So is the need for automated ... hour-based electricity meter readings stored in the databases ... individual hour-based autornatic meter readings (AMR) to all.
2005 IEEE/PES Transmission and Distribution Conference & Exhibition: Asia and Pacific Dalian, China I
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F. Wallin, C. Bartusch, E. Thorin, T. Bdckstrom and E. Dahlquist increased by 7.6 %o. The residential and service sector
A determining factor for a successful Abstract implementation of a demand-based pricing model or control strategy in electricity markets is not only the effects of peak load management, but also the economical consequences for the utility operator and the end customer. In this economical modelling a subset of 460 residential customers has been implemented in a software tool analysing the economical outcome of three different tariffs. Two demand-based tariffs were investigated and compared with a traditional energy-based tariff. The demandbased tariffs transform the flat income curve into a more complex, due to a stronger economical dependency to the system peak loads. The demand-based tariffs move the revenues to the high-peak period, November - March, and the utility operator gains a good matching between system peaks and distribution of incomes.
Index Terms- Demand-based pricing, demand-response, demand side management, load-based tariff, modelling electricity revenues
I. INTRODUCTION HE deregulation of the Swedish electricity market in 1996 lead to a number of actions that had impact on the future power balance in Sweden. As a direct response on declining electricity prices the installed production capacity started to decrease. In the period 1996 to 1999 totally 2500 MW were shut down by Swedish energy producers due to lower price margins [1]. This corresponds to 10 % of the maximum peak load a normal winter. The reduction of peak load capacity combined with political decisions to phase-out nuclear reactor capacity with an additional loss of 600 MW lead to an even more unbalanced situation. The Governmentowned system operator Svens a Kraftn t interfered in the deregulated market and purchased 1000 MW peak load in order to secure the power balance in 2001 [2].
Despite the downward trend of the Swedish production capacity, the consumption has historically increased by approximately 1.2 % and year in all Nordic countries. In the past ten years, the total electricity consumption in Sweden
This work was supported in part by the Swedish Energy Agency, and the Swedish energy companies MalarEnergi and Smedjebacken Energi Nat AB. F. Wallin, C. Bartusch, E. Thorin and E. Dahlquist are with the Department of Public Technology, Malardalen University, Box 883, S-72123 Vasterds, SWEDEN (e-rmail: fredrik.wall inr(mdh.se, cajsa.bartusch(Thmdh.se, eva.thorin(tmdr.se, erik.dahlquist(mdh.se ) T. Backstrom is with Smedjebacken Energi Nat AB, Gunnarsv. 7, S777347 Smedjebacken,, SWEDEN
(e-mail: tobias.backstrormnseab.smedjebacken.se)
0-7803-9114-4/05/$20.00 ©2005 IEEE.
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represents over 50 = of the total consumption [3]. Numerous of papers have discussed different means of controlling and reduce energy demand in peak periods, known
as demand-side management (DSM). One strategy is direct control that aims to actively disconnect load. In the residential sector DSM has been achieved with direct control on water heaters, heating systems and air conditioning units within the dwellings. Besides developing cost effective and reliable techniques in the necessary systems, additional problems due to the nature of direct control remains to be investigated further, e.g. the payback demand that are shown in several papers [4], [5]. Indirect load control is controlling energy demand in peak periods with strict economical incentives such as time of use (TOU) rates, time of day (TOD) rates or real time pricing (RTP) rates. The benefit of differentiated rates is that they reflect the conditions in the electricity market and. to some extent transfers the risk to the electricity consumer. There are a number of projects testing and evaluating time varying prices described in the literature [5], [6], [7]. The results are varying, but common conclusions are that the effects of differentiated pricing have been relatively modest [7] and somewhat difficult to distinguish from consumption variations related to e.g. temperature variations [6]. It has also been observed that the load reduction of a large number of participating households does not coincide with the system peak [6]. RTP projects demand highly advanced. technical solutions. Communication systems which convey actual real time electricity rates, are crucial. So is the need for automated solutions that react on different prices on a real-time basis in the household dwellings A determining factor for a successful implementation of a demand-based pricing model or control strategy in electricity markets is not only the effects of peak load management. An even more crucial parameter is the economical consequences of the DSM action for the operator's revenues and the customer's costs. In this paper the economical consequences of a demand-based TOU tariff, and particularly the effect of the demand-based tariff on the revenues of the distributing utility operator during one year, are analysed. The analysis is performed with a self-developed software tool utilizing the hour-based electricity meter readings stored in the databases of the electricity distribution company. The work has been carried out in cooperation with the utility operator Smedjebacken Energi Nat AB (SENAB) and the Department of Public Technology, Mllardalen University. SENAB apply individual hour-based autornatic meter readings (AMR) to all
their approximately four thousand residential customers. In the first study presented in this paper, 460 residential customers where analysed, all of them part of a large-scale pricing project that aims to develop a model as regards residential load based. pricing of electricity supply, transmission and energy taxes. The following section gives an overview on the software tool used for the analysis. The third section contains the prerequisites in the calculation and fourth part includes the economical outcome from the calculations of a load-based tariffs. The final sections contain a discussion part and a part of made conclusions, respectively.
the database tables the data format and the resolution of the time series (see Fig. 1). An additional reason for this construction is the safety aspects of not processing original consumption values directly in the operator databases. The database containing the set up parameters and calculation result (see fig. 1, calculation database) is organized in different tables containing customer specific information. All values are related to each other through relational database keys, at the same time securing that each stored value is
absolutely uniqu.e.
The customer information is divided in two different parts, a combined building and household specific pa where information regarding maximum load capacity, tariff type, heating system and. further relevant properties are stored. The second part is related to consumption and. contains both energy consumption values and. maximum peak loads within the actual time period. The software tool is based on a bottom-up model where each customer is represented as a component in the calculation procedures. The programming architecture is componentbased. and. developed in the Microsoft based language C#. The technique is similar to object-oriented programming, e.g. where a building component consists of energy consumption components and peak load components. These components are further divided into separate timestamps with a matching hour-based energy consumption value. The user creates adjusted. price functions and. combines them to accomplish new grid tariffs (grid operator) or electricity prices (electricity trader). The cost function used. in the software is defined as follows:
IL. THE SOFTWARE TOOL
The full-scale project geographically embraces four different local electricity grids owned by three different operators and the study comprises approximately 500 customers in each district. Within the project two different AMR systems are represented; hour-based. meter readings and. meter readings based on 27 hour intervals, respectively. Different operators naturally have various needs regarding the resolution of time series. The same solving techniques are, however, to be applied in all user cases. When the developing work started a number of prope ies were considered to be particularly import ant in order to increase the usefulness of the software tool: * Flexible handling of electricity AMR A set of user levels, suitable for different purposes; research company strategies and customer services * An openly constructed cost function * A web-based interface In order to attain flexibility in the analysis software a separate database has been used to store data related to the calculations. The setup data from the grid operator databases where k, a, b and C represent variables that can be set through the graphical user interface. E respectively L represent the are extracted into the calculation database through adapters. actual metered energy consumption and. the selected. peak load The adapters will pre-process the stored electricity meter The subscripts e and I states t e variables respectively. readings in different ways depending on the construction of dependency to the energy and the peak load part of the cost function respectively. IEl etri city grid operators ___ __
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1Fig. 1. A constituent overview of the software tool, illustrating the metered data flows and user interactions within each part.
2
The first is a traditional tariff, corresponding to the present tariff used by the grid operator. The fixed part is traditionally solely related to the size of the main fuse, 14.0 USD and 21.5 USD respectively per month in the SENAB case. The flexible part is an energy-based fee, consisting of 2.1 cent/kWh. The energy rates are matched with mean load values with hourbased resolution, kWh/h and are further aggregated into an energy tariff model. For a Swedish residential customer with an electrical heating system, 25 ampere main se, and a of 25000 kWh the total cost is 781 USD, yearly consumption where the flexible part amounts to 523 USD. The fixed part results in a cost of 258 USD/year. The flexible part and fixed part answers to 67 00 and 33 % of the total cost respectively. The second tariff is constructed. as a combined. solution between a flexible load-component and a fixed part based on the size of the main fuse. The fixed. part of the loadcomponent tariff is to be kept at a similar level as the traditional energy tariff. The fixed rate is set to 14.0 USD/month. The flexible part is converted to a load-based, differentiated tariff. The tariff is differentiated both in a seasonal peak period as well as a daily peak period. The seasonal peak period is in this case defined as the winter months November through March. The daily peak period is defined as the hours 7 to 19. The rate is set to 5.6 USD/kW in the seasonal peak period between the hours 7 and 19. The offpeak period rate, April through October, between the hours 7 and 19, is set to half the peak rate value, i.e. 2.8 USD. In both cases the flexible component is matched with the monthly mean peak load value based on the three highest peaks each month. In the third implemented tariff the fixed part is completely excluded in favor of a higher flexible rate. The intention is to investigate the impact of a strict flexible construction with higher price per used kilowatt in the seasonal peal period. Again, the mean peak load values used in this case are based on the three maximum peak loads each month.
After having performed the calculations the customer specific information can be used to aggregate results in numerous ways, analyzing dependencies and consequences. One useful product of the calculations is the peak load versus energy consumption. A load factor Lf, is defined as a value between 0 and 1 [9] The load factor shows the consumption characteristics of individual households or an aggregated set of households' values. A value closer to 1.0 indicates consumption with a smoother load curve. The load factor is calculated for each household within the study.
Lag Lf ~LmaxISi In)
2)
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where Lmaximum is defined as the peak load within the period. Lave is defined as the average load in the actual period according to:
~~~~~~~~n j=r1 L jE(
where E~represent the individual hour-based meter readings and n correspond to the number of hours in the actual period. The results from the calculations are stored in pre-designed data tables in a SQL Server and. then extracted to, e.g. Microsoft Excel for graphical presentation. CALCULATIONPREREQUISITESANDDATAINPUTS In this economical modelling a subset of 460 residential customers has been implemented in the software tool. The whereEi in the Numbeert ofe are MxLimiuml Totr-ale installreadi customers subset distributed over the three load Z*_ kW, levelsFh11.1 13.9 kW and 17.3 kW as follows56.50, eulsfrmte houehlds latoladesoedirloa-desgl 41.1 %o and 2.4 %o respectively, which corresponds well to the total composition of the residential customers in the area. (see tab. 1). All customers have hour-based electricity meter readings and totally are over 4 000 000 separate electricity values processed from the year 2003 in the analysis. III,
TABLE 1 A SURVEY OVER THE HOUSEHOLDS INCLUDED IN THE ANALYSIS
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Swedish authorities have provided the utility operators with regulations that states the maximum allowed revenue in each concession area. Each tariff is therefore needed to be adapted within this actual limitation. Three different grid rates have been implemented and modelled in the analysis tool (see tab. 2).
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IV. RESULT
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In figure 2, 3 and 4 the load demand profile is an aggregated series for the whole subset of customers. It consists of consumption data, with hour-based resolution (kWh/h), which correspond to the demand in each hour, over the calculated year. Given the Swedish regulations mentioned above, the total revenues from each tariff must be close to the ideal value of 205 900 USD or 23.5 USD/h over the year
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J M M J S 0 N D F A I A. Revenues from Modelled Tar ifs Month The first modelled energy-based tariff generates regular Fig. 3 Revenues from daily demand-based tariff (tariff 2) and the relations in revenues with a weak economical connection to separate peak time to the load demand profile in the year 2003 loads for the utility operator (see fig.2). The maximum hourbased revenue calculated for the total aggregated set was 47 600 USD/h. When observing ten of the highest peaks over the year 500 their total collected economical contribution ends at 349 USD or 0.2 00 of the yearly revenue. 1000 400 3 0, A division of the economical outcome over the surnmer and winter period results in the revenues 99980 USD and 105900 300 0 2 750 USD respectively which corresponds to 48.5 % and 51.5 % of 0 200 a 500 the total result respectively. rw
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Fig. 4. Seasonal demand.-based tariff (tariff 3) revenues related to the load demand. profile in the year 2003
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B. Customer aspects
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When comparing the classical energy tariff with the most progressive, the seasonal demand-based tariff, several factors
are of interest. For residential customers the economical consequences are identified as the single most crucial factor. The load factor presented above reveals one aspect of the Fig. 2. Energy-based tariff (tariff 1) revenues related to the load demiand customer consumption habits, i.e. the relation between profile in the year 2003. consumed energy and the maximum peak load. Tbe total The second tariff, daily demand-based tariff, modelled in revenue has to be smaller or equal to the level stated in the the software shows stronger economical dependency to regulations, but there are no restrictions as to how the variations in the load-demand profile. This is a result of the revenues are generated within the concession area. Among the initiated load component in the tariff. The highest hour-based studied customers the highest load factor was 0.37 and the revenue amounts to 534 USD (see fig. 3). The economical lowest 0.03. Households with an extremely low load factor are contribution of the top ten system peal loads in 2003 amounts evaluated separately. Common reasons for extreme values are to 3210 USD, which is equivalent to 1.6 00 of the company s e.g. temporary residences such as surmnerbouses with low revenue. The daily demand-based tariff generates greater energy consumption, but a relatively high load demand limited revenues from November to March inclusive. in time. Two customers increase the electricity cost with 460 The third modelled tariff shows an extreme volatile USD/year, corresponding to 19 % and 13 % of their previous dependency towards load variations. The highest peak total cost respectively. generates 866 USD in revenues from the studied set of The maximum savings that are gained amounts to 300 customers. The total revenues from the ten topmost peak loads 330 USD, and these customers are decreasing their cost by 7 amounts to 53 10 USD or 2.6 % of the total revenue. Since the 9 %. Since the greatest savings are made by larger residential tariff is closely connected to the winter months the revenues consumers the saving percentage generally becomes less. increase in the period November to March and totally amount A total of 42 % of the studied households increase their to 206700 USD. The amount exceeds the ideal yearly revenue electricity cost between 10 - 460 USD/year. 9 % of the but the deviation from it is less then one percent. customers do not experience any affect. 49 0 of the customers reduce their cost by 10 - 330 USD/year. I
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The greatest reduction expressed in terms of percentage arrives at 14 % Fig. 5 shows the dependency between the load factor and the calculated savings when modelling a change of tariff. 0.40
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Fig. 5. The difference in customner yearly cost it applying the seasonal demand based tariff instead of the energy tariff Eaeh point representing a eustomer load faetor related to the eeonomieal outeome. V. DiscusS oN
Several utility operators have experienced an increased problem with system peaks tat are rising faster then t e energy demand, relatively. This is a problem since energy is the only flexible par in most of the tariffs used today. This means that the rising peak load demand will be economically uncovered. The traditional energy tariff has been used by the electricity companies for a long time and they are familiar
with revenues that vary with e.g. whether conditions and. holidays. The advantage with an energy-based tariff is the relatively uniformed and regular revenue of the operator. The disadvantage is the total lack of means to restrain system
peaks.
The demand-based tariffs change the flat income curve into complex curve. The revenues are to be remained. at the level stated. by Swedish regulations, but the number of hours that the customers are charged decrease from 8760 bours per year with a traditional energy tariff down to approximately 2000 hours with the seasonal demand-based tariff depending on tariff construction (see fig. 6). a more
The daily demand-tariff needs almost 6000 hours to generate 90 °o of the total revenues in a year. Despite the number of hours reduced in the seasonal tariff, from 8760 to 3600, is it only 40 % in this remaining dataset that actually provides the basis for the load-demand calculations. The daily demand-based tariff has already been implemented in at least one concession area in Sweden. The tariff shows a stronger connection to the system peaks and due to its design it generates peak-based revenues each month over the year. This can be stated as a principally imported property since it is affecting the customer behavior over the whole year. The seasonal demand-based construction shows the absolute strongest connection to the system peaks of the three modelled tariffs. The single highest economical outcome for one hour is 18 and 1 6 times greater then its corresponding maximum values using the energy demand tariff and daily demand tariff respectively. The tariff gives the utility owner a powerful economical means of controlling the customers' load demand under periods coinciding with the system peaks in the observed areas. Since the grid. operators' costs are based on their maximum systems peaks charged by the overhead utility owner it is correct to implement a demand-based tariff for residential customers as well. Fig. 7 illustrates the yearly revenues distributed among the months during one year and how the amount from the seasonal tariff dominates in share of revenues over the high peak months. The energy-demand tariff and the daily demand-based tariff generate unexpectedly similar revenue profiles over the year although the demand-based tariff shows a stronger connection between the economical outcome and peak loads, especially during the five winter months.
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Fig. 7. How the utility operator revenues are distributed over the year 2003. Revenues are divided in; energy-based tariff (white bar), daily demand-based tariff (light grey bar) and seasonal demand-based tariff (dark grey bar).
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Fig. 6. Number of load values (kWh/h) that generates the yearly revenue.
5
6
When implementing new tariffs it is crucial for the company to investigate how their customers are affected. The custorners have different needs and. possibilities to adapt to the new tariffs. This can both be due to physical prope ies within the building, but also social factors, such as family circumstances, work, knowledge, etc. The load factor presented above can provide interesting information, especially if additional conditions of the households are known. Customers with a higher load factor, i.e. a smoother consumption profile, seem to be favoured by the implementation of a demand-based tariff in the area. The dependency became even more evident having performed a linear regression analysis on the set of customer load factors as shown by the straight black line in fig. 5.
V111. BIOGRAPHIES Fredrik Wallin received his M.Sc in Energy Engineering from the Malardalen University, Vasteras Sweden, in 2001EHe is now employed as a postgraduate student at M:alardalen University working mainly in a load pricing pro ect. He has earlier been involved developing web-based applications to analyzing energy consumption patterns and to display these to electricity customers.
Cajsa Bartusch received the M.A. degree in business economics from the Malardalen University in 1998. She was employed with the Korean Chamber of Commerce - Korea Trade Center from 1999 tn 2000 and the Cummercial Sectiun at the British Embassy in Stockholm from 2001 to 2002. Currently she is a postgraduate student at Malardalen University, specializing in indirect load
VI. CONCLUSIONS
Today utility owners are using energy based tariffs when distributing electricity. If implementing a load-demand based tariff the possibilities to control the consumption pattern increase because the economical revenues become more connected to the peak loads within the system. The new demand-based tariffs meet the Swedish regulations as long as the total yearly revenue does not exceed the fixed limit. However, the monthly revenues are varying depending on which tariff is being modelled. The demandbased tariffs move the revenues to the high-peak period, November - March, and the utility operator gets a good matching between system peaks and revenues. Further investigations needs to be done in order to determine the adequate pricing level from the utility operators' as well as the customers' points of view.
management.
Eva Thorin holds a M. Sc. in chemical engineering and a Ph. D. in the field of energy processes from the Royal Institute of Technology in Stockholm, Sweden. She works as a senior lecturer at the Department of Public Technology at Miilardalen University in Vasteras, Sweden and is co-supervisor to PhD students in the area of energy and. envirunmental engineering.
Tobias Backstr6m received his Bachelor Of Electrical Engineering from Dalarna University, Ludvika, Sweden, in 2004. He is now eimployed as an Electrical Engineer at Smedjebacken Energy working full time as respnsihle for the electrical metering system. As a degree pro ect he worked with a prestudy on a wind power station.
VII. REFERENCES [1] Swedish Energy Agency "The Electrici Mar ket 2002" ET 28:2002 2002" (in Swedish) [2] Swedish Energy Agency, The Electricity Marlet 2001 ET 9:2001 2001 (in Swedish)
Agency, "The Electricity Market 2003", ET 10:2003, 2003 (in Swedish) C. N. Kurucz D. Brandt and S. Sim "A Linear Programming Model for Reducing System Peak Trough Customer Load Control Programs", IEEE Trans. on Power Systems Vol. 11 No. 4 November 1996. T. Ericsonn H. Saele and P. Finden "Autumatic Lnad Cntrul uf Residential Electricity Consumptiou", 6th IAEE European Conference Zurich, Switzerland, September, 2004, [Online]. Available:
[3] Swedish Energy
[4] [5]
http://www.saee.ch/saee2004/Torgeir%2oEricson.pdf [6] M. RasaFnen J. Ruusunen and. R. P. Hamalainen, "Customer level analysis of dynamic pricing experiments using consumption-patterm models", Energy, Vol. 20, No. 9, 1995. [7] I. Matsukawa, H. Asano and H. Kakinmoto "Household response to incentive payments for load shifting: A Japanese time-of-day electricity pricing experiment", The Energy Journal, Vol. 21, No. 1, 2000 [8] W. Zhang and A. Feliachi, "Residential load control through real-time pricing signals", Proceedings of the 35th South Eastern Symposium on System Theory pp 269 - 272 2003. [9] J. Pyrkon K. Sernhed, J. Abaravicius and Victoriani Perez Mies, " Pay for Load Demand. - electricity pricing with load demand. component", ECEEE 2003 Summer Study, Saint-Raphae,l France, June, 2003.
Erik Dahlquist received his PhD in 1991. He is now working as a Prof. in Energy Technology. He has earlier worked at ABB for 27 years as project manager and in different managing positions in both research and business in the fields of process autonmation, power technology and. energy systems.
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