This is the author’s version of a work that was submitted/accepted for publication in the following source: Marwan, Marwan, Ledwich, Gerard, Ghosh, Arindam, & Kamel, Fouad (2011) Integrating electrical vehicles to demand side response scheme in Queensland Australia. In IEEE PES Conference on Innovative Smart Grid Technology Asia, 13 - 16 November 2011, Pan Pacific Hotel, Perth, WA. This file was downloaded from: http://eprints.qut.edu.au/48586/
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Integrating Electrical Vehicles to Demand Side Response Scheme in Queensland Australia M. Marwan, G. Ledwich, A. Ghosh and F. Kamel
Abstract-- Depleting fossil fuel resources and increased accumulation of greenhouse gas emissions are increasingly making electrical vehicles (EV) attractive option for the transportation sector. However uncontrolled random charging and discharging of EVs may aggravate the problems of an already stressed system during the peak demand and cause voltage problems during low demand. This paper develops a demand side response scheme for properly integrating EVs in the Electrical Network. The scheme enacted upon information on electricity market conditions regularly released by the Australian Energy Market Operator (AEMO) on the internet. The scheme adopts Internet relays and solid state switches to cycle charging and discharging of EVs. Due to the pending time-of-use and realprice programs, financial benefits will represent driving incentives to consumers to implement the scheme. A wide-scale dissemination of the scheme is expected to mitigate excessive peaks on the electrical network with all associated technical, economic and social benefits. Index Terms--Demand side response, Electrical vehicles, Electrical network, transportation.
II. INTRODUCTION
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USTRALIA consumed 30 billion liters of fossil oil for transportation in the 12 months prior to 31 October 2007 (62.8 % petrol and 31.2 % diesel) [1]. Transportation is roughly the equivalent of 250 million barrels, or about 80 % of Australia's total liquid fuel consumption. Of this total, petrolconsuming passenger vehicles accounted for nearly half of the total [1]. In 2007, cars were responsible for almost half of transport-related greenhouse gas emissions (GHGs) in Queensland, equivalent to more than 9 million tons of carbon dioxide (Mt CO2e). As Queensland population and car use continue to grow, emissions from transport are projected to reach 21 Mt CO2e/year by 2020 [2]. Between 1990 and 2007, Queensland’s transport sector emissions increased by almost 59 per cent [3]. In 2007, the transport sector was the fourth largest source of greenhouse gas emissions in Queensland, generating approximately 18.9 Mt CO2e representing 10.4 per cent of statewide emissions [3].
I. NOMENCLATURE GHGs EV AEMO V2G DSR TOU RTP CPP DLC I/C DB EDRP CAP A/S
: Greenhouse gas emissions : Electrical Vehicles : Australian Energy Market Operator : Vehicles to Grid : Demand Side Response : Time of use : Real time pricing : Critical peak pricing : Direct load control : Interruptible/curtailable : Demand bidding : Emergency demand response program : Capacity market programs : Ancillary service markets
This work was supported by the Directorate General of Higher Education Department of National Education the Indonesian Government and the State Polytechnic of Ujung Pandang Makassar Indonesia. Marwan Marwan, Gerard Ledwich and Arindam Ghosh are with Queensland University of Technology, Brisbane, Queensland, 4001.
[email protected],
[email protected],
[email protected]. Fouad Kamel is with the Faculty of Engineering and Surveying University of Southern Queensland Toowoomba Australia.
[email protected]
Fig. 1. Queensland transport emission by sector [3].
Figure 1 illustrates road is the majority of transport-related greenhouse gas emission in 2007. Of this, passenger cars were responsible for approximately 9.17 Mt CO2-e 57 % of all road transport emission and almost 50 % of total transport emissions [3]. Figure 2 shows transport sector emissions have grown by 59 per cent over the last 17 years. This can be attributed to the recent mining and resources boom, freight needs associated with high population and economic growth, and new competition in the aviation sector providing greater access for Queenslanders to lower cost air travel [3]
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Fig. 2. Queensland transport emission trend 1997 - 2007 [3].
Fig. 3. Queensland transport emission projection to 2050 [3].
Figure 3 highlights this trend, with transport related greenhouse gas emissions projected to reach approximately 30.5 million tonnes by 2050 [3]. III. DEMAND SIDE RESPONSE AND ELECTRIC VEHICLES Definition of demand side response includes both modifications of electricity consumption by consumers in response to price and the implementation of more energy efficient technologies [4]. Those modifications in behavior include primarily price response by consumers, but also can include substitution of grid sourced electricity with behindthe-meter or distributed generation [5]. As demand response is allowed to more fully participate in energy markets, it has the potential to provide economic benefits in addition to reliability benefits [6]. The benefits of demand response are expected to grow as it becomes an integral part of energy markets. For example: government regulations, financial performance, customer satisfaction and system reliability [6]. Many different economic models are used to represent Demand Side Response programs. DSR is divided into two basic categories, namely: the time based program and the incentives based program [7]. The specific types of time based program are: TOU, RTP and CPP [8]; while the specific types of incentive based program consist of DLC, I/C, DB, EDRP, CAP and (A/S) programs [9].
EV technologies bring impacts to the electrical distribution grid. The vehicle can not only charge, but also discharge and thus inject energy to the grid [10]. In addition, there are social, environmental and economic advantages in switching to electrical vehicles [11]. Electric vehicles can be connected either to the house or to charging facilities and perform vehicle to grid (V2G) discharging as well. Charging or discharging of the vehicles battery can be performed according to the remote commands from the grid operator or demand side response scheme [12]. Further on, the electrical vehicles system can help to match consumption and generation by charging and discharging at the right moment' [10]. EVs used in combination with smart grid technology DSR programs have the potential to further cut greenhouse gas emissions as EVs provide a way of using and storing electricity which is often stored when demands for electricity are low. Additionally benefits from widespread use of EVs over time include improved air quality (especially in urban areas), energy security through reduce dependency on fossil fuels and the potential for industry development through the development of a local EV supply chain [2]. Electric vehicles combined with DSR programs and increased renewable energy contribution have the potential to significantly reduce generation as well. In Australia, charging EVs at night using DSR program would allow the current electrical generation system to run continuously at full capacity and allow a displacement of perhaps 50% of petrol usage and associated emissions [1]. The recent government investment in the Toyota electric hybrid car construction facility in Australia is an example of such support, develop and demonstrate activities to bring forward lower carbon vehicles [1]. In December 2009, the Queensland Government signed on to the EV20 initiative an, international accord between global cities, states and countries to collaborate in accelerating the development and deployment of EVs [2]. On 15 June 2010, the Queensland Government released An Electric Vehicles Roadmap for Queensland. Electric vehicles can offer Queensland drivers a low carbon motoring option when compared to traditional petrol-powered cars. Electric vehicles are considered to be an important next generation technology as they offer the potential for zero emission car travel when recharged from renewably generated electricity. [13]. IV. LOAD ECONOMIC MODEL Customer participation in a DSR program could expect savings in electricity bills if they reduce their electricity usage during peak periods [14]. Figure 4 illustrate how the demand elasticity could effect on electricity prices [15].
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∑
,
(5) where: : Initial demand value (MWh) : Initial electricity energy price ($/MWh)
Fig. 4. Simplified effect of DSR on electricity market prices [15] .
Price elasticity of demand is calculated by dividing the change in quantity (d) demanded by the proportionate change in price (p). To measure the price elasticity of demand at a specific point on demand curve is called point elasticity. For an infinitesimally small change in price and quantity, it is defined by differential shown below [16]:
In order to achieve the maximum benefit for the consumer, detailed process and formulating the optimization model are discussed by [15, 17]. The final economic model as defined by [17] as
(1) Where: : Elasticity of demand : Demand value (MWh) : Electricity price ($/MWh) Based on equation (1), consumers can calculate the price elasticity of the ith period versus jth period, defined as [16]:
(6) where: A : Incentive of DSR program ($/KWh) pen : Penalty ($/KWh) V. METHODOLOGY
,
(2)
In real conditions, the electricity price is different at different times. Some of the appliances can only be used at one period and -it is not possible to use them at a different time period. Such loads have sensitivity just in a single period. This is called "self elasticity" and always has a negative value [17]. On the other hand, some loads can be used or transferred from peak to off-peak periods. Such behavior is called multi period sensitivity and it is evaluated by a positive "cross elasticity," always yielding a positive value [18]. The self elasticity and the cross elasticity can be classified as [17]:
, ,
0 0
(3)
Accordingly, based on scheduling periods from AEMO every 30 minutes, both self and cross elasticity coefficients can be arranged in a 48 by 48 matrix for a day as:
1,1 48,1
,
1,48 (4)
48,48
The following optimal demand model can be deduced as [16]:
This work aimed at developing an integrated energy scheme that enables electricity consumers an automated control of energy consumption and optimized use of electrical vehicles. The main purposes of this control is for users to avoid peak-demand periods on the electrical network, thus helping to mitigate detrimental impacts and risks of heavy congestions and charging EV at suitable low demand periods. The proposed scheme comprises a technical set-up of a programmable internet relay, a router, solid state switches in addition to a suitable computer program to control electricity flow [19]. In order to achieve the aims and objectives of this research, a multi media tool is developed for use on user’s premises, to enable the users to effectively and continuously apply the model[20,21]. The relay is programmed to receive and act upon information received about electricity demand/price conditions every 30 minutes from the Australian Energy Market Operator (AEMO) over the Internet [22]. This is a low-cost DSR technique implemented at user's premises, which assists electricity end-users to shift loads around the clock averting peak-demand periods and making use of low price-elasticity. This will help the users to be engaged in leveling peak demands on the electricity network. The scheme is structured to maximize benefits for endusers. Consumers are gaining an automated control on consumption according to own preferences. In case the user is already on a DSR program agreement with the supplier, the scheme still allows additional savings besides the benefits and savings already achieved through other DSR agreements. The
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proposed scheme can secure financial benefit and energy savings for the consumers. The scheme is applicable for commercial and industrial consumers on fluctuating energy prices as well. For domestic consumers on flat-rate tariffs, users can gain financial benefits by reducing energy consumptions at certain times a day; mainly averting peak-load periods. Domestic consumers on tariffs in which the energy price differs with day time and network conditions (e.g. night tariffs), will be getting financial benefits by shifting loads from day to night, when electricity is cheaper. The scheme can help in engaging end-users to participate in solving the peak and low demand problem on the electrical network in Australian States covered by the Australian Energy Market Operator (AEMO) and other electricity markets under similar operation condition [23]. Figure 5 describes the proposed scheme [24].
Fig. 5. Power flow of demand side response smart grid scheme integrating renewable energy sources and electrical vehicles [24].
VI. CASE STUDY In order to evaluate the effect of the proposed scheme on electricity energy savings the electricity price/demand in Queensland, 2nd May 2010 has been used as a sample. Maximum demand was 5994.87 MW and base load (minimum load) was 4413.18 MW, with a difference of 1581.69 MW. Figure 6 shows the impact of implementing the scheme on an arbitrary percentage load moderation by transferring loads from peak to off-peak periods to 60 %, 75 % and 90 % of the load above the average load.
Scenario 1 (60% reduction) The DSR program causes a reduction of load above the average load to 60% of original load. The maximum demand is 5678.88 MW, the minimum 4729.87 MW with a difference of 949.01 MW. Scenario 2 (75% reduction) The DSR program causes a reduction of load above the average load to 75% of original load. The maximum demand is 5797.38MW, the minimum 4611.11 MW with a difference of 1186.27MW. Scenario 3 (90% reduction) The DSR program causes a reduction of load above the average load to 90% of original load. The maximum demand is 5915.87 MW, the minimum 4492.35 MW with a difference of 1423.52 MW. As shown in Figure 7, the consumers fully curtail energy consumption at any energy price above $55/MWh.
Fig. 7. Day electricity demand curtailed for wholesale regional reference price above AUD $55/MWh in Queensland in 2010
Figure 8 shows achievable energy savings in Queensland by curtailing energy demand over certain energy price. This figure illustrates that the technique can enable consumers can reduce load 6.1 TWh/year from a total of 52.324 TWh/year if they are prepared to curtail loads at any regional reference prices above $50/MWh. In case users choose to curtail loads at $40/MWh, the savings are 12 TWh/year; 25 TWh/year at $30/MWh and 44 TWh/year at $20/MWh.
Fig.8. Achievable energy savings by curtailing energy demand over a certain energy price in Queensland
Fig. 6. The impact of implementing DSR scheme in Queensland on 2nd May 2010.
Figure 9 depicts the possibility to utilize 25.558 TWh during 2010 for electrical vehicles, mainly from peak-load power stations. The procedure helps enhancing the utilization of present electrical power stations to approach a plant
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utilization factor close to the unity, thereby achieving an optimal use of power plants. Figure 10 depicts the case where the proposed scheme is enables consumers to defer loads above the average load from times of peak-demand to times of low-demands. Such a procedure shall help flattening the total energy demand to meet a constant average of 5957 MW for Queensland. In such a procedure the technique enables deferring 3.175 TWh/year from peak to off-peak times.
Fig. 9. Occurrence of electrical demand in Queensland during 2010 accommodating electrical vehicles.
Fig. 10. Occurrence of electrical energy demand in Queensland during 2010 with average demand 5957 MW.
VII. CONCLUSION The proposed DSR scheme aimed to develop an integrated energy scheme that enables electricity consumers to gain autonomous control on own energy consumption and optimized use of electrical vehicles securing financial and energy savings. The scheme can help in engaging consumers to be participating in solving peak and low demand conditions on the electrical network in Australian States covered by the Australian Energy Management Operator (AEMO) and other electricity markets under similar operating conditions. The scheme helps using 25.558 TWh/year of electrical capacity for charging EV in Queensland. This is the capacity needed to raise the present demand to the level of a full utilization of the installed electrical capacity in 2010. Thus, the scheme helps enhancing the utilization of the present electrical infrastructure to approach the capacity factor close to the unity. VIII. REFERENCES 1. Michael Dopita and R. Wiliamson, "Australia's Renewable Energy Future," Australian Academic of Science: Canberra, 2010 2. X. Zhang, "Orderly Consumption and Intelligent Demand-side Response Management System under Smart Grid," in Proc. 2010 Power and Energy
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IX. BIOGRAPHIES Marwan Marwan received thee B.Eng degree from Hasanuddin University Makasssar Indonesia and the M.Eng degree from Queenssland University of Technology (QUT) Brisbane A Australia, in 2000 and 2006, respectively, all in electriccal power engineering. He has been a lecturer with thee State Polytechnic of Ujung Pandang Makassar Inndonesia in Energy Conversion Department sincce 2001. Presently, Marwan is pursuing his Ph..D degree with the Queensland University of Technology (QUT) Brisbane Australia. His researrch interests include demand-side-response, smart grrid, electrical market, electrical vehicles and renewablee energy. Professor of Power Gerard Ledwich is the P Engineering at Queensland Univversity of Technology, Brisbane, Australia. He receiveed his Ph.D. degree in University of Newcastle. He is now with QUT leading the Power Engineerinng Group in power systems, power electronics and H HV equipment. Arindam Ghosh (S’80, M’83, SM’93, F’06) is the Professor of Power Engineeering at Queensland University of Technology, Brrisbane, Australia. He has obtained a Ph.D. in EE E from University of Calgary, Canada in 1983. Prioor to joining the QUT in 2006, he was with the Dept. of Electrical Engineering at IIT Kanpur, Inddia, for 21 years. He is a fellow of Indian National Acaademy of Engineering (INAE) and IEEE. His intereests are in Control of Power Systems and Power Elecctronic devices. Fouad Kamel received hiis Ph.D. degree in photovoltaic systems from Hanover University, Germany in 1984. He is curreently a senior lecturer with the Faculty of Engineeering and Surveying, Department of Electrical, Elecctronic and Computer Engineering, University of Soouthern Queensland in Toowoomba, Australia. Hiss research interests include demand-side-response,, smart grid, electrical market, renewable energy and eelectrical vehicles.
