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1. Department of Electrical and Computer Engineering, School of Engineering and Science, Curtin University, Sarawak Malaysia
2. Faculty of Built Environment and Engineering, Queensland University of Technology, Brisbane, Australia
3. Energy Conversion Department Polytechnic, State of Ujung Pandang, Makassar, Indonesia
Received: June 03, 2011 / Accepted: July 07, 2011 / Published: February 25, 2012.
Abstract: Despite several efforts of electrical energy suppliers to mitigate peak demand by adopting smart meters and other supplier-centred smart grid techniques the problem is currently persistent. This research endeavours to present a demand side response(DSR) scheme that enables electricity consumers to engage in tackling the problem by independently observing market conditions and simultaneously acting to mitigate peak demands by shifting loads from peak to off peak periods. This shall assist in achieving improved economic conditions for the electricity infrastructure and financial benefits to the consumer as well. The technique is based on accessing the network’s load profile publicly released by the Australian Energy Market Operator (AEMO) on the internet. The scheme is using programmable internet relays and computer-controlled switches installed at consumer’s premises. Pre-prepared computer software installed on a multi-media tool (CD-Rom) is designed to monitor the state of electricity market on the AEMO website and carryout load control accordingly. The scheme is targeting best economic conditions for consumers, suppliers, electrical generators and the electrical network. To evaluate the scheme simulations have been conducted using a typical demand-price profile for Queensland. The results are indicating the impact of this scheme on achieving energy and monetary savings in electrical consumption. Key words: Demand side response, electrical energy consumption, electricity market, electrical network, smart grid.
1. Introduction
The National Electricity Market (NEM) across the Australian Capital Territory, New South Wales, Queensland, South Australia, Victoria and Tasmania is operated by the Australian Energy Market Operator(AEMO) responsible for the supply and purchase of electricity. Western Australia and the Northern Territory are not currently connected to this market primarily because of their geographic distance from the rest of the market. In order to achieve the high quality service to all customers the AEMO sends price/demand information to the public on the internet every 30 minutes. Electricity users can generally access actual information about demand and price of electricity using internet facilities.
Based on data of December 31, 2008, Queensland total electricity generating capacity was 12487 MW; coal-fired power stations provided 70% of this total capacity, while gas-fired electricity accounted for 17%, renewable energy accounted for around 5%, energy storage 4% and other fossil 4% [1]. These power generations are used to provide electrical energy for all consumers in the Queensland area: residential, commercial and industrial. However, the amounts of energy produced from various generators depend on market demand, price and availability of sources. Fig. 1 illustrates electricity generation in Queensland as on 31st December 2008 [1].
The total Australian energy consumption grew at an annual rate of 2.6% for the 25 years to 1997/1998 [2]. In the 2007-2008 period, the annual electricity consumption in Queensland has grown by over 29% or approximately 10500 GWh making Queensland the second highest consumer of electricity in Australia [3].
Many different economic models are used to represent Demand Side Response (DSR) programs. DSR is divided into two basic categories, namely: the time based program and the incentives based program[24]. The specific types of time based program are: time of use (TOU), real time pricing (RTP) and critical peak pricing [25]; while the specific types of incentive-based programs are direct load control(DLC), interruptible/curtailable (I/C), demand bidding(DB), emergency demand response program (EDRP), capacity market (CAP) and ancillary service (A/S) programs [26]. In the following brief description of four popular market available programs: TOU, RTP, I/C and EDRP. 4.1 Time of Use
Time of use (TOU) is one of the important demand side response programs, which responds to the price and is expected to change the shape of the demand curve [27]. Further on, TOU rate is the most obvious strategy developed for the management of the peak demand in the world, which is designed to encourage the consumer to modify the pattern of electricity usage[28]. For applying this program, the utility does not provide reward or penalty to consumers. To participate, all consumers are required to remove energy consumption during peak session to off-peak session as soon as they receive information from the utility. The type of contract and the rate is fixed for the duration of the contract but it depends on the time of the day [29]. As compared to the flat rate contract some of the risk is shifted from retailer to consumer because the consumer has an incentive to consume during periods when the rates are lower. Fig. 6 illustrates the type of hourly price variation consumers would face under different TOU rates.
4.2 Real Time Pricing
The real time pricing (RTP) program gives consumers to access hourly electricity prices that are
4.3 Interruptible/Curtailable Program
Interruptible/curtailable (I/C) program has traditionally been one of the most common DSR models used by electric-power utilities. In this program consumers sign an interruptible-load contract with the utility to reduce their demand at a fixed time during the system’s peak-load period or at any time requested by the utility [30]. This service provides incentives/rewords to consumers participating to curtail electricity demand. The electricity provider sends directives to the consumers for following this program at certain times. The consumers must obey those directives to curtail their electricity when being notified from the utility or face penalties. For example: the consumers must curtail their electricity consumption starting from 6:00 pm-7:00 pm; those consumers who are following will get a financial bonus/reword to their electricity bill from the utility. In California the incentive of the I/C program was $700/MWh/month in 2001 [23].
4.4 Emergency Demand Response Program
Emergency demand response program (EDRP) is energy-efficient program that provides incentives to consumers who can reduce electricity usage for a certain time; this is usually conducted at the time of limited availability of electricity. EDRP provides participants with significant incentives to reduce load[31]. To participate on this program, all consumers are expected to reduce energy consumption during the events. This program will determine which houses must be included in the event to minimize cost and disruption, while alleviating the overload condition[32]. When asked to curtail, and verified to have performed, the consumer is paid as high as $500/MWh[33]. In New York, emergency demand response program allowed participants to be paid for reducing energy consumption upon notice from the New York Independent System Operator (NYISO [34].
Fig. 8 indicates the importance of the EDRP during a reserve shortage that occurred during July 2002.
This work is presenting a low-cost Demand-Side-Response (DSR) technical concept implemented at user’s premises, which assists electricity end-users to be shifting loads averting peak-demand periods and making use of on-site renewable energy sources. This shall help users to be engaged in mitigating peak demands on the electricity network. The proposed concept comprises a technical set-up of a programmable internet relay, a router, solid state switches and suitable software to control electricity demand at user’s premises (Fig. 9). In addition the scheme allows the user to shift loads from the utility supply to on-site renewable energy sources as available. The softwares on appropriate multimedia tool (CD Rom) developed in framework of this research shall offer users optimized control of energy consumption.
The described concept involves an economic model based on the maximization of financial benefits to electricity users. Additionally the scheme is designed to be targeting the national electrical load to be spread-out evenly throughout the year in order to satisfy best economic performance for electricity generation, transmission and distribution infrastructure. The scheme is applicable in regions managed by the Australian Energy Management Operator (AEMO) covering states of Eastern-, Southern-Australia and Tasmania and other regions in similar conditions.
Usually the electricity price will be high during peak demands and low at off-peak periods. The concept allows users controlling consumption on self-controlled load preferences. In case the user is on other DSR program with the supplier, the scheme is still allowing additional savings besides the benefits and saving already achievable through the DSR agreement.
For commercial and industrial consumers on fluctuating energy prices implementing the scheme will enable achieving immediate financial savings. For domestic consumers on flat-rate tariffs, in contrast, users are gaining financial benefits from reducing energy consumptions at certain times a day; mainly averting peak-load periods. Domestic consumers on different tariffs, where energy price differs with day time and network conditions (e.g., night tariffs), will be gaining financial benefits also by shifting loads from day- to night-times, where electricity is cheaper.
Achievable savings at users end in energy cost $35644.
Scenario 2: Users are shifting peak demand of 711 MWh occurring between 17:00-20:00 pm to the period between 23.30 pm to 00:00 am. Achievable savings in energy cost $53467.
Scenario 3: Users are shifting peak demand of 711 MWh occurring between 17:00-20:00 pm to the period between 02:00 am to 05:00 am. Achievable savings in energy cost $53467.
Scenario 4: Users are shifting peak demand of 711 MWh occurring between 17:00-20:00 pm to the period between 05:00 am to 07:30 am. Achievable savings in energy cost $53467.
Scenario 5: Users are shifting peak demand of 711 MWh occurring between 17:00-20:00 pm to the period between 07:30 am to 10:30 am. No savings in energy cost due to applicable day-time tariffs. However, the scheme was still able to remove congestions out of peak demand area.
Scenario 6: Users are shifting peak demand of 1078 MWh occurring between 10:30 am-20:30 pm. All participants are suggested to set-up the electricity profile to stop some appliance to run during that time. Users can run chosen appliances between 20:30 pm-01:30 am. Achievable savings in energy cost $46323.
The proposed DSR scheme represents an integrated energy model, which enables electricity end-users an automated control of energy consumption and optimized use of renewable energy sources. The main purposes of this control is for users to be averting peak-demand periods on the electrical network helping thus to mitigate detrimental impacts and risks of heavy congestions. The scheme is securing financial and energy savings to domestic, commercial and industrial consumers on fluctuating energy prices. It is helping engaging electricity consumers to be contributing solving the peak demand problem on the electrical network in Australian States covered by the Australian Energy Management Operator (AEMO) and other electricity markets in similar operating conditions.
[18] M. Marwan, F. Kamel, W. Xiang, A demand-side response smart grid scheme to mitigate electrical peak demands and access renewable energy sources, in: Proc. The 48th Australian Solar Energy Society (AuSES), Canberra, 2010.
[19] T.J. Hammons, Integrating renewable energy sources into european grids, in: Proc. of the 41st International Conference Universities Power Engineering 2006, pp. 142-151.
[20] L.F. Ochoa, G.P. Harrison, Minimizing energy losses: Optimal accommodation and smart operation of renewable distributed generation, Power Systems, IEEE Transactions on 99 (2010) 1.
[21] K. Clement-Nyns, E. Haesen, J. Driesen, The impact of vehicle-to-grid on the distribution grid, Electric Power Systems Research 81 (1) (2010) 185-192.
[22] Electric Power Research Institute, EPRy’s Energy Efficiency Initiative, Kansas, 2007, http://my.epri.com/ docs/CorporateDocuments/EnergyEfficiency/EEWebcast0 30807.pdf.
[23] H.A. Aalami, M.P. Moghaddam, G.R. Yousefi, Demand response modeling considering Interruptible/Curtailable loads and capacity market programs, Applied Energy 87 (1)(2009) 243-250.
[24] International Energy Agency, Strategic plan for the International Energy Agency demand- side management program 2004-2009, 2010, www.iea.org.
[25] H.A. Aalami, M.P. Moghaddam, G.R. Yousefi, Demand Response model considering EDRP and TOU programs, in: Proc. of the Transmission and Distribution Conference and Exposition IEEE/PES, 2008, pp. 1-6.
[26] Federal Energy Regulatory Commission, Assesment of demand response and advanced metering, Department of Energy, Washington DC, 2006, http://www.ferc.gov/legal/ staff-reports/demand-response.pdf.
[27] N. Yu, J.L. Yu, Optimal TOU Decision Considering Demand Response Model, in: Proc. of the Power System Technology, 2006, pp. 1-5.
[28] W.C. Chu, Y.P. Chen, T.H. Lin, The competitive model based on the demand response in the off-peak period for taipower system, in: Proc. of Industrial & Commercial Power Systems Technical Conference IEEE/IAS. 2007, pp. 1-5.
[29] D.S. Kirschen, Demand-side view of electricity markets, Power Systems, IEEE Transactions on 18 (2) (2006) 520-527.
[30] C.W. Yu, S. Zhang, T.S. Chung, K.P. Wong, Modelling and evaluation of interruptible-load programmes in electricity markets, IEEE Proceedings Generation, Transmission and Distribution 152 (5) (2005) 581-588.
[31] S. Covino, Demand side response 21st century style. IEEE, 2203, pp. 280-2284.
[32] R. Tyagi, J.W. Black, Emergency demand response for distribution system contingencies, in: Proc. of the Transmission and Distribution Conference and Exposition, 2010 IEEE PES, 2010, pp. 1-4.
[33] F. Rahimi, A. Ipakchi, Overview of demand response under the smart grid and market paradigms, in: Proc. of the Innovative Smart Grid Technologies (ISGT) 2010, IEEE 2010, pp. 1-7.
[34] D.J. Lawrence, B.F. Neenan, The status of demand response in New York, in: Proc. of the Power Engineering Society General Meeting, 2003, pp. 2274.
2. Faculty of Built Environment and Engineering, Queensland University of Technology, Brisbane, Australia
3. Energy Conversion Department Polytechnic, State of Ujung Pandang, Makassar, Indonesia
Received: June 03, 2011 / Accepted: July 07, 2011 / Published: February 25, 2012.
Abstract: Despite several efforts of electrical energy suppliers to mitigate peak demand by adopting smart meters and other supplier-centred smart grid techniques the problem is currently persistent. This research endeavours to present a demand side response(DSR) scheme that enables electricity consumers to engage in tackling the problem by independently observing market conditions and simultaneously acting to mitigate peak demands by shifting loads from peak to off peak periods. This shall assist in achieving improved economic conditions for the electricity infrastructure and financial benefits to the consumer as well. The technique is based on accessing the network’s load profile publicly released by the Australian Energy Market Operator (AEMO) on the internet. The scheme is using programmable internet relays and computer-controlled switches installed at consumer’s premises. Pre-prepared computer software installed on a multi-media tool (CD-Rom) is designed to monitor the state of electricity market on the AEMO website and carryout load control accordingly. The scheme is targeting best economic conditions for consumers, suppliers, electrical generators and the electrical network. To evaluate the scheme simulations have been conducted using a typical demand-price profile for Queensland. The results are indicating the impact of this scheme on achieving energy and monetary savings in electrical consumption. Key words: Demand side response, electrical energy consumption, electricity market, electrical network, smart grid.
1. Introduction
The National Electricity Market (NEM) across the Australian Capital Territory, New South Wales, Queensland, South Australia, Victoria and Tasmania is operated by the Australian Energy Market Operator(AEMO) responsible for the supply and purchase of electricity. Western Australia and the Northern Territory are not currently connected to this market primarily because of their geographic distance from the rest of the market. In order to achieve the high quality service to all customers the AEMO sends price/demand information to the public on the internet every 30 minutes. Electricity users can generally access actual information about demand and price of electricity using internet facilities.
Based on data of December 31, 2008, Queensland total electricity generating capacity was 12487 MW; coal-fired power stations provided 70% of this total capacity, while gas-fired electricity accounted for 17%, renewable energy accounted for around 5%, energy storage 4% and other fossil 4% [1]. These power generations are used to provide electrical energy for all consumers in the Queensland area: residential, commercial and industrial. However, the amounts of energy produced from various generators depend on market demand, price and availability of sources. Fig. 1 illustrates electricity generation in Queensland as on 31st December 2008 [1].
The total Australian energy consumption grew at an annual rate of 2.6% for the 25 years to 1997/1998 [2]. In the 2007-2008 period, the annual electricity consumption in Queensland has grown by over 29% or approximately 10500 GWh making Queensland the second highest consumer of electricity in Australia [3].
Many different economic models are used to represent Demand Side Response (DSR) programs. DSR is divided into two basic categories, namely: the time based program and the incentives based program[24]. The specific types of time based program are: time of use (TOU), real time pricing (RTP) and critical peak pricing [25]; while the specific types of incentive-based programs are direct load control(DLC), interruptible/curtailable (I/C), demand bidding(DB), emergency demand response program (EDRP), capacity market (CAP) and ancillary service (A/S) programs [26]. In the following brief description of four popular market available programs: TOU, RTP, I/C and EDRP. 4.1 Time of Use
Time of use (TOU) is one of the important demand side response programs, which responds to the price and is expected to change the shape of the demand curve [27]. Further on, TOU rate is the most obvious strategy developed for the management of the peak demand in the world, which is designed to encourage the consumer to modify the pattern of electricity usage[28]. For applying this program, the utility does not provide reward or penalty to consumers. To participate, all consumers are required to remove energy consumption during peak session to off-peak session as soon as they receive information from the utility. The type of contract and the rate is fixed for the duration of the contract but it depends on the time of the day [29]. As compared to the flat rate contract some of the risk is shifted from retailer to consumer because the consumer has an incentive to consume during periods when the rates are lower. Fig. 6 illustrates the type of hourly price variation consumers would face under different TOU rates.
4.2 Real Time Pricing
The real time pricing (RTP) program gives consumers to access hourly electricity prices that are
4.3 Interruptible/Curtailable Program
Interruptible/curtailable (I/C) program has traditionally been one of the most common DSR models used by electric-power utilities. In this program consumers sign an interruptible-load contract with the utility to reduce their demand at a fixed time during the system’s peak-load period or at any time requested by the utility [30]. This service provides incentives/rewords to consumers participating to curtail electricity demand. The electricity provider sends directives to the consumers for following this program at certain times. The consumers must obey those directives to curtail their electricity when being notified from the utility or face penalties. For example: the consumers must curtail their electricity consumption starting from 6:00 pm-7:00 pm; those consumers who are following will get a financial bonus/reword to their electricity bill from the utility. In California the incentive of the I/C program was $700/MWh/month in 2001 [23].
4.4 Emergency Demand Response Program
Emergency demand response program (EDRP) is energy-efficient program that provides incentives to consumers who can reduce electricity usage for a certain time; this is usually conducted at the time of limited availability of electricity. EDRP provides participants with significant incentives to reduce load[31]. To participate on this program, all consumers are expected to reduce energy consumption during the events. This program will determine which houses must be included in the event to minimize cost and disruption, while alleviating the overload condition[32]. When asked to curtail, and verified to have performed, the consumer is paid as high as $500/MWh[33]. In New York, emergency demand response program allowed participants to be paid for reducing energy consumption upon notice from the New York Independent System Operator (NYISO [34].
Fig. 8 indicates the importance of the EDRP during a reserve shortage that occurred during July 2002.
This work is presenting a low-cost Demand-Side-Response (DSR) technical concept implemented at user’s premises, which assists electricity end-users to be shifting loads averting peak-demand periods and making use of on-site renewable energy sources. This shall help users to be engaged in mitigating peak demands on the electricity network. The proposed concept comprises a technical set-up of a programmable internet relay, a router, solid state switches and suitable software to control electricity demand at user’s premises (Fig. 9). In addition the scheme allows the user to shift loads from the utility supply to on-site renewable energy sources as available. The softwares on appropriate multimedia tool (CD Rom) developed in framework of this research shall offer users optimized control of energy consumption.
The described concept involves an economic model based on the maximization of financial benefits to electricity users. Additionally the scheme is designed to be targeting the national electrical load to be spread-out evenly throughout the year in order to satisfy best economic performance for electricity generation, transmission and distribution infrastructure. The scheme is applicable in regions managed by the Australian Energy Management Operator (AEMO) covering states of Eastern-, Southern-Australia and Tasmania and other regions in similar conditions.
Usually the electricity price will be high during peak demands and low at off-peak periods. The concept allows users controlling consumption on self-controlled load preferences. In case the user is on other DSR program with the supplier, the scheme is still allowing additional savings besides the benefits and saving already achievable through the DSR agreement.
For commercial and industrial consumers on fluctuating energy prices implementing the scheme will enable achieving immediate financial savings. For domestic consumers on flat-rate tariffs, in contrast, users are gaining financial benefits from reducing energy consumptions at certain times a day; mainly averting peak-load periods. Domestic consumers on different tariffs, where energy price differs with day time and network conditions (e.g., night tariffs), will be gaining financial benefits also by shifting loads from day- to night-times, where electricity is cheaper.
Achievable savings at users end in energy cost $35644.
Scenario 2: Users are shifting peak demand of 711 MWh occurring between 17:00-20:00 pm to the period between 23.30 pm to 00:00 am. Achievable savings in energy cost $53467.
Scenario 3: Users are shifting peak demand of 711 MWh occurring between 17:00-20:00 pm to the period between 02:00 am to 05:00 am. Achievable savings in energy cost $53467.
Scenario 4: Users are shifting peak demand of 711 MWh occurring between 17:00-20:00 pm to the period between 05:00 am to 07:30 am. Achievable savings in energy cost $53467.
Scenario 5: Users are shifting peak demand of 711 MWh occurring between 17:00-20:00 pm to the period between 07:30 am to 10:30 am. No savings in energy cost due to applicable day-time tariffs. However, the scheme was still able to remove congestions out of peak demand area.
Scenario 6: Users are shifting peak demand of 1078 MWh occurring between 10:30 am-20:30 pm. All participants are suggested to set-up the electricity profile to stop some appliance to run during that time. Users can run chosen appliances between 20:30 pm-01:30 am. Achievable savings in energy cost $46323.
The proposed DSR scheme represents an integrated energy model, which enables electricity end-users an automated control of energy consumption and optimized use of renewable energy sources. The main purposes of this control is for users to be averting peak-demand periods on the electrical network helping thus to mitigate detrimental impacts and risks of heavy congestions. The scheme is securing financial and energy savings to domestic, commercial and industrial consumers on fluctuating energy prices. It is helping engaging electricity consumers to be contributing solving the peak demand problem on the electrical network in Australian States covered by the Australian Energy Management Operator (AEMO) and other electricity markets in similar operating conditions.
[18] M. Marwan, F. Kamel, W. Xiang, A demand-side response smart grid scheme to mitigate electrical peak demands and access renewable energy sources, in: Proc. The 48th Australian Solar Energy Society (AuSES), Canberra, 2010.
[19] T.J. Hammons, Integrating renewable energy sources into european grids, in: Proc. of the 41st International Conference Universities Power Engineering 2006, pp. 142-151.
[20] L.F. Ochoa, G.P. Harrison, Minimizing energy losses: Optimal accommodation and smart operation of renewable distributed generation, Power Systems, IEEE Transactions on 99 (2010) 1.
[21] K. Clement-Nyns, E. Haesen, J. Driesen, The impact of vehicle-to-grid on the distribution grid, Electric Power Systems Research 81 (1) (2010) 185-192.
[22] Electric Power Research Institute, EPRy’s Energy Efficiency Initiative, Kansas, 2007, http://my.epri.com/ docs/CorporateDocuments/EnergyEfficiency/EEWebcast0 30807.pdf.
[23] H.A. Aalami, M.P. Moghaddam, G.R. Yousefi, Demand response modeling considering Interruptible/Curtailable loads and capacity market programs, Applied Energy 87 (1)(2009) 243-250.
[24] International Energy Agency, Strategic plan for the International Energy Agency demand- side management program 2004-2009, 2010, www.iea.org.
[25] H.A. Aalami, M.P. Moghaddam, G.R. Yousefi, Demand Response model considering EDRP and TOU programs, in: Proc. of the Transmission and Distribution Conference and Exposition IEEE/PES, 2008, pp. 1-6.
[26] Federal Energy Regulatory Commission, Assesment of demand response and advanced metering, Department of Energy, Washington DC, 2006, http://www.ferc.gov/legal/ staff-reports/demand-response.pdf.
[27] N. Yu, J.L. Yu, Optimal TOU Decision Considering Demand Response Model, in: Proc. of the Power System Technology, 2006, pp. 1-5.
[28] W.C. Chu, Y.P. Chen, T.H. Lin, The competitive model based on the demand response in the off-peak period for taipower system, in: Proc. of Industrial & Commercial Power Systems Technical Conference IEEE/IAS. 2007, pp. 1-5.
[29] D.S. Kirschen, Demand-side view of electricity markets, Power Systems, IEEE Transactions on 18 (2) (2006) 520-527.
[30] C.W. Yu, S. Zhang, T.S. Chung, K.P. Wong, Modelling and evaluation of interruptible-load programmes in electricity markets, IEEE Proceedings Generation, Transmission and Distribution 152 (5) (2005) 581-588.
[31] S. Covino, Demand side response 21st century style. IEEE, 2203, pp. 280-2284.
[32] R. Tyagi, J.W. Black, Emergency demand response for distribution system contingencies, in: Proc. of the Transmission and Distribution Conference and Exposition, 2010 IEEE PES, 2010, pp. 1-4.
[33] F. Rahimi, A. Ipakchi, Overview of demand response under the smart grid and market paradigms, in: Proc. of the Innovative Smart Grid Technologies (ISGT) 2010, IEEE 2010, pp. 1-7.
[34] D.J. Lawrence, B.F. Neenan, The status of demand response in New York, in: Proc. of the Power Engineering Society General Meeting, 2003, pp. 2274.