Electric Energy Cost Reduction by Shifting Energy Purchases from On-Peak Times

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1 Electric Energy Cost Reduction by Shifting Energy Purchases from On-Peak Times Tan Zhang, Stephen Cialdea, Alexander E. Emanuel, LFIEEE, John A. Orr, LFIEEE Department of Electrical & Computer Engineering Worcester Polytechnic Institute Worcester, MA USA Abstract This paper reports simulated results that detail the effects of the parameters that control the charge/discharge operation of a Battery Energy Storage System (BESS) located on a distribution feeder. The following BESS parameters were evaluated: energy capacity, imum discharge current, and round-trip efficiency. Also, the following system parameters were studied: marginal cost (MC) of electric energy, and the 24 hour load profile of the feeder. The BESS performance was quantified as a function of the differential cost of energy (DCE) representing the difference between the cost of energy purchased to charge the BESS and the cost of energy delivered, (sold). The obtained results prove that the use of BESS can be beneficial, yielding significant savings, if the battery size is sufficiently large and the battery efficiency is high enough. Index Terms-- Energy Storage. Differential Cost of Energy. Engineering Economics. Renewable Generation. I. INTRODUCTION Multiple potential benefits can be derived from the use of battery energy storage systems (BESS) in distribution systems [1]. Previous publications have reviewed the state of the technologies for electric energy storage [2], [3], and battery sizing and operating strategies in distributed generation environments, [4], [5]. End users as well as utilities may find the installation of such systems to be economically attractive under appropriate conditions. Among the many proposed applications the following are particularly significant operationally or economically: Shifting the energy purchase from on-peak times to off-peak times, Reduction of demand charges, Deferral of transmission and distribution investment, Avoidance of outages when demand exceeds a critical level, Improved power quality, Reduction of transmission and distribution losses. The goal of this study is to quantify the benefits and shortcomings related to the characteristics of the first application, dealing with the energy shifting. II. PREPARATION OF BESS AND SYSTEM DATA The economic viability of a BESS is a function of several parameters and variables. This work presents an organized method for evaluating the combined impact of those quantities on cost savings. The electrical and economic quantities that govern energy storage and discharge operation are analyzed below. A. Marginal Cost of Electric Energy This paper is based on the assumption that the curve describing the Marginal Cost (MC) of the energy consumed by the studied feeder loads is a reduced scale replica of the MC that corresponds to a region large enough to include power plants, substations and a complex transmission and distribution system with multiple feeders. In Fig. 1a are presented actual measurements covering a region with a total load that varies from 16 MW to 725 MW [6]. The curves given in Fig. 1b summarize the three MC curves used in this work as representative of the MC of electric energy, ($/MWh), versus the total power, (MW), delivered to the feeder loads. The three curves presented in Fig. 1b are the best fit curves, using (1), of three conditions: A - low demand, B - medium, and C - very high demand. In this expression ($/MWh) (1) P is the total active power (MW) supplied to the end users served by the BESS and the parameters, and γ are given in Table I. This study is focused on a single feeder, 15 kv class with a imum loading limited to SL = 1. MVA. CURVE TABLE I. MARGINAL COST PARAMETERS ($/) A γ ($/ ) B C Financial support for this work was provided by Premium Power Corporation, National Grid, and the US Department of Energy. 213 IEEE Electrical Power & Energy Conferenc (EPEC) /13/$ IEEE

2 This approach to the feeder MC has limitations. In actuality there is not a strong correlation between the regional MC and the MC that corresponds to instantaneous demand on a particular feeder. Presently the available data on MC is limited to regions that include hundreds of thousands of customers. However, if the studied feeder belongs to a region where relatively homogenous clusters of customers constitute the major loads (advanced proliferation of electric vehicles represents such a case) the present results may provide valuable insight on the benefits derived from the use of BESS. where the power P B (MW) is the imum power output. The battery life will be limited to some number of cycles determined by T and P B. The efficiency is given B, W BDCh by = W BDCh / WBCh, (round-trip efficiency) where is the energy supplied to the battery during the charging time and WBDCh is the amount of energy that can come out of battery relative to the. TABLE II: PARAMETERS CHARACTERIZING THE 24 h LOAD CURVES Curve a (MW) b c t t 24 I II III I SL P Power (MW) III II 6. Figure 1. Electric energy marginal cost: actual measurements [6] best fit curves. B. The 24 hour Load Curve. The range of possible shapes of the time variation of total feeder power P(t) versus time t in hours is shown in Fig. 2. These shapes closely represent actual feeder load profiles [7]. In this study the shape is parametrically defined 1exp (MW) (2) where the parameters a, b and c are given in Table II for the three curves in Fig. 2. The curves are constrained within the extremes Pmin and P. C. Battery characteristics. The capacity, W BDCh (MWh), is the imum energy that can be delivered from a full charge, without damaging the unit. This energy can be supplied over the time T = W P, B BDCh / B Time Figure hour Load Curves. S L = 1 MVA, P/ S L =. 85 W/VA, Pmin/ S L =.55 W/VA. Three major variables were considered in this study: η, B and W BCh. The remaining parameters were normalized. The base values are W T (MWh), the total 24 h energy supplied to the loads that benefit from the BESS, and S L (MVA), the imum apparent power that can be continuously distributed by the feeder under normal conditions (see Fig. 2). III. METHODOLOGY A typical 24 h load curve is presented in Fig. 3. The impact of the charge/discharge process is illustrated by the shaded areas. In the absence of the BESS the feeder load would be represented by the bold line. During the charge process the feeder load increases with the energy transfer to the BESS indicated by the shaded area between t and t e. Note that P ( t ) = P( t e ). During the battery is supplied with the power P B. During and, P <. The charging strategy is such that B P B P min

3 the area Pmin A 1 = WBCh occupies a region as clo in order to minimize the cost of energy discharge takes place for tb t t f. The discharge area A2 = W BDCh = ηbwbch is located as close as possible to P in order to imize the price of the energy delivered. ose as possible to y purchased. The time t, which provides imum economic benefit. Thus if the battery total charging time is increased to allow the storage of more energy, the DCE will peak at some point, after which the DCE will start to decrease. Theoretically it is possible, if the battery capacity is large enough, to reach the situation where t t. One may call this condition saturation. Under this condition, any additional battery capacity will not be used. Figure 3. The charge/discharge method. The benefit of energy shifting was evaluated using the DCE (Differential Cost of Energy): DCE = K WD K WC ($) (3) where (4) is the cost of energy supplied to the battery, and (5) is the reduced cost of the energy delivered to feeder s loads by the BESS, energy that shifts the on-peak power. The actual battery charge/discharge process takes place in real time, but the start and stop times are planned in advance based on the 24 hour load curve prediction. This same approach may be used for a load curve with multiple peaks as shown in Fig. 4. The algorithm operates by identifying the times of relative minimum energy cost for charging and of imal energy cost for discharging. Minimum MC occurs at and imal MC occurs at. From these starting points the chargingg and discharging times are expanded as shown in the shadedd areas of Fig. 3, subject to the constraints of BESS energy capacity W, BDCh, P ( t ) = P( t e ) and The power is increased incrementally, (meaning an earlier start of the battery charging) and the charging/discharging periods are expanding. At each increment the DCE is calculated and plotted vs the variables and parameters as illustrated in the results. With unlimited BESS capacity there is an optimum Figure 4. The charge/discharge method for a load curve with two peaks. The charging process is finished at before the discharging starts. This algorithm assumes perfect day-ahead knowledge of the load and MC curves, so that the charge/discharge commands may be determined in advance. In actuality updated estimates of the load and MC curves may be used to adjust the BESS control in real time. IV. RESULTS Space limitations confine this paper to report results only as a function of the variables, and with /.5 W/VA, S L = 1 MVA. In Fig. 5 are summarized the DCE curves as functions of the discharging time, with the efficiency ηb as a parameter. From these curves one learns that for a given 24 h load curve and battery round trip efficiency, there is a battery size, characterized by a, that provides the highest DCE. As the MC describes a more extreme variation of price with demand, the DCE increases. For example, if 7% and TB=5 h, the DCE = $52/day for curve A, and $1,7/day for curve C. The Fig. 6 graphs provide information on the effects of the load curve s imum power P, as well as the effect of the marginal cost. For the marginal cost curve A, the DCE is as low as $18/day for 85% and P /. 95. S L = For the same parameters a jump to $39/day is found for the marginal cost curve C.

4 DCE ($/day) % % % DCE(S/day) Figure 5. Differential cost of energy (DCE) vs. with round-trip efficiency as parameter, 24 hour Load Curve Type I, P/ S L =. 85 W/VA, /.55W/VA, /.5W/VA, S L = 1 MVA, marginal cost curve A, marginal cost curve B, marginal cost curve C. 4 4 P =.95 3 P =.95 3 P = Figure 6. Differential cost of energy (DCE) vs. the round trip efficiency with feeder loading (.75 /.95W/VA) as parameter, 24 hour Load Curve Type I, /.55W/VA, /.5W/VA t, 5.h, S L = 1 MVA, W T 181.8MWh. marginal cost, curve A, marginal cost, curve B, marginal cost, curve C It is to be expected that lower efficiency will lower the DCE. In Fig. 7 is presented the DCE as a function of BESS input energy as a percentage of the total 24 h energy demand of the feeder ( / WT ) under load condition I with a parameter. The effect of ηb is dramatic. Note that for = 7%, the peak savings occurs for BESS input energy representing 1% of the total feeder energy and that for a low value of ηb the DCE may always be negative % Figure 7. DCE vs. the normalized. round trip efficiency as parameter. 24 hour Load Curve Type I, P / S L =.7 W/VA, P min/ S L =.6 W/VA, PB / S L =. 5 W/VA, 5.h, S L = 1 MVA, 16.8, marginal cost curve C. Figures 8 and 9 reveal the effect of battery charging energy and the effect of the 24 hour load curve. It is seen that curve III, that requires less energy during the on-peak hours, results in lower DCE when compared with a load curve that requires higher energy during on-peak hours (curve I). The DCE (Differential Cost of Energy) was calculated also for one year using a probabilistic approach. The instantaneous load profile for a typical year is shown in Fig. 1. The 365 days load variation assumed a 24 hour Load Curve Type I, Each day has a Load Curve characterized by P and P, as is shown in Fig. 2. The peak load for each day n was assumed to follow a uniform distribution based on the expression.65.35,1 (6) defines the upper boundary of ; it is a curve that peaks during the summer and winter times. One can see the imum peak value for the 24 hour load curve is varying randomly in a range:.65. The yearly imum value of occurs in July and is termed. is computed in the same manner:.65.35,1 (7) where defines the upper boundary of. / was adjusted daily for imum DCE. The results are summarized in Table III under several different conditions.

5 % % % Figure. 8. DCE vs. / WT. The 24 hour Load Curve Type I, Round Trip Efficiency is parameter. P/ S L =. 85 W/VA, Pmin/ S L =. 55 W/VA, PB / S L =.5 W/VA, 5.h S L = 1 MVA, WT = MWh.marginal cost, curve A, marginal cost, curve B, marginal cost, curve C % % Figure 9. DCE vs. / WT. The 24 hour Load Curve Type III. Round trip efficiency is parameter. P/ S L =. 85 W/VA, Pmin/ S L =. 55 W/VA, PB / S L =.5 W/VA, 5.h h S L = 1 MVA, W T 165.7MWh. marginal cost, curve A, marginal cost, curve B, marginal cost, curve C % Power (MW) J F M A M J J A S O N D 2 Month Figure 1. Annual load variation. TABLE III: ANNUAL DCE MC Curve ANNUAL DCE ($) =1MW =9MW =8MW A 57,72 15,389 4,688 B 139,26 62,951 33,23 C 296, 154,86 92,545 V. CONCLUSIONS This paper reports the results of a study of the differential cost of energy obtained by shifting the energy purchases from on-peak times using a BESS. The study considered a 15 kv class feeder, rated at 4 A imum current. The results indicate that if the marginal cost of energy is directly related to the instantaneous load demand of the feeder, the annual benefit may be substantial. Not surprisingly, the results also demonstrate the strong impact of round-trip efficiency on the feasibility of BESS. The approach presented here may be used to estimate annual cost savings with use of an appropriate BESS, and to avoid situations where the savings may be negative. ACKNOWLEDGMENT The authors gratefully acknowledge the contributions of Clayton Burns and Justin Woodard of National grid for their assistance in accessing feeder data, Stephen Rourke and staff of ISO New England for their insight into the various aspects of the cost of power. REFERENCES [1] Electric Energy Storage Technology Options: A White Paper Primer on Applications, Costs, and Benefits, EPRI, Palo Alto, CA, 21, [2] Yang, Z.; Zhang, J.; Kintner-Meyer, M. C. W.; Lu, X.; Choi, D.; Lemmon, J. P.; Liu, J. Electrochemical energy storage for green grid. Chem. Rev., 211, 111 (5), pp [3] Vazquez, S.; Lukic, S.M.; Galvan, E.; Franquelo, L.G.; Carrasco, J.M.;, "Energy Storage Systems for Transport and Grid Applications," Industrial Electronics, IEEE Transactions on, vol.57, no.12, pp , Dec. 21 [4] Borowy, B.S.; Salameh, Z.M.;, "Methodology for optimally sizing the combination of a battery bank and PV array in a wind/pv hybrid system," Energy Conversion, IEEE Transactions on, vol.11, no.2, pp , Jun 1996 [5] In-Su Bae; Jin-O Kim; Jae-Chul Kim; Singh, C.;, "Optimal operating strategy for distributed generation considering hourly reliability worth," Power Systems, IEEE Transactions on, vol.19, no.1, pp , Feb. 24 [6] [7] Zaborsky and J. W. Rittenhouse, Electric Power Transmission, Rensselaer Bookstore, 1969, p. 582.