TOWARDS ENERGY INDEPENDENCE IN THE CITY OF VERIA, GREECE

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1 Proceedings of the 13 th International Conference on Environmental Science and Technology Athens, Greece, 5-7 September 2013 TOWARDS ENERGY INDEPENDENCE IN THE CITY OF VERIA, GREECE A.LELAKI, V. SOULIOS, D. VAGIONA AND G. KOUTITAS Department of Spatial Planning and Development, Faculty of Engineering, Aristotle University of Thessaloniki, Aristotle University of Thessaloniki, Thessaloniki, Greece. EXTENDED ABSTRACT Renewable energy sources (RES) have radically penetrated in the power grid sector and the available energy can be considered as a time variant parameter. It strongly depends on the meteorological conditions and the time of the day. In addition, many countries strategies and policies have been focused upon the reduction in greenhouse gas emissions and the smart power grid concept is considered as the most promising solution to meet the new era. Together with the increase of information and communication technology (ICT) sector and innovations in this direction, it is believed that the next decade will be characterized by dramatic changes in the smart city scene. In this paper, a methodology of transforming the city of Veria, a city of inhabitants in northern Greece, in a smart city in terms of clean energy consumption and energy management is presented. Annual energy demand is first analyzed and alternatives of responding to this amount are thoroughly investigated. According to the natural and geographical characteristics of the area under study, two kinds of renewable energy sources are selected: solar and wind energy. Under the hypothesis that photovoltaic panels can be installed on the roofs of every single building of the town, the percentage of the energy needs of the city that can be satisfied by solar energy is examined. According to the criteria of the methodology of this installation, some segments of the roofs are rejected. The output of the beneficial surface can produce 58% of the energy demand of Veria. The cost of this venture is estimated to reach and the payback period will be achieved in about 6 years, assuming a selling price of 0.45 /KWh. In order to fulfill the rest of the energy needs, the appropriate siting of wind turbines on the outskirts of the town is investigated. As far as the spatial criteria of wind farm siting and the wind potential are concerned, the installation of 19 wind turbines in the area of Panagia Soumela and Zwodoxos Phgh is investigated and accepted. Moreover, an algorithm of Direct Load Control is implemented, the potential savings that the smart grid can achieve based on demand side management procedures are investigated. A suggestion for dynamic pricing, which contributes to the energy saving, especially in peak hours, saves resources and reduces the individual bills of electricity, is finally performed. KEYWORDS: energy independence, renewable energy sources, dynamic pricing

2 1. INTRODUCTION Nowadays a more rational management of energy is considered necessary due to the environmental, economic and social demand. Innovations in technology have led to the development of energy systems friendly to the environment and the users. The majority of RES are sources of energy, which operate in such way that they cannot cover the peak demand only by themselves. Nevertheless, combining and utilizing them as Distributed Energy Resources, they can cover a large amount of the energy demand operating in a smart grid mode. Smart grids are considered as novel technology grids, which allow an efficient penetration of RES, supported by ICT, while they are reducing the concentration of greenhouse gases in the atmosphere. Smart grid can provide smart ways to monitor and control energy as well as balance the energy demand in a building or a neighborhood. The management of the transmission and distribution uses two-way communications, advanced sensors and computers able to improve the efficiency, reliability and security of electricity transmission and use. These networks use mainly existing technologies, but apply them in new ways to network operation (Kärkkäinen, 1993). The main purpose of this paper is to indicate that RES can cover a great percentage of the energy demand of a city, and even make it energy independent, technical supported by smart grids. It also presents the economic evaluation of this application and proposals leading to a change in the energy profile of consumers, in resource savings of energy providers and applications that facilitate the daily lives of users. The operation of the above is undertaken in Veria, which is an average Greek city concerning the population. Moreover, the authors examine solar and wind energy because they consist of the two most commonly used sources in the Greek territory. The demographic and economic characteristics of the city and the electricity consumption data given by the Public Power Corporation are initially analyzed. Therefore, ways in order to exploit the solar and wind potential of Veria are examined, installing photovoltaic panels and taking advantage of the produced energy of an existing wind park in the suburbs of Veria. Subsequently, the architecture and the operation of smart grids are described, facing the intermittency of RES. In order to maximize the efficiency of this venture, the principles of demand response are introduced and a suggestion for dynamic pricing, purposing energy saving and advantages for both the provider and the consumer. 2. DESCRIPTION OF THE TOWN 2.1 Demographic and economic characteristics of Veria Veria has the characteristics of a typical Greek city in terms of area and population (43,683 inhabitants and Km 2 size) (National Statistical Authority, 2001). By studying the energy consumption of Veria, some basic conclusions are exported about the energy consumption of a typical Greek city. The Public Power Corporation, which is a dynamic network, provides electricity in Veria. The economy of the city is mainly based in the primary sector, and especially in agriculture. Industrial activity is currently limited to the processing of agricultural products and the tertiary sector is based in export trade and tourism (National Statistical Authority, 2001).

3 2.2 Electricity consumption of Veria According to the consumption data, provided by the National Electricity Company of Greece, for the year 2011, it is calculated that Veria consumed GWh (Public Power Corporation of Greece, 2011). In the following figure, 4 days of 4 different seasons of 2011 are selected. The 15 th day of every middle month of every season is selected, in order to consider the most representative values. The energy consumption (MWh) per hour is illustrated for each of these 4 days of the year. During the night, the energy consumption levels are low, whereas during the day, there is an exponential, followed by a stable growth of the consumption. Figure 1: Energy Consumption (MWh) per hour for 4 days of 2011 for Veria, (PPC of Veria) 3. DISTIBUTED ENERGY RESOURCES 3.1 Definition of Destributed Energy Resources According to the National Renewable Energy Laboratory of USA, Distributed Energy Resources (DER) are small, modular, energy generation and storage technologies that provide electric capacity or energy wherever needed (National Renewable Energy Laboratory of USA, 2002). The types of DER examined in this case study (city of Veria) are photovoltaic panels and wind turbines. 3.2 Photovoltaic Panels In order to install photovoltaic (PV) panels on the roof of every single building of Veria, the shadowed segments of the roofs shoud be excluded. In order to identify this segments, the floors, the type of the roof (terrace or tiles) and the movement of the sun during the day of everyday of the year are recorded (Figure 2). After this process, the following assumptions are considered: (a) installation of panels with south, southeast and southwest orientation, (b) appropriate inclination reaches 20-30%. The combination of the

4 above assumptions implies a maximum efficiency that equals to 90%. The roofs with north orientation are also excluded from the siting of PV panels. Figure 2. Beneficial and shadowed segments The beneficial surface for roofs with tiles is calculated equal to m 2, whereas the beneficial terrace to m 2. Panels with mean dimensions, mean nominal power and mean cost are used. In order to estimate the number of panels installed, the shadow that every panel creates should be calculated so as to place the next one in the suitable distance. The amount of panels that can be installed in roofs with tiles is about panels, whereas in terraces panels. The total installed power capacity of the panels for the whole city is computed and reaches 46.5 MW. Combining this number with the hours of solar radiation according to the data provided by the National Meteorological Service, the total produced energy is calculated about GWh. However, considering that there are 24.7% losses due to temperature and 10% losses due to the angle of incidence, the energy production is estimated to reach GWh (HelioSystems, 2011). The energy produced by PVs is going to fulfill 58% of the total energy demand of the city. In order to calculate the total cost of this project, the panels cost, the number and the kind of inverters and the necessary number of tables are considered. We also estimated costs for transportation, materials, foundations of panels, labor and maintenance. The total cost reaches and the payback period is estimated to be in about 6 years, assuming a selling price of 0.45 /KWh (Public Power Corporation of Greece, 2011; Avelion, 2012). Moreover, according to the guidelines of Special Framework for Spatial Planning and Sustainable Development for RES, the option of solar farms siting in the agricultural area around the city is rejected, as the majority of this area includes high productivity land. 3.3 Wind Turbines The rest of the energy needs will be obtained from an existing wind farm at the outskirts of the town. The park is situated in the area of Panagia Soumela and Zwodoxos Phgh and is consisted of 19 turbines, which generate power of 38MW. Each wind turbine has an horizontal axis and three blades. According to the wind potential of the area, this farm is able to produce 66.4 GWh per year that are enough to cover the rest of the city needs (53.47 GWh). The installation of these turbines is initially examined according to the spatial criteria of wind farm siting and finally accepted. These criteria include: (a) maximum allowable wind power density at primary local authorities - calculation of load

5 capacity of the city, (b) excluding areas such as declared monuments of world cultural heritage, areas of nature conservation and Ramsar wetlands (c) control criteria for location such as distances to ensure the functionality and performance of wind power, distances from areas of environmental interest, distances from regions and elements of cultural heritage, distances from residential activities, distances from technical infrastructure networks and specific uses, distances from facility operations and (d) landscape criteria. The cost of this operation is (Imathia Prefecture, 2007). 4. SMART GRIDS Taking all the aforementioned into consideration, it is obvious that RES can cover the energy demand of a city. Nevertheless, there are a lot of challenges. The main one is the intermittency of solar and wind power, because there is no solar radiation during the night and the wind does not blow constantly. For the efficient implementation, operation and management of RES, a smart grid is necessary. Smart grid monitors and controls energy and communicates interactively using ICT. It can also face emergency conditions with its ability of self-healing while serving a wide range of consumers timely and safely as it has an intelligent communications infrastructure (Sollecito, 2009). 5. DEMAND SIDE MANAGEMENT AND DYNAMIC PRICING 5.1 Demand side management Demand Side Management (DSM) is the systematic utility and government activities designed to change the amount and/or timing of consumers use of energy for the overall benefit of the society. This is a generic term that is used to encompass various categories of activities such as: load management which aims at reducing or changing the size or timing of the demand, energy conservation which aims at reducing the demand, essentially through technical efficiency improvements and load building which implies developing load for strategic purposes which could help manage the system better. Smart grid management and renewable energy integration are becoming very important aspects of efficient energy management (Federal Energy Regulatory Commission, 2006; Warmer et al., 2007). The way that demand response operates and how possible it is for the city of Veria, in theory, to cover its energy needs by using RES is presented in Figure 3.

6 Figure 2. Production and demand regarding the time, Veria 2011 It is based in the control of flexible smart devices that a smart grid is consisted of. Every new device which is entering the grid can be flexible or not and the grid will be able to turn off the device or not. The demand-response (DR) algorithm aims to control the flexible devices in order to equilibrate demand and production of energy. The bold black line illustrates the average rate of the energy produced by the installed PV and wind turbines in the city. This graph concerns the energy consumption of Veria on 15th of July The maximum power consumption is 24 MW. The metering starts at 6:00 a.m., when electricity consumption actually begins. The energy reduction achieved with DR is 15%, which means that GWh per year could be saved, according to the energy consumption of the town. Therefore, producing energy by RES and implementing the principles of demand response the energy demand of Veria can be fulfilled apart from 6 hours per day (20:00 p.m. - 2:00 a.m.) (Koutitas, 2012). 5.2 Dynamic Pricing Dynamic pricing charges electricity according to the moment of consumption increases the cost of electricity during peak hours and reduces this cost during off peak hours. The main goal of implementation of dynamic pricing in Veria is the profit for both consumers and the energy provider, reducing the energy demand during peak hours and equating this to the production of energy by RES, without increasing the total energy consumption of the city. Initially, in order to make this venture, taking into account the first 100 peak hours, it is detected that the duration of peak hours lasts from 5:00 p.m. to 9:00 p.m. These hours correspond to the 80% of the sample. It is estimated that during these hours the city consumes 37.7 GWh and the production of RES is GWh. The demand elasticity is defined as the percentage change in quantity divided by the percentage change in price (e= %Δ quantity/ %Δ price {1}) and according to literature review, the elasticity for peak hours and off peak hours is -0.5 and 0.9 relatively. Therefore for peak hours, 1 KWh costs (according to {1}) and for off peak hours costs according to the condition

7 above (Woo 1985; Taylor 1997; Hawdon 1992; Filippini and Massimo 1995; Encorp Intelligence Virtual Power Plant, 2003) Hence, the implementation of dynamic pricing implies benefits for the provider, as the total income is higher after dynamic pricing than before (short term profit) and the expenses are reduced because of resources saving (long term profit). Consumers bills will be lower if they change their mentality and the reduction of ecological footprint will be achieved. 6. CONCLUSIONS PV panels and wind turbines are suggested for the energy independency of the city of Veria. The total amount of PVs is equal to and their total power is 46.5 MW, which means that they cover 58% of the energy needs. The cost of this operation is estimated to reach In order to cover the rest of the energy needs, an existing wind park is exploited. This park is consisted of 19 turbines of 2 MW power each and it costs For the effective penetration of RES into the existing power grid, the new technology called smart grid is necessary. Smart grids can face the intermittency of these resources and can support a secure communication between the consumers and the power provider. As far as the intermittency of RES is concerned, the operation of smart grid can be enhanced by demand side management, which reduces the consumption by 15%, and dynamic pricing which reduces the consumption by 26.5% for the city of Veria. Furthermore, with dynamic pricing one more hour that production of RES is equal to the demand of electricity is gained and the period that RES cover the energy needs is expanded to 19 hours. Thus, there are only 5 hours that are not covered by the combination of RES, smart grid, demand response and dynamic pricing. For these hours, either storage systems should be created in order to support an autonomous microgrid or the peak hours should be expanded until 2 am in order to create an interactive microgrid connected to the main grid. Another effective solution could be a smart mix of more types of RES to make the grid more reliable. REFERENCES 1. Avelion (2012) URL: (accessed 01/02/2012) 2. Encorp Intelligence Virtual Power Plant (2003) Virtual Power Plant Multi - Site Remote Dispatching Software, Technical Data, Colorado. 3. Federal Energy Management Program, Office of Energy Efficiency and Renewable Energy (2002) Using Distributed Energy Resources: A How-To Guide for Federal Facility Managers, National Renewable Energy Laboratory of USA. 4. Federal Energy Regulatory Commission (2006, Revised December 2008), Assessment of Demand Response and Advanced Metering, Report, Washington. 5. Filippini M. (1995) Electric Demand by Time of Use: An Application of the Household AIDS Model, Energy Economics, 17, Hawdon, D. (1992) Is Electricity Consumption Influenced by Time of Use Tariffs? A Survey of Results and Issues, Energy Demand: Evidence and Expectations, Surrey Univ. Press, HelioSystems (2011) URL: (accessed 17/04/2012) 8. Imathia Prefecture (2007) Installation and operation of Power Station at positions of "Panagia Sumela, D. Veria Imathia and "Life-Giving Spring", D. Hellespont Kozani (total capacity of

8 38MW), Study of Environmental Impacts, Imathia Perfecture. 9. Kärkkäinen S. (1993) Task 17 Integration of Demand Side Management, Distributed Generation, Renewable Energy Sources and Energy Storages of International Energy Agency Demand-Side Management Programme, vol.1: Main Report, VTT, Finland. 10. Koutitas G. (2012) Control of flexible smart devices in the smart grid, IEEE Trans. on Smart Grids, 3, Lester D.T. (1977) The Demand for Electricity: A Survey of Price and Income Elasticities, in International Studies of the Demand for Energy, William D. Norhaus, ed. Amsterdam: North Holland. 12. National Statistical Authority (2001) URL: (accessed 06/06/2012) 13. Public Power Corporation (PPC) of Greece (2011) Energy Consumption Data of Veria for 2011, Report, Research Department, Veria. 14. Public Power Corporation of Greece (2011) Introducing new tariffs of PPC, Report, Athens. 15. Sollecito L. (2009) Smart grid: The road ahead, Protection and Control Journal, 8, Warmer C., Hommelberg M., Kamphuis R. and Kok, K. (2007), Market Integration of flexible demand and DG-RES supply - A new approach for Demand Response, 19th International Conference and Exhibition of Electricity Distribution, May 2007, Vienna, Italy. 17. Woo C.K. (1985) Demand for Electricity of Small Nonresidential Customers under Time of- Use (TOU) Pricing, Energy Journal, 6,

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