological rain forecasts. A recurrent fuzzy system model was employed with parameters estimated by a genetic algorithm using observed rainfall as input. The model performs well using observed rainfall as input; however, its performance using predicted rainfall as input decays with the forecasting horizon, illustrating the effect of meteorological prediction errors. The prototype implementing the model has been used for dispatch planning by the Brazilian Electric System Operator.

Importance of Auxiliary Power Consumption for Combined Cycle Performance

The key product of a combined cycle power plant is electric power generated for industrial, commercial, and residential customers. In that sense, the key performance metric that establishes the pecking order among thousands of existing, new, old, and planned power plants is the thermal efficiency. This is a ratio of net electric power generated by the plant to its rate of fuel consumption in the gas turbine combustors and, if applicable, heat recovery boiler duct burners. The term in the numerator of that simple ratio is subject to myriad ambiguities and/or misunderstandings resulting primarily from the lack of a standardized definition agreed upon by all major players. More precisely, it is the lack of a standardized definition of the plant auxiliary power consumption (or load) that must be subtracted from the generator output of all turbines in the plant, which then determines the net contribution of that power plant to the electric grid. For a combined cycle power plant, the key contributor to the plant’s auxiliary power load is the heat rejection system. In particular, any statement of combined cycle power plant thermal efficiency that does not specify the steam turbine exhaust pressure and the exhaust steam cooling system to achieve that pressure at the site ambient and loading conditions is subject to conjecture. Furthermore, for an assessment of the realism associated with the two in terms of economic and mechanical design feasibility, it is necessary to know the steam turbine exhaust end size and configuration. Using fundamental design principles, this paper provides a precise definition of the plant auxiliary load and quantifies its ramification on the plant’s net thermal efficiency. In addition, four standard auxiliary load levels are quantitatively defined based on a rigorous study of heat rejection system design considerations with a second-law perspective.

Global Shallow—Water Bathymetry From Satellite Ocean Color Data

Knowledge of ocean bathymetry is important, not only for navigation but also for scientific studies of the ocean's volume, ecology, and circulation, all of which are related to Earth's climate. In coastal regions, moreover, detailed bathymetric maps are critical for storm surge modeling, marine power plant planning, understanding of ecosystem connectivity, coastal management, and change analyses. Because ocean areas are enormously large and ship surveys have limited coverage, adequate bathymetric data are still lacking throughout the global ocean.

Fuel management optimization based on power profile by Cellular Automata

Fuel management in PWR nuclear reactors is comprised of a collection of principles and practices required for the planning, scheduling, refueling, and safe operation of nuclear power plants to minimize the total plant and system energy costs to the extent possible. Despite remarkable advancements in optimization procedures, inherent complexities in nuclear reactor structure and strong inter-dependency among the fundamental parameters of the core make it necessary to evaluate the most efficient arrangement of the core. Several patterns have been presented so far to determine the best configuration of fuels in the reactor core by emphasis on minimizing the local power peaking factor ( P q ). In this research, a new strategy for optimizing the fuel arrangements in a VVER-1000 reactor core is developed while lowering the P q is considered as the main target. For this purpose, a Fuel Quality Factor, Z( r), served to depict the reactor core pattern. Mapping to ideal pattern is tracked over the optimization procedure in which the ideal pattern is prepared with considering the Z( r) constraints and their effects on flux and P q uniformity. For finding the best configuration corresponding to the desired pattern, Cellular Automata (CA) is applied as a powerful and reliable tool on optimization procedure. To obtain the Z( r) constraints, the MCNP code was used and core calculations were performed by WIMS and CITATION codes. The results are compared with the predictions of a Neural Network as a smart optimization method, and the Final Safety Analysis Report (FSAR) as a reference proposed by the designer.