Title

Iulia Coroamă, Gianfranco Chicco, Mihai Gavrilas

“Gheorghe Asachi” Technical University of IasiFaculty of Electrical EngineeringBd. Profesor Dimitrie Mangeron, nr. 21- 23.700050 IASIRomaniaFax: +40-232-237627E-mail coroamaiulia@yahoo.com

Politecnico di Torino

Abstract

1. Introduction

2. Demand response concepts and optionsDemand response [IEA 2003][Torriti 2010]

Short-term demand response:Previous literature studies suggest that the short-term price elasticity of the electrical demand is relatively low [Lijesen 2007]Households consumers could increase their propensity to select dynamic tariffs when demand flexibility is high [Ericson 2011].Kirschen[Stadler, 2008] discusses different types of control.[Dashti, 2011] wide analysis and modelling of distribution system efficiency including DR

[Greening 2010] discusses the presence of multiple entities dealing with demand response aspects in a restructured market, indicating that there is not a single entity that can perform all the functions needed to fully promote demand response implementation. (in our case, we deal with the technical side of DR, connected with optimization)

[Albadi 2008] categorization of demand response programs into incentive-based and price-based (our case goes to the incentive-based)

[Yusta 2007] optimal prices under two objective functions (profit of the electric utility and social welfare). Points of interest: both sides (utilità and consumers) are included in the reasoning. At the utility side ( the one of our interest; a third operator to consider in general is the System operator) the objective functions are considered for 1 h and for 24 h (solving 1 h problems independently). CHK

[Kim 2011] discusses the merits and drawbacks of demand response actions (CHK)(we are proposing a side action wrt demand response).

[Valencia 2011] intra-day markets and DR

Selection and prioritization of demand response programs is addressed in [Aalami 2010] by using Multi Attribute Decision Making. (also DR modelling is addressed)

Demand response initiatives may aim at massive response from many aggregated customers (theoretically all of them). However, loss allocation concepts state that the technical advantages provided to the distribution network depend on the location of loads and local generators in the network. In some cases, only reducing the load in some nodes could even have a negative impact, with an increase in the network losses. More specifically, the notion of technical advantage is explained by the use of marginal loss coefficients [EGbook 2000, Mutale 2000, Carpaneto Chicco Sumaili]. EXPLAIN THE DEFINITION AND USE OF MLCs

This paper discusses how to provide selective incentives to modify the local generation and/or consumption on the basis of the technical advantages quantifiable through the use of the MLCs, considering an intra-day market structure. This market structure has a shortest term wrt day-ahead markets, and as such is able to handle intra-day variations in a more effective way (it could be associated to network configuration adjustments done with the same cadence of the intra-day market, e.g, if there are 4 periods in a day, we can think about using 4 “optimal” network configurations with the same time cadence).

Application: The MLCs are quantified by taking into account the results of the optimal reconfiguration in reference cases, trying to move the operating point to a more advantageous solution in terms of a specified objective function. Here voltage control and losses *******, not conflicting with each other.

In a smart grids paradigm, optimal reconfiguration could be run more times during a day, in order to operate the distribution system closer to optimal conditions.The adoption of an intra-day electricity market structure could be helpful to make the updates of the energy market closer to the evolving conditions in which the real system is operating.However, optimization has to take into account that moving from one configuration to another implies more operation costs, so configuration changes have to be limited. Maintaining a clear reference configuration of the network is also useful for the distributor to provide information to the regulatory authorities concerning the continuity of supply for reliability applications [*****].

The mechanism of demand response is seen as a key means to exploit anticipatory knowledge on events possibly occurring in the system in the next hours (for instance, linked to weather forecast in a geographic area). Example of what happens in a winter period if a storm is forecast 8 hours ahead: the expected customers’ reaction is for instance to increase consumption by switching on more lights, and upon reduced ambient temperatures the automatic reaction of thermostat-controlled loads is to increase their duty cycles. In addition, consumers could switch on manually-controlled thermostats (and others, chk refs). Meanwhile, the operation of local generation such as PV systems could be drastically reduced. In a system expected to be operated close to the optimal configuration (min losses) in normal conditions, the varying generation and loading could cause loss increase and move the operation point far from the optimal solution. Upon anticipatory knowledge, there are two ways to operate the system close to its optimum: (i) seek for a new optimal configuration (but the uncertainty on what will happen in high, and switching is expensive); (ii) leaving the same network configuration and giving incentives to loads and local generators to operate in such a way to reduce losses (here MLC matters).Demand response incentives can be aimed at correcting the situation in a specific way. (DR is for loads; and for DG, which incentives?)

On the economic side, demand response can be activated by proposing changes to the tariff applied by correspondingly giving incentives to the application of load reductions at strategic locations.

From [Moura 2010]: Other technology that can performamajor role in the integrationof renewable intermittent power is demand response (DR) [IEA 2003].With these technologies it is possible to direct or indirectly force a

consumption reductionin critical situations, in a short time.The ideabehind DR is that if the marginal peak load price is higher than thevalue that a consumer gets out of the services derived from theelectricity, he would be willing to modify the demand, if paid thepeak price or slightly less instead. Traditionally the DR technologieswere typically used to attend to economical concerns. However,nowadays they can be used to improve the system reliability,reducing instantaneously the energy consumption to prevent themost unbalanced situations, like the problems that result from thelarge space conditioning consumption on days with reduced windvelocity. As more customers practice automated price-responsivedemand or automatically receive and respond to directions toincrease or decrease their electricity use, systemloads will be able torespond to, or manage, variability from wind power production.(we are going further, thinking of using DR to operate the systems in an efficient way in the next future – successive periods of the intraday markets upon anticipatory knowledge)

3. Optimization objectives and marginal loss coefficients

Optimal distribution system reconfiguration

The reconfiguration problem was solved for different objectives, as:

- minimizing of total losses

where is the rezistance of the branches (b) and is the load current of the branch

- minimizing of voltage deviation related to the rated voltage

where

where E is the consumer energy, V is the node voltage and Vref is the refence voltage considered as the target.

- maximizing the charging reserve of the branches:

where is the maximum current of the branches and is the current of the branches .

Background concepts on marginal loss coefficients

Marginal loss coefficients (MLCs) [1] refers to the changes that can appear in the total active power losses due to a marginal change in the generation or consumption of the active or reactive power in each bus and they are

defined in eq.(1) where L represents the total real power losses, and are the active and the reactive factor

for the bus i.

(1)

It can be said that the MLCs method is a loss allocation one based on the power flow solutions. For the slack node and for the PV nodes in the network, the MLCs are undefined and are considered zeros. According to this

coefficients demand response incentives are used to the local generation and/or consumption when the total losses in the network are higher, in order to get smaller ones.

Thus, for e.g. if the total losses for a entire day for the analyzed network are 0.108 MWh and we want to minimize them according to the MLCs, we have to take in consideration the coefficients signs. If the MLCs are positives or negatives, for minimizing losses we have two possibilities: a) to increase the consumption and the local generation remains the same, or b) decrease the local generation with the same consumption.

4. Exploiting marginal loss information to drive customers’ response

Intra-day markets enabling more accurate decision-makingSpot pricing as the most refined goal.

Anticipatory knowledge

Quantification of the benefits (technical aspects)Discussing demand response economics is without the scope of this paper.

5. Case study

Exemplificative analyses carried out by using the proposed approach are illustrated in the sequel.The example refers to a specific day (18 October). In some jurisdictions the heating period allowed for households starts on mid October (e.g., the heating period could be from 15 October to 15 March), so that heating systems could be in place in the day under analysis for residential customers.

For the study case a distribution network was considered as in Fig.1. The structure of the network and data are presented in [1]. The distribution network has Nbus = 44 buses, L = 47 branches, SL= 4 supply nodes and O = 7 open branches.

Fig. 1. The structure for the distribution network considered for the study case

The power flow analysis of the network was performed with the Newton-Raphson (NR) method, in order to get the information needed to compute the MLCs. Load profiles for one day were used for three types of consumers: residential, commercial and industrial. The normalized shapes of these profiles are shown in Fig. 2. The peak power is used as normalizing factor. In order to apply these load profiles to the distribution

system nodes, the peak power of each profile is assigned at each node, then the actual power is obtained as the product of the peak power times the typical load profile. Moreover, each node could contain multiple consumer categories, hence the total load is the sum of the contributions of the different categories. Without loss of generality, the reactive power loads are considered by using constant power factors for each consumer category.

Fig. 2. The typical daily load profiles

Fig. 4. The photovoltaic generation profiles

To solve the reconfiguration problem the iterative improvement method based on branch exchange [CC03] was used and implemented in Matlab. The reconfiguration problem was solved for different objectives, such as minimizing of total losses, minimizing of voltage deviation related to the rated voltage, and maximizing the charging reserve of the branches.

To find the optimal solution for the reconfiguration problem for the objectives mentioned above, three cases were considered:

- Case I: no local generation source is connected to the network;- Case II: small hydro connected to the network at three different nodes (with *** MVA at node 7,

*** MVA at node 19, and *** MVA at node 34). Flat generation profile with constant production during the day is assumed for the three hydro plants.

- Case III: small hydro as in case II, and photovoltaic (PV) systems connected to the network at different nodes. The photovoltaic generation profile considered refers to the specific day considered (18 October), location (latitude 45°45' North, longitude 7°21' East) and positioning (on a surface with 22°

inclination and azimuth 20°, with albedo coefficient = 0.2). The generation profile is assumed for a clean day, with maximum solar irradiance of 802 W/m2 at hour 13:00. The details of the photovoltaic production referring to these characteristics are shown in [BC06]. The same photovoltaic production conditions are applied to all the distribution system nodes, using different installed PV power in the nodes (details).

For these cases, the distribution system optimal configuration with loss minimization was calculated for a day, hour by hour. Comments on all optimal solutions, possible strategy for selecting the “best ones” for specified periods? Hypothesis of considering the network with small hydro + PV with clear sky. Use for the day the solution at 2 pm Two (why only two?) optimal solutions were found, one in the daylight period (hours 8 am – 5 pm) for the three cases, and one in the night period for cases I and II, as shown in Table 1.

Table 1

Best solutions for the reconfiguration problem for night and daylight periods. The open branches in the optimal solution that were not open in the initial configuration are highlighted in bold.

initial open branches of the network

best solution during the night best solution during the daylight period

for the network

without DG sources

for the network with small hydro

connected

for the network

without DG sources

for the network with small hydro connected

for the network with

small hydro+PV connected

7 834 3520 216 1519 2225 2818 23

7 835 3620 216 1515 1925 2817 18

9 1031 3220 216 15

21 2528 3617 18

7 834 3520 216 1519 2225 2817 18

5 733 3420 218 9

15 1725 2817 18

8 936 3720 216 1515 1928 3617 18

From Table 1 it can be seen that for the case when no DG sources were connected to the network the optimal solution for night and daylight periods does not differ much with respect to the initial one, with only 2 and 1 open branches changed, respectively. Yet, in the other two cases with DG sources connected to the network, the optimal solution are completed different, with only two open branches unchanged in case II and case III.

Then, the optimal solutions for day and night were considered and the Newton Raphson method was run for each one of them, for the entire day, regarding losses minimization. Also, each of the solution found were run for the three cases, without and with DG sources connected at the network. For example, the optimal solution for the day in the case I was run for the: case I without DG sources, for the case II with small hydro connected to the network and for case III with small hydro and photovoltaic connected to the network, in the sense of minimizing the total losses of the network during a day. Also, in the NR method were computed the voltage deviation related to the rated voltage and the charging reserve of the branches. The results are presented in the Table 2 and 3.Consistent behavior of the three objective functions considered.

Table 2 Results for the best solution for the reconfiguration problem for night when the three cases are considered

Best solution for night

Results for the network without DG

Results for the network with hydro

Results for the network with hydro+photovoltaic

Case I (no DG) Losses = 1.2803dV=0.0066

Ilim=34.3337

Losses = 0.8100dV=0.0036

Ilim=35.6959

Losses = 0.8100dV=0.0036

Ilim=35.6959

Case II (with hydro DG)

Losses = 1.5973dV=0.0112

Losses = 0.8287dV=0.0053

Losses = 0.8287dV=0.0053

Ilim=33.5022 Ilim=35.4591 Ilim=35.4591

Table 3Results for the best solution for the reconfiguration problem for day when the three cases are considered

Best solution for daylight

Results for the network without DG sources

Results for the network with hydro

Results for the network with small hydro+photovoltaic

Case I (no DG) Losses = 1.2759dV=0.0065

Ilim=34.4571

Losses = 0.8158dV=0.0038

Ilim=36.0220

Losses = 0.4673dV=0.0014

Ilim=37.1381

Case II (with hydro DG)

Losses = 1.6710dV=0.0142

Ilim=34.2668

Losses = 0.7588dV=0.0041

Ilim=35.9436

Losses = 0.4315dV=0.0012

Ilim=36.6191

Case III (with hydro + PV)

Losses = 1.4171dV=0.0088

Ilim=33.7581

Losses = 0.7613dV=0.0037

Ilim=35.4236

Losses = 0.3962dV=0.0013

Ilim=36.7666

Fig. 5. Total losses obtained for the solutions in the night period

Third: with

Fig. 6. Total losses obtained for the solutions in the daylight period

For each case, the marginal loss coefficients (MLCs) were computed. These coefficients refer to the changes that can appear in the total active power losses due to a marginal change in the generation or consumption of the active or reactive power in each bus. According to this coefficients demand response incentives are used to modify the consumer load when the total losses in the network are higher, in order to get smaller ones.

From the MLCs computed it was observed that during the night, values are reasonably low (5 times lower than during of the day or even less). For this fact, it is not reasonable to make changes during the night, so the optimal configuration during the night will not be modified.

Thereby, changes will be made only during the daylight period, and demand response incentives will be used in terms of moving the consumers’ load to better fit the optimal solution found in case III.

Incentives to keep the distribution network at relatively low losses

For the day under analysis (18 October), let us consider a situation in which the expected weather conditions indicate clean sky during the morning, with expected weather changes at a specific hour (2 pm). These changes could reduce the PV production to 20% of the one at clean sky. The production from small hydro plants is considered to be unchanged. Furthermore, in some jurisdictions the heating period allowed for households starts on mid October, so that heating systems could be in place in the day under analysis, and the effects of heating systems with automatic and manual response could lead to increasing the electrical load, for instance by 5% for the residential customers (assuming all aggregated residential customer loads at the various nodes have a consistent behavior – this is reasonable just because the residential load is taken as an aggregate). By running optimal reconfiguration in these new conditions for PV production and residential load, the best configuration changes ********. In the changed configuration, the MLCs are **** (Table ***).Thereby, for decreasing losses, actions could be made in terms of promoting demand response incentives, taking in the consideration the marginal loss coefficients computed in the new conditions without changing the network configuration. We shall apply the MLCs to find the nodes in which changes in DG and/or load are most relevant in reducing the loads. If changes are applied successively, we should find the solution moving towards the one with the best configuration with 100% PV generation. This is true only when all the customers participate in making the changes. If only a subset of them is participating, the results could move towards another solutions.

Sub-cases, with different shares of customer participation:1. all consumers participate: parametric analysis to show what happens if load increases by 5%, 0%, or

decreases by 5%, 10% or 20%.2. part of the consumers participate: parametric analysis to show what happens if load increases by 5%,

0%, or decreases by 5%, 10% or 20%.

Table *A**: considering the MLC results (calculated with 20% PV and 5% load increase) as sensitivity factors, sharing the load reduction proportionally with respect to the MLCs (taking into account their sign, so excluding the nodes in which load reduction would lead to higher losses) - no customer willingness used, and no limit to the per cent load reduction in the individual nodes (that is, the load reduction is driven by the MLCs only, and not by the load power; in any case, the load power has to remain positive).

Table *A**Network lossesper cent customer

per cent load reduction -5% 0 5% 10%

participation025%50% 75%100%

Table *B**: (“blind” approach, the node location and the MLCs are not considered) not considering the MLC results, only the willingness of the customers to participate to the load reduction. Statistical analysis with Monte Carlo approach.

Example 10 nodes: extract 10 random values from uniform probability (e.g., 0.112, 0.998, 0,881,…), then reorder the nodes in descending order of the random values, take the first x% of them and assign the load reduction proportionally to the load used in the MLC calculation. Make the statistic.

Table *B**Network losses, best values, average values and standard deviations per cent customer participation

per cent load reduction -5% 0 5% 10%

025%50% 75%100%

Expected “ideal” result: the values in Table A are lower than the best case of Table B in the corresponding cells. verify???

According to the signs of the MLCs computed, the load will be increased or decreased in order to get smaller losses for approaching the solution obtained in the case with 100% PV.

Conclusions

ReferencesM.G. Lijesen, The real-time price elasticity of electricity, Energy Economics, 2007.T. Ericson, Households’ self-selection of dynamic electricity tariffs, Applied Energy, 2011P.S. Moura, A.T. de Almeida, Multi-objective optimization of a mixed renewable system with demand-side management, Renewable and Sustainable Energy Reviews, 2010.IEA. Demand response in liberalised electricity markets. International Energy Agency, 2003.

J. Torriti, M.G. Hassan, M. Leach, Energy, 2010, Demand response experience in Europe: Policies, programmes and implementationH.A. Aalami, M.P.Moghaddam, G.R. Yousefi, Modeling and prioritizing demand response programs in power markets, Electric Power Systems Research, 2010.F.C. Schweppe, M.C. Caramanis, R.D. Tabors, R.E. Bohn, Spot Pricing of Electricity,Kluwer Academic Publishers, 1989D.S. Kirschen, Demand-side view of electricity markets, IEEE, Transactions on Power Systems 18 (May (2)) (2003)I. Stadler, Power grid balancing of energy systems with high renewable energy penetration by demand response, Utilities Policy, 2008R. Dashti, S. Afsharnia, Demand response regulation modeling based on distribution system asset efficiency, Electric Power Systems Research, 2011[Greening 2010] L.A. Greening, Demand response resources: Who is responsible for implementation in a deregulated market?, Energy 2010[Kim 2011] J.-H. Kim, A. Shcherbakova, Common failures of demand response, Energy 2011

[Albadi 2008] M.H. Albadi, E.F. El-Saadany, A summary of demand response in electricity markets, Electric Power Systems Research 2008

[Yusta 2007] J.M. Yusta, H.M. Khodr, A.J. Urdaneta, Optimal pricing of default customers in electrical distribution systems: Effect behavior performance of demand response models, Electric Power Systems Research 77 (2007) 548–558.

[Valencia 2011] I. Valencia-Salazar, C. Álvarez, G. Escrivá-Escrivá, M. Alcázar-Ortega, Simulation of demand side participation in Spanish short term electricity markets, Energy Conversion and Management 52 (2011) 2705–2711

[1] A. Augugliaro, L. Dusonchet, and S. Mangione, “Optimal reconfiguration of distribution network for loss reduction using non-linear programming,” Eur. Trans. Elect. Power, vol. 1, no. 6, pp. 317–324, 1991.

[1] Baran M.E., Wu F.F. Network reconfiguration in distribution systems for loss reduction and load balancing. IEEE Trans Power Deliv 1989;4(2):1401–7.

[2] Baran, M.E. & Wu, F.F. Network reconfiguration in distribution systems for loss reduction and load balancing. IEEE Trans. on Power Delivery 1989; 4(2), 1401–7.

[3] Borozan V, Rajicic D, Ackovski R. Improved method for loss minimization in distribution networks. IEEE Trans Power Syst 1995;10(3): 1420–5.

[4] Civanlar S, Grainger JJ, Yin H, Lee SSH. Distribution feeder reconfiguration for loss reduction. IEEE Trans Power Deliv 1988;3(3): 1217–23.

[5] Opazo Mora H, Garcia-Santander L, Lopez Parra E. Minimal loss reconfiguration considering random load: applications to real networks, Ingeniare Journal of Engineering 16 (2008) 264–272.

[6] Taleski R, Rajicic D. Distribution network reconfiguration for energy loss reduction, IEEE Trans. Power Deliv. 12 (1997) 398–406.

[7] Savier JS, Debapriya Das. Loss allocation to consumers before and after reconfiguration of radial distribution networks, Electric Power and Energy Systems 33 (2011): 540-9

[8] Wu YK, Lee-Chang L, Tsai SH. Study of reconfiguration for the distribution system with distributed generators, IEEE Transactions on Power Delivery 2010, 25(3): 1678-1685

[9] Milani AE, Haghifam MR. An evolutionary approach for optimal time interval determination in distribution network reconfiguration under variable load, Mathematical and Computer Modelling 2011

[10] Broadwater R, Khan A, Shalan H, Lee R. Time varying load analysis to reduce distribution losses though reconfiguration, IEEE Trans. Power Delivery 1993, vol. 8, 294–300.

[11] Peponis GJ, Papadopoulos M. P. New dynamic, branch exchange method for optimal distribution system planning,nProc. Inst. Elect. Eng. Gener. Transm. Distrib. 1997, 144(3): 333-9.

[12] Ackermann T, Andersson G, Soder L, Distributed generation: a definition, Electric Power Systems Research 2001, 195-204

[13] Ackermann T, Knyazkin V. Interaction between distributed generation and the distribution network: operation aspects. In: Proc second int symp distributed generation: power system market aspects, Stockholm, Sweden; 2002.

[14] Carmen LTB, Djalma MF. Optimal distributed generation allocation for reliability, losses, and voltage improvement. Int J Electr Power Energy Syst 2006;28(6):413–20.

[CC03] Carpaneto E, Chicco G, Roggero E. Comparing deterministic and simulated annealing-based algorithms for minimum losses reconfiguration of large distribution systems. Proc. IEEE Bologna PowerTech, Bologna, Italy, June 23-26, 2003, Vol.2, paper No.137.

[BC06] Batrinu F, Carpaneto E, Chicco G, Gagliano S, Spertino F, Tina GM. Assessing the performance of photovoltaic sites and grid-connected plants: a study case. Proc. VI World Energy System Conference, Torino, Italy, 10-12 July 2006, 386-393.

[1] Mutale J; Strbac G; Curcic S, Jenkins N. Allocation of losses in distribution systems with embedded generation. IEE Proc.-Gener. Transm. (2000) Distrib., 147(1), 7–14