Paper accepted for presentation at 2003 IEEE Bologna Power Tech Conference, June 23th-26th, Bologna, Italy
Assessment and Simulation of Demand-Side Management Potential in Urban Power Distribution Networks A. Gabaldón, Member, IEEE, A. Molina, Student Member, IEEE, C. Roldán, J. A. Fuentes, E. Gómez, I. J. Ramírez-Rosado, Member, IEEE, P. Lara, J. A. Domínguez, E. García-Garrido, E. Tarancón Abstract-- The main aim of this paper is to present a national research project that is being jointly carried out by four Spanish Universities. This project is focused on creating and developing methodologies to allow a comprehensive, efficient and flexible management of residential distribution systems, achieving this flexibility in the delivery side by means of intelligent operation of the Demand-Side and a flexible control of electric demand, taking into account the opportunities introduced by new uses in energy and more efficient electro-technologies. The first part of this paper will be devoted to obtain the changes in the load curve for a residential feeder generated by Direct Load Control (DLC). This task will be accomplished through the use of Physically-Based Load Models (PBLM) of loads developed by authors. In the second part we will analyze the impact of power and energy demand reductions of the load curves (using DLC strategies) on the operation of an illustrative power distribution network. Finally, the possibilities of a new tool, Demand-Side Biding for residential users will be presented. Index Terms— Demand-Side Management, Power Distribution, Demand-Side Bidding, Distribution Automation
I. INTRODUCTION
I
N a near future, the Electrical Power Systems will suffer a series of pressures which are going to determine the operation and planning of their most complex level: the distribution of the electric power. From those conditioning items we can quote the following: the continual increase in demand, the deregulation of power markets, the change of the concept of energy, the increasing restrictions from environmental regulations, and the competition from other energy sources, energy storage and dispersed generation in the demand side. The development of new tools and methodologies adapted to these premises may provide to users, utilities, and therefore to the society as a whole, some competitive advantages to deal with these problems. The approach used here to solve the continuous growth experienced in the demand of residential electric systems −see Table I−, is to try to influence the customers to modify their load shape through Demand-Side Management (DSM policies) in order to obtain several objectives: A.Gabaldón is with the Department of Electrical Engineering, Universidad Politécnica de Cartagena, SPAIN (e-mail:
[email protected]) I. J. Ramírez-Rosado is with the Department of Electrical Engineering, Universidad de La Rioja, SPAIN (e-mail:
[email protected])
0-7803-7967-5/03/$17.00 ©2003 IEEE
• • • • •
Minimize peak demand Improve load factor Improve system operation and planning Maximize quality and reliability of service Allow customer participation in new market structure TABLE I. LOAD DEMAND TRENDS IN THE AREA OF MURCIA (SPAIN) Electrical demand Residential Global
1997 3638 GWh 984 GWh
2000 4818 GWh 1255 GWh
Percent. of growth 32,4 % 26,0 %
The most common of all DSM policies is known as Direct Load Control (DLC), in which load portions such are Heating, Ventilation and Air Conditioning Loads (HVAC), Water Heaters (WH) and Energy Thermal Storage Systems (ETS) are under the direct operational control of the utility −or aggregators, in the future deregulated structure of Electrical Energy Systems− taking into account several constraints fixed previously between customers and the Supply-Side. These constraints are very important because during the last decade a lot of control policies have been considered as not acceptable by consumers because the utility analysis has not considered the correct modeling of loads involved in these control strategies −for example, comfort temperature indices during and after the DLC−. Due to this fact it is necessary to develop aggregate models for each load (HVAC, WH and ETS) to have a way of anticipating DLC effects from two points of view: the system load curve modifications and the customer expectations −economy, quality of supply, comfort…−. The precision of this evaluation depends on several factors; among the most important are the elemental load model which has been used and the percentage of each end-use in our system. The next paragraph deals with the residential system characterization, based on available data and data recorded from distribution transformers (CTs). The rest of the paper has been organized as follows: a description of the method used to obtain main end-uses load composition is presented in section III. Section IV is focused in a description of PBLM models and aggregation techniques for simulation purposes. These models are also applied to show their suitability in several DLC scenarios. In section V an analysis of the effects of DLC strategies in the distribution network is presented, analyzing basic variables and the computer results. Finally an insight about Demand-Side Bidding (DSB) is presented, [1].
II. RESIDENTIAL DISTRIBUTION SYSTEMS ATTRIBUTES
Current (A)
1
Current (A)
To simulate DSM policies, we have selected a typical distribution system located in the North of Spain, in La Rioja. The 13.2 kV power distribution system corresponds to a new residential square, and it is part of a typical residential area in Spain. Twenty seven CTs branch off the main feeder to supply at customer low voltage, normally 400/230V. Nowadays, and for some representative CTs, the demand is being monitored to characterize demand patterns. The results in CT number 15 are shown as an example in Table II for main end-uses.
Imax + Imin per period
5
0
0.5 0 -0.5
-5
0
0.5
1 Time (s)
1.5
2
0
2
4 Time (s)
6
Fig. 1. Current waveforms absorbed by an HVAC and its IMMP signal
TABLE II. END-USE EQUIPMENT FOR CT-15 Load HVAC Fridges/freezers WH Clothes washing Lighting PC/TV/HiFi
Residential customers Rated Power (kW) 135.8 23.15 14.4 82.8 86.6 16.0
Nº of appliances for DSM 64 64+64 12 -
To evaluate DSM policies is necessary to know the percentage of load switched-on by user. The process to obtain this data is described in the next section. III. CONTROLLABLE LOAD ESTIMATION In the special situation where there is an interest in only a small subset of loads, i.e. HVAC loads the Load Research program has to meter, on a sample of customers, the energy and demand for these specific loads. The method has the great barrier of its cost, because these studies are expensive. Also, for evaluation purposes, there is a need to measure the energy and demand savings associated with the Load Control program, so as to realize the effectiveness of the program. For the previous reasons, there is a need to obtain direct or indirect measures of the total power absorbed by these loads. In order to obtain an indirect measure of the total load absorbed by these thermal storage loads, measures of the current absorbed, during several minutes, were made of various air conditioning and refrigerators. In Fig. 1, the current absorbed by air conditioning equipment can be seen for a six seconds period. We measure the maximum and minimum current absorbed, and draw the values obtained after adding them and represent it along the time, the resultant graphic could also be seen in Fig. 1 (Imax + Imin per period, IMMP). The resultant is also a sinusoidal wave, but of a different frequency. The frequency of the IMMP varied, for the rest of the loads, in a small range between 1Hz and 3Hz. The question is: can the measure of the IMMP signal be used as an estimation of the total air conditioning load absorbed at that point?. In order to study its suitability, a test was made where the total load absorbed, for a single circuit, at the secondary of a CT was measured. The results of the test could be seen in Fig. 2.
Fig. 2 Power absorbed by an aggregation of loads and its IMMP
The results obtained, for the measured IMMP signal, shows that there are three peaks, which are placed around the expected frequencies (1-3Hz) for the air conditioning and refrigerators loads, so it could be used as an estimation of the controllable load if we obtain a relation between the IMMP signal, generated by an aggregation of loads, and the total controllable load present in that aggregation. IV. LOAD MODELING AND AGGREGATION PROBLEMS A. Elemental Models The first problem to be solved in load modeling methodologies is to model the load at the elemental level. In HVAC appliances, we have proposed a stochastic state-space equation system which reflects an energy balance between the outdoor environment, the indoor environment and the load (heat conversion from electrical source), [2] [3]. The thermal exchange is explained by a system of stochastic differential equations such as: 3
dX t = ( A ⋅ X t + B ⋅U t ) ⋅ dt + ∑ dWt ,
(1)
where Xt is the vector of state variables (dwelling temperatures), Ut is the input variable vector (heat generation through external or internal sources) and dWt are a set of Wiener stochastic processes. The need for including dWt is driven by the assumption that the loads do not share the same values of model parameters (Aij, Bij). B. Aggregation Process The aggregation problem consists on describing approximately the expected value of the total power demand due to the group. This problem can be solved using different
mathematical tools: Fokker-Planck equations, EulerMaruyama discretization or kernel estimators. Each method has its own advantages and drawbacks, and they will be explained in this work. Also, through the last alternative we can improve the previous methodologies by using smoothing techniques. The results for internal temperature –correlated by load demand− are shown in Fig. 3, where we notice that the density distribution function of the group suits well a normal, −see [4] for additional information−. 0,9
3
0,8
2,5
0,7 0,6
2
0,5
1,5
0,4
1
0,3
0,5
0,2 0,1
0 24,4
24,6
24,8
25
25,2
25,4
0 21
Indoor temperature (ºC)
21,5
22
22,5
23
23,5
24
24,5
25
Indoor temperature (ºC)
Fig. 3. Euler-Maruyama discretization and Kernel aggregation methods
C. Simulation Example of DLC Before implementing DLC policies, it is advisable to forecast the demand and indoor temperature evolution for the customer under these control actions. As it has been shown, the loads suitable for these LM control applications must imply some kind of energy storage, so that the control will not completely destroy the service to be supplied to our residential customer. In our case these loads are limited to CT air conditioning loads. The HVAC load composition is obtained through measurement in CT-15, from 0h to 24h. Some important parameters affecting the results are: the duration of cycling action, the duration of the total control period and the time of the day in which the control is implemented. The effects of some cycling control actions have been tested on the same CT-15. The load was structured in ten control groups –the same ON/OFF time periods– to minimize peaks and payback during control, and these effects have been filtered through a sophisticated control action, allowing some longer connection time as the final control period approaches. It can be observed in Fig. 4 that over 5% peak powers saving is obtained.
Fig. 4. Control of HVAC appliances in CT-15
V. EFFECTS IN THE POWER DISTRIBUTION NETWORKS A. Basic Variables and Summary of the Power Distribution Network Modeling The basic variables of demand side control are: • p. The peak power reduction level, expected peak power of the load demand after the direct load control is implemented, divided by the peak power without DLC. • e. The energy reduction level, defined in a similar way, but for electric energy • f. The load factor improvement ratio, using the same structure as before. The most interesting situations correspond, obviously, to the ones with improvements of load factors, f, larger than 1 (the basic situation represents the distribution system with no DLC, that is, p=1 and e=1). For illustrative purposes, we have used a modeling of the primary distribution networks similar to the one presented in [5]-[7], but only considering the variable costs of operation of such system. Thus, the model represents also peak power losses in the distribution feeders, power flows and voltage values at the distribution nodes (where power transformers high voltage/low voltage are situated) and it considers the peak power demands at the nodes, the Kirchhoff´s laws, the power capacity limits (for the feeders and the substations), and the voltage drops limits (the average and largest voltage drops) of the nodes, CTs mentioned in Section II. Therefore, from the model and the data we obtain several additional results of interest: the transformer peak power losses, the distribution system peak power losses, the transformers energy losses, the feeder energy losses, the distribution system energy losses, the average voltage of the nodes and the lowest voltage of the nodes (for the peak power demands). B. Computer Results A large set of computer simulations of diverse DLC strategies have been carried out in order to analyze technical and economical effects in the operation of the distribution systems, using the above modeling of such systems. In this paper we present a summary of some illustrative results for the residential 13.2 kV power distribution feeder previously described in Section II. After the simulation of the basic situation (p=1 and e=1), we have obtained the results of several simulations of demand side control, corresponding to the values of p and e of the Table III, with an improvement of the load factor, f, larger than 1 (with respect to the basic situation). Table III shows the values of the operation economical costs of the feeders, in percentage, with respect to the basic situation. Furthermore, for illustrative purposes, Table IV and Table V respectively contain the energy losses and the peak power losses of the distribution system, with respect to the basic situation. Finally, the average voltage drops, c.dtm, and the lowest voltage drops of the nodes, c.dtp, are indicated in Table VI, in percentage, with respect to the basic situation.
TABLE III. COSTS p.
1.0
0.99
0.98
0.97
0.96
0.95
0.94
e. 96.02
95.63
95.24
94.88
94.46
0.99
97.99
97.59
97.20
96.81
96.45
96.02
1.00
100.00 99.58
99.18
98.79
98.39
98.02
97.59
0.98
TABLE IV. ENERGY LOSSES p.
1.0
0.99
0.98
0.97
0.96
0.95
0.94
e. 96.68
96.36
96.03
95.72
95.38
0.99
98.33
98.00
97.67
97.34
97.03
96.68
1.00
100.00 99.66
99.32
98.99
98.66
98.34
97.99
0.98
TABLE V. POWER LOSSES p.
1.0
0.99
0.98
0.97
0.96
0.95
0.94
e. 96.38
94.60
92.84
91.11
89.37
0.99
98.17
96.38
94.60
92.84
91.11
89.37
1.00
100.00 98.17
96.38
94.60
92.84
91.11
89.37
0.98
TABLE VI. VOLTAGE DROPS 0.99
0.98
0.97
0.96
0.95
0.94
100.00 98.97
97.97
96.97
95.96
94.98
93.96
c.dtm. 100.00 98.97
97.98
96.98
95.96
94.99
93.97
p. c.dtp.
1.0
Notice that always the operation economical costs of Table III and the distribution system energy losses of Table IV are decreased in all the control situations −with respect to the basic situation−, especially when lower values of e are considered. Furthermore, the voltage drops (average and lowest voltage drops) of Table VI and the peak power losses of the distribution system of Table V are always reduced under all the demand side control situations, especially when lower values of p are achieved. Suitable lower values of p could lead to delay some future investments in distribution systems. Therefore, from these results −and from additional extensive computer results−, not included in the paper, the DLC policies can lead to significant improvements in energy savings and in economical cost savings, as well as improvements in the quality of the electric service. VI. DEMAND-SIDE BIDDING In the European Union the electricity and gas sectors are being restructured to a new framework: deregulation. This process supposes a change in the relationship of utilities and their customers. Unfortunately, and despite news in the press and elsewhere that suggest otherwise, the number of those products, which have been brought into competition for small users (residential and commercial), appear to be quite small in the future. The only way to participate in new deregulated (or re-regulated) markets appears to be, for residential users, through energy aggregators and Demand Side Bidding (DSB), [8].
Thus some promising initiatives, and pilot projects, have been tested in the last three years: the so called Demand Responsive Policies (DRP), [9] [10]. These policies are the way to promote some customer participation in the new electricity market (at present limited to medium and large customers). DSB and DRP are changing the traditional concept −1985− of DSM from “policies driven by utilities to obtain a change in consumer demand” to policies where the customer is the main driving force to change its own demand. Also, these DRP policies are far from the traditional DSM objectives and drawbacks. From the user viewpoint, DRP policies will be focused on the customer time of use rather than the magnitudes of electricity, searching for customer benefits −for example, through participation in ancillary and settlement markets−. From societal viewpoint, the greater elasticity of demand in some users will benefit significantly all the users, because the price will be lower, being participants or not −equity principle, usually not reached in older demand side programs−. From environmental point of view, some older expensive and pollutant generation plants will not be necessaries. But also, policies to achieve flexibility in customer demand present benefits for electrical energy systems because this “negative demand” or “distributed generation” should be an interesting reliability tool in the near the future. At present, the rate of market penetration of these policies are very limited, in spite of the fact that deregulated electricity industry incentives for demand response seem to be greater than in the old traditional industry. The main barriers for DSB are: • The necessity of improved metering, communication and computer technologies to perform bids. • The lack of a clear policy from governments and System Operators to include demand and supply policies on an equal foot, [10]. • The necessity of engineering tools to evaluate bid availability The first barrier should be overcome. In our days such a communication and control system may have applications from beyond electricity affecting the control of a lot of parameters related to user activities −heating, lighting, ventilation, maintenance…−, or also affecting natural gas and water uses. The demand and supply participation in the new markets on an equal basis is only a political question, because this premise is a basic rule for any free market. VII. CONCLUSIONS PBLM models of HVAC devices have been developed and assessed. The simulations have been focused on the behavior of these models under DLC control actions. Among their main advantages, we emphasize a high accuracy and complete information about the physical magnitudes, for example the indoor temperature evolution. For each end-use, the percentage of demand is obtained through non intrusive measurement, using the IMMP signal. The basic variables for
DLC simulation and the used modeling approach of power distribution networks have also been described. The extensive computer results indicate that suitable DLC strategies can lead to significant economical and technical benefits in urban distribution systems. As conclusion, DLC strategies can achieve positive effects in peak power losses reductions, energy savings and economical savings, as well as their positive effects in the improvement of the quality of the electricity supply. Finally, this paper tries to present an engineering tool to evaluate the magnitude and elasticity of a customer demand curve through direct DLC, and in this way a mechanism to improve the customer or aggregators ability to perform demand bids
VIII. ACKNOWLEDGMENTS The work described in this paper is financially supported by the Ministerio de Ciencia y Tecnología of Spain, through the research project DPI2001-2779-C02, as well as to thank the European Union for its support.
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International Energy Agency. “Demand Side Bidding in a Competitive Electricity Market”. Tech. Rep. Task VIII , 1999-2002 [2] C. Álvarez, R. P. Malhamé, and A. Gabaldón, “A class of models for load management application and evaluation revisited,” IEEE Trans. Power System, vol. 7 (4), pp. 1435-1443, Nov. 1992 [3] A. Molina, A. Gabaldón, J.A. Fuentes, C. Álvarez, “Implementation and assessment of physically based electrical load models: application to direct load control residential programmes,” IEE Proceedings on Generation, Transmission and Distribution, vol. 150 (1), pp. 61-66, 2003 [4] A. Molina, A. Gabaldón, M. Kessler, and J.A. Fuentes. “Application of smoothing techniques to solve the cooling and heating residential load aggregation problem”. in Proc. 2002 Probabilistic Methods Applied to Power Systems, pp. 879-885 [5] I. J. Ramírez-Rosado, and J. L. Bernal-Agustín, “Reliability and costs optimization for distribution networks expansion using an evolutionary algorithm”, IEEE Trans. Power Systems, vol. 16 (1), pp. 111-118, Feb. 2001 [6] Westinghouse Electric Corporation, “Electric Utility Engineering Reference Handbook, Distribution Systems”, Pittsburgh, Pennsylvania, 1965 [7] I. J. Ramírez-Rosado and J. L. Bernal-Agustín, “Genetic Algorithms Applied to the Design of Large Power Distribution Systems”, IEEE Trans. on Power Systems, vol. 13(2), pp. 696-703, May 1998 [8] International Energy Agency. “Review of Existing Mechanisms for Promoting DSM and Energy Efficiency in New Electricity Bussiness Environments”. Task IV Final Report (1996) [9] D. Grayson, C. Heffner, Ch. A. Goldman, “Demand Responsive Programs. An Emerging Resource for Competitive Electricity Markets?” in Proceedings of 2001 IEPEC International Energy Program Evaluation Conference [10] E. Hirst “Reliability Benefits of Price-Responsive Demand”. IEEE Power Engineering Review, vol. 22(11), pp. 16-21, November 2002
