IEEE PES PowerAfrica 2007 Conference and Exposition Johannesburg, South Africa, 16-20 July 2007
Important Parameters for Prediction of Power Loads - A Bottom-Up Approach Utilizing Measurements from an Automatic Meter Reading System F. Wallin, C. Bartusch, E. Thorin and E. Dahlquist Department of Public Technology, Mälardalen University P.O. Box 883, SE-72123 Västerås, Sweden Phone: +46-21-101300, Fax: +46-21-101370 Email:
[email protected],
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Abstract –In Sweden the deregulation of the electricity market has increased the demand of efficient and accurate short term prognosis. Top-down models are today widely used for both planning and prognosis. The top-down models are known as accurate and effective when working with data in a well known domain, but can have problems estimating new trends in consumption patterns and in other situations with a limited access to reliable consumption statistics. In a bottom-up approach a stronger physical relationship can be implemented and individual parameter adaptations can be made. Increased knowledge on an individual consumption basis become more important to be able to increase the accuracy of such tool, and the extensive installations of automatic meter reading systems provides the necessary consumption data for these new models. In this paper an analysis method of individual customers is presented, a bottom-up approach is described and important parameters for forecasting and simulation are discussed.
I. INTRODUCTION In Sweden the deregulation has changed both the prerequisites and the incentives for the actors in the electricity market. The peak load capacity has been reduced due to the lower price margins on electricity with possible risk for power shortage under strained conditions. The physical planning has therefore become even more important as a tool to manage a stable price control. The long term planning is based on weather forecasts, predictions of the fuel price developments and available energy resources on an annual basis. In the short term perspective top-down models are used by the electricity companies when establishing electricity consumption prognosis. In fact, top-down models are the dominating methods implemented in software tools used in the energy market for both planning and prognosis. The reason for using this method is that the top-down tools are known to be effective and accurate within a well defined range of energy consumption statistics and temperature statistics. Another reason would be that there are no other possibilities because until now there has been a lack of detailed measured consumption data, both in the electricity system as well as in the district heating system. However, in situations with very cold weather conditions the relations may not be linear, especially because of the influence of the many
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new installed heat pumps. In a report covering the state-ofthe-art, it is stated that the basic knowledge of the electricity demand in extreme weather conditions is missing. The lack of knowledge is considered a problem since these circumstances often coincide with high electricity prices in the power exchange market [1]. Existing top-down models may not be good enough for modeling new consumption trends or investigating the impacts of new technical solutions [2]. The top-down models are limited due the lack of reliable consumption statistics. Several works have started to utilize bottom-up approaches when creating software tools, both for modeling and simulation [3] – [5], and report interesting results. The work presented in this paper aims to describe a bottom-up approach for a simulation tool utilizing the largescale installations of automatic meter reading (AMR) systems in Sweden, and to identify important input parameters needed in order to improve the accuracy of such models. This paper will be structured as follows: in section 2 the extensive installations of automatic meter reading systems are discussed and available information from previous made inquires are presented. In section 3 the analysis of individual residential consumption in terms of; base load, heating loads and household consumption is presented. Section 4 present a bottom-up approach and suggests how to relate individual analysis to physical properties. In section 5 discussions of e.g. advantages and drawbacks with these new models are discussed and the possibilities to utilize conducted inquires to validate the outcome. II. AUTOMATIC METER READING SYSTEMS
A. Installation of automatic meter reading systems The electricity market in Sweden has been deregulated since 1996, and as a direct consequence of the reformed electricity acts, the production and sale of electricity were separated from the transmission part. To improve the market mechanisms further, a reformation of the electricity metering regulations where made in 2003 [6]. A major part in the revision concerned the time interval in which the electricity
transmission companies should be obligated to read off the electricity metering devices. As a result of the approved electricity metering regulations, all customers with fuses above 63 ampere require an hour-based settlement of their electricity consumption, implemented no later than the 1st of July 2006. Further, before the 1st of July 2009, the electricity transmission companies are obligated to read off all their customers’ electricity consumption every month. It is assumed that the electricity companies will utilize the collected electricity metering data as a basis when charging the customers. The new electricity metering demands have resulted in extensive discussions about the new automatic meter reading systems. The Swedish Energy Agency estimates that the total national cost of these large-scale investments in metering systems will reach up to 1.4 billion USD and will cover 5.1 million points of measuring [7]. B. Detailed electricity consumption data The data used for the analysis in this work is collected from Smedjebacken, a small town in the middle of Sweden. There are totally about 5000 electricity customers in the area and all of them are having hourly meter readings, collected each night. The data is collected through power line communication; counters are installed at the customer site and collected via repeaters, into the central metering value system. About 10 % of these customers have earlier been encouraged to respond to a comprehensive questionnaire. The information has been used to identify properties for the selection of the initially customers included in the study. One common property for all customers included in the analysis is that they have electricity based heating systems. Access to extensive electricity consumption data will later be crucial to evaluate the large-scale possibilities for implementing the proposed method. Electricity consumption statistics from four different network areas are available. The cities are Sala, Smedjebacken, Södra Sandby and Västerås, represented by the local electricity network owners; Sala Heby Energi AB, Smedjebacken Energi Nät AB, Skånska Energi AB and Mälarenergi AB, respectively. C. Detailed household data The survey comprised some 2 000 households and the ratio of respondents reached a little less than 50 %. The questionnaire included 56 questions about the property, the heating system, the stock and usage of electric domestic appliances, the household composition and the family members’ energy related behavior. This data set was supplemented with variables related to the annual electricity consumption and load demand. The latter were represented by several load factors, which are defined as follows:
LF =
L avg L max
(1)
where Lmax correspond to the peak load demand in a given period, Lavg stands for the average load demand in the same period of time according to:
Lavg =
1 n ∑ Ei n i =1
(2)
where Ei embodies the energy from hourly meter readings for n number of hours in the period. In total, the database contains approximately 200 variables and offers a unique opportunity to further improve the exactness of the forecast model. In one paper discussing residential electricity consumption [8], an attempt was made to estimate and explain variations in the profiles. The analysis included individual, residential electricity demand using traditional statistical methods based on a micro database consisting of more than 1 600 observations on individual households. The authors did, however, not have access to any hourly meter readings and one of the main conclusions drawn was that, based on annual electricity consumption, it is only possible to explain a small share of the total variations in residential electricity demand. In that sense the dataset at hand has a greater potential for a more precise assessment and account of consumption pattern variations. III.
INDIVIDUAL ELECTRICITY CONSUMPTION
The electricity consumption data used in the analysis is, as mention above, collected from buildings with electricity as a main heating source. There are 15 customers, with a yearly electricity consumption between 25 000 kWh and 32 000 kWh, who have been analyzed and included in this work. If a national average had been used, the domestic use of electricity would amount to 6 100 kWh/year, and this applies for detached houses [9]. There are different ways of disaggregate the residential consumption. In previous work several suggestions are provided; to combine load data and information of e.g. customer appliance ownership and assumptions of daily behavior patterns [10]. Further, pattern recognition of appliances use could also be implemented to disaggregate total electricity consumption data into major end-uses [11]. This could also be referred to as the energy or electrical signature of the utilization. However, one problem with the suggested methods is that a more detailed metering series would usually be required. Instead of hourly-based intervals, typical a resolution between 5 and 15 minutes is used, and it could therefore arise some problems if implemented.
A. Individual heat load analysis In order to analyze the individual heating demand it is necessary to identify periods with no or low impact of other electricity loads. It is assumed that consumption data in nighttime, within the hours between 01 and 04, will have small influences from household electricity loads. The electricity consumption data in these hours consist of two different parts; a base load and a temperature dependent load. The electricity base load can be identified in the warmer periods when impact of both heating loads and household loads are low, typically in the summer nights. Appliances with a continuous operation are represented in the base load, e.g. freezers and coolers etc. Fig. 1 illustrates how a typical residential consumption profile for a building with an electrical space heating system. The electricity consumption is depending of the outside temperature.
It is also possible to identify the temperature breakpoint, where the electricity consumption starts to increase with decreasing outside temperatures (see Fig. 1). The intersection of the two graphs can be used to identify the actual temperature, Tlim, for each construction when the daily generated energy within the building is not sufficient to maintain the requested inside temperature. This temperature limit is dependent of the indoor temperature, and the ratio between free heating loads and total specific heat losses.
Tlim = Tin −
L free Q heat
(4)
where the subscript free denotes the so called free heating loads consisting of heat generated from e.g. base load devices, human beings and electrical appliances within the building. B. Individual household load analysis
Fig. 1. Night consumption data, in the hours between 01 and 04, is used when analyzing heat demand. The intersection between the inclined line and the flat line illustrates the breakpoint where heating is needed.
The horizontal line in the illustration above indicates the minimum load level on an hour basis, kWh/h. This base load, Lb, is assumed to be in an operational mode 8760 hours each year. The second line in (see Fig. 1) illustrates the regression analysis preformed to evaluate the temperature dependency of the load. A linear dependency is assumed between the load and the outside temperature, which also is frequently used in the literature [12]. The heating load demand, LT, for a building will be described using a first order linear polynomial:
L T = C1Tout + C 2
for Tlim > Tout
LT = 0
for Tlim ≤ Tout
(3)
Analysis suggests that the heating system, the size of the living area and the number of family members have the most significant influence on the electricity consumption [13]. These variables do, however, only account for a limited share of the variances in individual annual consumption. These variances can partly be explained by factors such as the occurrence of supplementary heating devises, such as an open fireplace or a stove, heat pumps, underfloor heating and energy preserving installations, such as supplementary insulation, triple-glazed windows and tanks for accumulation of heat. Further analysis have, however, revealed that the considerable variations are primarily explained by variables related to individual household behavior, such as the frequency and durance of the family members’ showers and baths, the time the family members spend at home, the usage of standby functions, airing habits and the placing of furniture. These findings are also confirmed by the results of a Danish study [14], which aimed to estimate the impact of energy related behavior on the level of the electricity consumption. The households included in the study lived in the same housing area that was built in the same period of time using the same building techniques and materials. The findings revealed substantial variances between households, and 60 % of these were proved to be related to individual behavior. Several methods have been suggested to refine electricity metering data from AMR systems [15]. Combining daily energy consumption values and a one peak load measurement per day have proven to be enough to recalculate the consumption patterns for a set of 300 residential customers on hourly basis.
Experiences from the work can be summarized in three important parts: -
Boundary conditions need to be defined before distributing the power loads. Household consumption signatures could solve problems predicting adjacent power loads. A calendar function could be used to identify days with consumption patters and levels deviating from normal.
To determine boundary conditions a careful analysis of hourly-based loads needs to be performed. The warmer summer months, including May and September, are suggested as a suitable period, especially due to low occurrence of cooling loads in Sweden. The remaining consumption values could therefore be divided a in base load part and a household load part, respectively. The night hours have already been used, as discussed above, to identify the individual base load of each customer. However, the daily variation in the summer period covers both of the two loads, and should therefore the base load be subtracted from the diurnal profile in order to reveal the household consumption part. A useful parameter to describe the individual household consumption would be the number of occurrences of daily maximum loads for each hour of the day. This parameter would indicate the likelihood for a peak load to occur a specific hour. The same analysis should be performed for the second largest, third largest, and so forth. This analysis could be performed grouping the outcome in weeks, day of the week, working days, weekends, etc. The maximum peak load reveals information about the nature of the electricity usage. An extensive concurrent usage of electrical appliances will of course generate higher peak loads. In combination with the load factor, described in section 2, an understanding of the frequency of this behavior could be obtained. The load factor will generate a value closer to one if the average consumption is relatively high. Consumers with low energy consumption and high peak loads will generate values closer to zero. IV.
BOTTOM-UP MODEL
In order to represent residential consumers in a modeling point of view it is important to have a representation of the most important features. A component-based or object-based approach as a programmatically design structure would here be suitable, also investigated in [16]. The first step in implementing a bottom-up residential building model is to set up the standard energy balance equation for each building component:
∂E E& in + E& gen + st = E& out ∂t
(5)
where ö denotes the energy for each time unit. The subscript in indicates an energy transport into the building. The subscript gen represents internal generated energy from e.g. heating devices. Further, the subscript st indicates the heat storage inside the building. Finally, out denotes energy losses from the building due to heat transport through the building, warm water consumption and controlled and uncontrolled ventilation. Energy generated from human metabolisms, computers, cooking, laundering and other social activities is also considered as internal generated energy. There are today no measurements separating the consumption within the residential buildings. However, the electricity used for social activities or household activities will finally, after the use, be transformed to heat and reducing the load of the heating system for that actual time. The load of the heating system is determined by the main components; transmission heat losses via the outer shell of the building and ventilation losses due to controlled and uncontrolled ventilation. The specific heat loss due to ventilation parts are represented as a product of air flow, density and specific heat capacity shown in the following equations.
Q uv = q uv ρ air c p,air
(6)
Q fv = q fv ρ air c p,air (1 − υ)
(7)
where the subscripts uv and fv denotes uncontrolled ventilation and forced ventilation, respectively. The term (1υ) represents the heat recovery that can be achieved by e.g. heat exchangers on the ventilation outlet. In similarity with electrical conduction, by analogy with Ohm’s law, a thermal resistance can be associated with the transfer of heat e.g. through a wall (see Fig. 2). In general, when a wall consists of several composite layers it is suitable to utilize thermal resistances to represent the heat transmission, including both convection, conduction and if necessary heat radiation. The thermal resistances are then formed as a serial thermal circuit where the temperature differences, i.e. similar to potential in voltage, are the driving forces for an increased heat flux. However, with a limited knowledge of separate construction types within the calculation set and to reduce the amount of parameters it is convenient to use the overall heat transfer coefficient, U, when describing the average heat transfer for each building.
L soc =
∑L
electr _ dev
(12)
With the additional assumption that the solar radiation is covered by a revised form of outdoor temperatures, so called equivalent temperatures, and an instantaneous impact from outside temperature variations the energy balance can be rewritten as follows.
E& in = 0,
∂E st =0 ∂t
∫
E& gen = L heat + L soc dt
Fig. 2. A wall represented as a thermal circuit in series including the convection and conduction part.
The following relationship between the total thermal resistance and the product of the overall heat transfer coefficient and the area is used to define the specific heat transmission losses, Qtr.
UA =
1 = R tot
Q tr =
∑U A i
1
∑
(8)
Ri
i
(9)
where the subscript tr denotes heat transmission losses. With the specific losses specified in (6), (7) and (9) it is possible to define the specific heat loss coefficient for a building.
Q heat = Q tr + Q uv + Q fv
(10)
The heat load is defined combining the temperature difference and the heat loss coefficient.
L heat = Q heat (Tin − Tout )
(11)
To estimate the total energy losses for the building the social activities must be considered. As mentioned earlier, all activities are finally transformed to heat contributing to a lower load in the heating system, alternatively generating higher air temperatures, so called over temperatures, leading to increased heat losses through the ventilation system. An assumption in this work is that social activities are considered as events consuming electricity separated from the heating, i.e. the consumed electricity within social activities are treated as separate loads generating immediate losses represented by the following term.
(13)
Using the results from the regression analysis in (3) and further replacing the indoor temperature in with the limit temperature, Tlim, from (4), the specific heat loss coefficient could be related to the slope, C1, of the line representing the heat dependency.
Q heat (Tlim − Tout ) = C1Tout
(14)
V. DISCUSSION There are several reasons for implementing a bottom-up model. In Sweden, the conditions have never been more auspicious. This, because of the ongoing installations of AMR systems, which results in an extensive access to electricity consumption values. The main purpose of modeling electricity consumption with a bottom-up approach is not a precise model on an individual level. A model with a good performance on an aggregated level, using a bottom-up approach, could increase the understanding of the different parts of electricity metering series. It is important to understand that there are many difficulties in the implementation of a bottom-up modeling tool. Most of the problems are related to the variations in the electricity consumption activities among households. In contrast to the heating load that, in most cases, correlates strongly with the outdoor temperature, the domestic electricity consumption tends to be very stochastic showing no obvious and clear consumption patterns. The main purpose with our work is to develop and improve methods that can be implemented in an automated environment that only necessitates an automatic metering process. However, it could not be disregarded that models that require extensive household input data increase the possibilities to improve the accuracy of the forecasting and simulation on an individual basis [5], [17]. The figure below illustrates interesting input data that would be useful to include for an improved possibility to individually disaggregate the power load (see Fig. 3).
NOMENCLATURE A c C E ö L LF MVS q Q R t T U
Fig. 3. Schematic overview illustrating phases where input parameters that could improve both forecasting and simulation. Additional input parameters are illustrated with dashed lines.
Increased knowledge of the construction parameters would provide opportunities to calculate and determine additional properties, and therefore improving the functionality of a model. For example, knowledge about indoor temperature makes it possible to determine or estimate the free heating loads, given in (4). Further, knowledge of heated area and year of construction could be used to separate the heat losses due to transmission and ventilation as described in (10). Another problem is to determine the amount of electricity consumed by electrical appliances that in turn contributes to the heating of the building. Gain factors have been suggested [18] to account for the generated heat from other activities, separated from the heating system. In additional work, including 7 single-family buildings, the gain factors were estimated between 40 % and 50 % [19]. An expected advantage and a great strength with all bottom-up approaches are the possibilities to perform largescale system studies combined with the ability to follow up the outcome with detailed analysis on an individual customer level. This has previously shown useful in modeling situations e.g. analysis of the effect of new electricity tariffs in an electricity distribution area, both on a system level and for individual customers [4].
[m2] [J/kg,K] [W] [J] [W] [W] [--] [m3/s] [W/K] [K/W] [s] [ºC] [W/m2,K]
Area of outer shell Specific heat constant Constant Energy Energy per time unit Load Load factor Database for meter readings Air volume flow Specific heat Thermal resistance Time Temperature Overall heat transfer coefficient
Greek letters ρ [kg/m3] υ [--]
Density Recovery factor
Subscripts air electr_dev fv gen heat in lim out st tr uv
Denotes the medium air Electrical appliances Forced ventilation Generated heat Denotes losses from heat Input into a control volume Denotes a limit Output from a control volume Stored Transmission Uncontrolled ventilation
ACKNOWLEDGMENT The authors wish to acknowledge Mälarenergi AB and Eskilstuna Energi & Miljö AB for financial support. Also thanks to Sala Heby Energi AB, Smedjebacken Energi AB, Skånska Energi AB and Mälarenergi AB for providing us with electricity metering series. REFERENCES [1]
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BIOGRAPHIES Fredrik Wallin received the M.Sc. degree in Energy Engineering from Mälardalen University, Västerås, Sweden, in 2001. He is now a doctoral candidate at Mälardalen University working mainly with automatic meter reading systems, electricity end-use, load forecasting and load pricing. Wallin has earlier been involved in developing web-based applications with purpose to analyze energy consumption patterns and to display these to electricity customers. Cajsa Bartusch received the M.A. degree in business economics from the Mälardalen University in 1998. She was employed with the Korean Chamber of Commerce - Korea Trade Center from 1999 to 2000 and the Commercial Section at the British Embassy in Stockholm from 2001 to 2002. Currently she is a doctoral candidate at Mälardalen University, specializing in indirect load control. Eva Thorin holds a M.Sc. degree in Chemical Engineering and received a Ph.D. degree 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 Mälardalen University in Västerås, Sweden and is co-supervisor to Ph.D. students in the area of energy and environmental engineering. Erik Dahlquist received the Ph.D. degree in 1991. He is now a professor in Energy Technology at Mälardalen University. Dahlquist has earlier worked at ABB as project manager and in different managing positions, in both research and business, in the fields of process automation, power technology and energy systems.
