Development of a decision support system for the operation of thermal ...

Development of a decision support system for the operation of thermal power plants in Western Macedonia

1

2

2

Athanasios Sfetsos , Alexandros Syrakos , Maurice Politis , George 2 1 1 Efthimiou , Nikolaos Gounaris , Diamanto Vlachogiannis , Nikolaos 2 2 3 3 Koziakis , John G. Bartzis , Georgia Nikolaou and Markos Voutsinas 1

Environmental Research Laboratory, INT-RP, NCSR Demokritos, Ag. Paraskevi, Greece

2

Environmental Technology Laboratory, Department of Engineering and Management of Energy Resources, University of Western Macedonia, Kozani, Greece

3

Generation Environment Department, Public Power Corporation S.A.

Abstract The paper describes the DSS jointly developed by ETL/UoWM, EREL/NCSRD and PPC’s Department of Environment / Production. It is of an innovative environmental management system which aims to : (i) continuously monitor and predict air quality in the operating region of the power plants and (ii) support the decisions which are relative to the dispatching strategies of the power plants, in order to limit as far as possible the exceedance of the permissible levels of atmospheric pollution.

1. Introduction Environmental information is the base of all decision support systems currently employed by different stakeholders or end-users (such as policymakers, local authorities, industry, researchers and non-government organizations) to conduct simple screening studies and/or detailed modelling I.N. Athanasiadis et al., Information Technologies in Environmental Engineering, Environmental Science and Engineering, DOI 10.1007/978-3-540-88351-7_11, © Springer-Verlag Berlin Heidelberg 2009

150

A. Sfetsos et al.

assessments of the effects of pollution in the urban environment (Fine, Ambrosiano, 1996). Integrated resource management, environmental policies and decision making need a composite management of the atmosphere, the urban environment and industrial sources considering the interconnected nature of these systems together with relationships to the social and economic systems. Environmental decision support systems (e.g. Hou et al 1998; Zhou et al, 2006; Poch et al., 2004; Hepting, 2007) also require relational databases which incorporate of interdisciplinary expert knowledge. To assess current and future air quality conditions, environmental managing authorities use a variety of modeling tools to monitor and evaluate existing demands and to predict their evolution. These generally encompass air quality monitoring information, emission inventories, air quality modeling, air quality mapping and air quality impact assessment of various control strategies in support of evaluation of action plans by using information to the public about past and present air quality levels. Examples of decision support systems used in major European cities are IMPAQT (Integrated Modular Program for Air Quality Tools; Lim, 2005), the Austrian AirWare (Fedra, 1999), the Norwegian AirQUIS (Bohler, 2002), the Swedish EnviMan (Tarodo, 2003). The Finnish AQM system is based on integrated dispersion (Karppinen et al, 2001) and exposure modelling system (Kousa et al, 2002) allowing for estimation of the spatial and temporal distributions of concentrations and resulting population exposures. The paper describes the Decision Support System jointly developed by ETL/UoWM, EREL/NCSRD and PPC’s Department of Environment / Production. It is of an innovative environmental management system which aims to : (i) continuously monitor and predict air quality in the operating region of the power plants and (ii) support the decisions which are relative to the dispatching strategies of the power plants, in order to limit as far as possible the exceedance of the permissible levels of atmospheric pollution. The greater lignite basin of Bitola – Florina – Ptolemais – Kozani is the main production area of the electric power of Greece and the FYROM. This is due to the abundance of lignite reserves in the region. The Greek Public Power Corporation (GPPC) operates 5 lignite power plants with a total power exceeding 4 GW, while a power plant near Bitola is the major lignite centre for the FYROM (675 MW), which covers 70% of the country’s energy needs. The basin is located in the northwest of Greece, with a mean elevation of 650 m above mean sea level (MSL), surrounded by mountains whose peaks exceed 2000 m MSL. The main axis of the basin is oriented from northwest to southeast. More than 150,000 people live within the region of the basin, with the main towns being Kozani and Ptolemaida. PM10 concentrations are the main concern in the region. The main PM sources are

Development of a DSS for the operation of thermal power plants

151

considered to be the power plant stacks, and the open-pit lignite mines (mining, transportation of soil and coal, movement of vehicles on unpaved roads). The air quality at certain locations within the basin (Triantafyllou, 2003) is characterized by exceedances of the TSP limits under certain meteorological conditions. Accumulation of emissions from sources in the vicinity (traffic emissions, fugitive dust, biogenic emissions) has also been reported in addition to long range transport of pollutants from the power plants of the neighbouring country FYROM. Dust emissions seem to be the most serious problem in the area, as the measured ambient concentrations of suspended particles are at high levels and exceed local and international standards. A decreasing trend of TSP concentrations is reported (Triantafyllou, 2000). Furthermore, Samara (2005) analysed the chemical composition of particles in the region and designated traffic, fly ash and domestic burning as equally important pollution sources (Samara, 2005).

2. DSS core system The DSS (Figure 1) will be based in a computational system, which utilizes elements from different databases, stationary and dynamic information from measuring stations and pre-diction models. Using this information, the system will proceed in the continuous monitoring and recording of the air quality in combination with the elements from the prediction models. In case of exceedance of the permissible levels or prediction for exceedance in unfavourable meteorological conditions, a series of alternative scenarios will be activated. These processes will include data for all PPC’ s activities in the interesting region (generation stations, mines etc). The main characteristics of the system include: • A detailed emissions inventory for the region which is dynamically updated depending on the prevailing meteorological conditions. The inventory has been extensively tested and validated against targeted measurement campaigns • A comprehensive and online database with hourly data from the PPC and UoWM monitoring stations spread throughout the region • A continuously updated database with data from the thermal plants stacks concerning emissions measurements. • A probabilistic model that gives the probability of exceeding the regulatory 3 threshold of 50 µg/m on a daily basis for each of the monitoring stations depending on the forecasting of the future days meteorological conditions • A computational model based on the PSU/NCAR model MM5, set up to provide a meteorological prediction and the SMOKE / CMAQ models

152

A. Sfetsos et al.

which make a 72-hour air quality prediction. • Forward trajectories forecasts from the main power plants and backward trajectories from the main cities as indicators of plume movement • A series of predefined scenarios, which demonstrate the relative impact of selected pollution sources, and user defined scenarios that aid in the more environmentally friendly management of the power plants in the area of interest. Currently the development of the integrated system is finished, and is under intense validation from the end users.

Fig. 1. DSS core system design

2.1 Geographical Information The basis of the environmental information management system is a series of spatial databases on the highest possible resolution. They present an extended portrait of the environmental situation and the primary economic functions of the region on the Local Administrative Unit level 2 (LAU2; perishes) and are presumed to be time invariant to a large extend. The compiled databases contain land uses which were obtained from the European land cover datasets PELCOM . The available information could be of potential use not only to air quality modellers but also regional authorities for planning purposes to secure the most efficient and effective use of land in the public interest (Culshaw et al 2006).

Development of a DSS for the operation of thermal power plants

153

Fig. 2. Land Uses in Western Macedonia

The environmental information system contains a detailed registry of the industries and enterprises that exist and operate in the region of Western Macedonia. The available data were obtained from the Greek Statistical Office, the Prefects Department of Industry, Commerce Chamber in each Prefect and field research. The data are available on a LAU2 level. Since traffic is one of the main polluting sources, special emphasis was placed on the accurate determination and registration of the existing road network. The main highway of the area is the Egnatia motorway that perpendicularly crosses the region from the West to the East. Further classifications includes the old national network and secondary network, smaller forest and farming roads in addition to urban network. Finally, significant impact from traffic activities is located on the private road networks within the mining areas. The database contains a spatial data set with the main agricultural activities. It is the product of the compilation of an extensive dataset that contains cultivated products on a LAU2 level. The original data were obtained from the Annual Vegetation Statistics of the Greek Statistical Office enhanced with the use of the GLC2000 Vegetation Map of Europe. 2.2 Emissions Inventory The spatial resolution of the inventory is 2x2 km2 and covers in total four NUTS areas (of level III) while the temporal analysis is of one hour. The inventories are characterised by seasonal variation and include the following air pollutants: NOx, CO, SO2, PM and VOC. The validated IM-

154

A. Sfetsos et al.

PRESSAREO top-down methodology has been applied to generate emission inventories from sources such as industry, traffic and domestic heating. On the other hand, data from PPC has been used for the electricity generating units. A detailed description of the emission inventory is presented in the work of Vlachogiannis et al (2007).

Fig. 3. Annual NOx and CO emissions

2.3 Modelling System 2.3.1 MM5 – SMOKE – CMAQ

For the prediction part of the system, the PSU/NCAR model MM5 (Grell et al, 1994) is set up to provide a meteorological prediction to the UNC/USEPA model CMAQ (Byun and Ching, 1999) which makes a 72hour air quality prediction. The pollutant sources are provided to CMAQ by the UNC SMOKE model. The MM5 (version 3.7.4) has been set up to use 4 nested domains. Their horizontal dimensions are respectively, from coarser to finer, 39×39, 36×36, 54×54 and 72x72 cells, and the respective spatial resolutions are 54 km, 18 km, 6 km and 2 km. All domains consist of 30 vertical layers. As for the physical parameterization, the following schemes are used: • MRF PBL scheme. • Grell cumulus parameterization on all domains except the finest, where no cumulus parameterization is used. • “Simple ice” for the explicit moisture scheme. • RRTM radiation scheme. The boundary conditions are obtained from the output of the GFS model stored in the daily global repository of the National Center for Environmental Prediction (NCEP), USA.

Development of a DSS for the operation of thermal power plants

155

Fig. 4. Weather forecast for Kozani and wind vector forecast

Fig. 5. Contour of daily forecasts for all pollutants and detail for the city of Kozani

156

A. Sfetsos et al.

CMAQ has set up to use only one domain, the finest one of the MM5 setup. The grid spacing is the same as for MM5 (2 × 2 km) and the same vertical structure of 30 layers is retained. The power plant stacks emissions are spread among several layers, according to the plume rise prediction performed by SMOKE which uses the meteorological prediction provided by MM5. All other emissions are assumed to occur within the grid cells which are adjacent to the ground (i.e. within layer 1), which is about 35 m tall. The CMAQ setup includes using CB-IV as the chemical mechanism, and the “aero3” aerosol module. 2.3.2 Model validation

The validation study of the MM5-SMOKE-CMAQ modeling system has been conducted using representative days of the prevailing meteorological conditions (Syrakos et. al, 2008). The model results are validated against the PPC monitoring network and the meteorological and air quality monitoring station of UoWM. The table below summarizes the comparison of the wind field. Table 1. Statistical measures for the meteorological predictions of each day.. Cat.

Wind Speed (ms-1)

Date mo

Wind Direction (deg)

mp

1

28-07-2007

2.06

mean RMSE bias 1.85 0.21 1.35

mo

2

04-05-2007

1.88

2.39

-0.52

1.68

123.7

103.3

20.4

76.4

3

29-05-2007

3.29

4.79

-1.51

3.04

233.1

253.9

-20.8

71.9

4

15-09-2007

3.13

3.24

-0.11

2.15

335.2

333.9

1.3

70.8

5

22-05-2007

2.71

3.53

-0.82

2.44

334.2

345.5

-11.3

80.6

6

21-09-2007

3.83

2.66

1.18

2.49

337.7

337.5

0.2

58.4

7

17-05-2007

2.67

2.78

-0.11

1.73

240.8

272.6

-31.8

72.4

9

13-05-2007

2.17

2.20

-0.03

1.75

73.1

32.7

40.4

84.9

10

26-06-2007

2.98

3.30

-0.32

1.95

261.7

266.6

-4.9

61.5

11

12-04-2007

2.07

3.12

-1.05

1.71

321.3

342.2

-20.9

89.23

85.4

mp

mean RMSE bias 91.8 -6.4 64.5

Figure 6 shows mean PM10 concentration predictions for four of the selected dates: 12/04 when weak north winds are recorded in the basin, 04/05 when weak SW winds are recorded, and 15/09 and 21/09 when strong NE winds are observed in the basin in both cases.

Development of a DSS for the operation of thermal power plants

157

Fig 6a. Observed and predicted daily average PM10 concentrations at each station

12-04-2007

04-05-2007

15-09-2007

21-09-2007

Fig. 6b: Mean daily PM10. Locations of the power plants are marked with red x.

158

A. Sfetsos et al.

2.3.3 Trajectory

The system also includes a trajectory forecast module that runs both in forward and backward modes. The Lagrangian-based HYSPLIT (HYbrid Single-Particle Lagrangian Integrated Trajectory) model (Draxler and Hess, 1997), has been employed using data from the MM5 meteorological forecasts. The forward run accepts as starting point the PPC stacks, where as the backwards is fed with the coordinates of the main cities of the area as receptor points.

Fig. 7. Forward and backward trajectories forecast 2.3.4 Scenario analysis

The DSS system is additionally containing an scenario analysis tool that is capable of (a) dealing with a set of predefined scenarios and (b) allows the user to examine the impact on the air quality of the region with userdefined emissions from the power plants. The set of predefined scenarios is based on switching on/off main emission sources, thus making a direct estimate on the relative contribution of the respective source’s impact on the air quality of the region in question. The scenarios are involving the PPC operations in the region (power plants and mining activities) collectively

3. Conclusions The paper described the DSS jointly developed by ETL/UoWM, EREL/NCSRD and PPC’s Department of Environment / Production. It is of an innovative environmental management system which aims to : (i) continuously monitor and predict air quality in the operating region of the power plants and (ii) support the decisions which are relative to the dispatching strategies of the power plants, in order to limit as far as possible the exceedance of the permissible levels of atmospheric pollution.

Development of a DSS for the operation of thermal power plants

159

Fig. 8. Air quality forecast and scenario impact on the city of Ptolemaida, under scenario 1 (all power producing plants are off)

Fig. 9. Air quality forecast contours under scenario 1 (all power producing plants are off)

160

A. Sfetsos et al.

Acknowledgements This work is partially funded by the Generation Environment Department, Public Power Corporation S.A. and by GSRT under the Regional Innovation Pole of Western Macedonia.

References Bohler, T., Karatzas, K., Peinel, G., Rose, T., San Jose, R., (2002). Providing multi-modal access to environmental data–customizable information services for disseminating urban air quality information in APNEE. Computers, Environment and Urban Systems 26, 39–61. Byun, D.W., Ching, J.K.S. (1999). Science algorithms of the EPA models3 community multiscale air quality (CMAQ) modeling system. EPA/600/R-99/030 Culshaw, M.G., Nathanail, C.P., Leeks, G.J.L., Alker, S., Bridge, D., Duffy, T., Fowler, D., Packman, J.C., Swetnam, R., Wadsworth, R., Wyatt, B. (2006). The role of web-based environmental information in urban planning—the environmental information system for planners, Science of the Total Environment 360, 233– 245 Draxler, R.R., Hess GD (1997) Description of the HYSPLIT_4 modeling system for trajectories, dispersion and deposition. NOAA Tech. Memo., ERL ARL224, 24 pp. , NTIS PB98-116593 Grell, G. A., Dudhia, J., Stauffer, D.R., (1994) A Description of the FifthGeneration Penn State/NCAR Mesoscale Model (MM5), NCAR Technical Note, NCAR/TN- 398+STR. Hepting, D.H. (2007). Decision support for local environmental impact assessment, Environmental Modelling & Software 22, 436-441. Hou, J., Jia, H., Cheng, S. (1998) A Developing Research of Environmental Decision Making Support System Under the Support of Information Techniques. Advances in Environmental Science, 6 (6), 42-47. Fedra, K., Haurie, A., (1999). A decision support system for air quality management combining GIS and optimization techniques, International Journal of Environment and Pollution, 12, 125–146. Fine, S.S., Ambrosiano, J. (1996) The environmental decision support system: Overview and air quality application, Environmental Applications, 152-157. Karppinen, A, Kukkonen, J., Elolähde, T., Konttinen, M., Koskentalo, T., Rantakrans, E. (2000). A modelling system for predicting urban air pollution, Model description and applications in the Helsinki metropolitan area. Atmospheric Environment 34(22), 3723-3733. Kousa, A., Kukkonen, J., Karppinen, A., Aarnio, P., Koskentalo, T. (2002). A model for evaluating the population exposure to ambient air pollution in an urban area. Atmospheric Environment 36, 2109-2119. Lim, L.L., Hughes, S.J., Hellawell, E.E., (2005). Integrated decision support system for urban air quality assessment, Environmental Modelling and Software 20 (7), 947-954.

Development of a DSS for the operation of thermal power plants

161

Poch, M., Comas, J., Rodriguez-Roda, I., Sanchez-Marre, M., Cortes, U. (2004) Designing and building real environmental decision support systems. Environmental Modeling & Software, 19, 857–73. Samara C. (2005). Chemical mass balance source apportionment of TSP in a lignite-burning area of western Macedonia, Greece. Atmospheric Environment 39, 6430-6443. Syrakos A., Sfetsos A., Politis M., Bartzis J.G., A comprehensive evaluation of the UOWM MM5-SMOKE-CMAQ modeling system for Western Macedonia, 12th Int. Conf. on Harmonisation within Atmospheric Dispersion Modelling for Regulatory Purposes, Cavtat, Croatia, October 6-9, 2008 Tarodo, J. (2003). Continuous emission monitoring, World Cement 34, 67–72. Zhou, P., Ang, B.W., Poh, K.L. (2006). Decision analysis in energy and environmental modeling: An update. Energy 31, 2604–2622. Triantafyllou, A. (2000). Patterns and concentrations of PM10 in a mountainous basin region. Journal of the Air & Waste Management Association, 50,10171022 Triantafyllou, A. (2003). Levels and trend of suspended particles around large lignite power stations. Environmental Monitoring and Assessment 89, 15-34. Vlachogiannis D, Sfetsos A, Gounaris N, Bartzis JG (2007) Compilation of Air emission inventories for Western Macedonia, Greece, Urban Air Quality 2007, Cyprus