dynamic pricing for smart distribution networks efficient and economic operation

8/13/2019 Dynamic Pricing for Smart Distribution Networks Efficient and Economic Operation

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THE 10thLATIN-AMERICAN CONGRESS ON ELECTRICITY GENERATION AND TRANSMISSION - CLAGTEE 2013 1

Abstract--Smart Grids have permitted the implementation of

Demand Response (DR) programs and have also improved the

control of Distributed Energy Resources (DER). In this paper we

obtain a model of DR of a group of customers, based on DR pilot

projects data and using Artificial Neural Networks (ANN). We

neglect the low voltage electrical network assuming that clients

and DER (e.g. Photovoltaic Generators) are directly connected to

distribution transformers. With the knowledge of the clients DR

model we will define an optimal distribution pricing (e.g. TOU

pricing) to minimize the costs related to distribution network

congestion: Energy Not Supplied Costs for network congestion,

Energy for Voltage Quality Penalty Costs, Technical Losses

Costs. On the other hand in this work we will assume thatPhotovoltaic Generation units do not have Energy Storage

Systems (ESS), and PV generation will be considered as a

negative demand in households. In a later work we will consider

that energy from PV ESSs can be dispatched in such a way that

clients receive more economic benefits because of electricity price

differentials through the day.

Index TermsSmart grids, demand response, dynamic

pricing, distribution management systems, cost optimization,

distributed energy resources, photovoltaic generation, artificial

neural networks, heuristic optimization algorithms.

I. INTRODUCTION

HE need of secure and reliable electric power supplycaused a transition of transmission and generation systems

from traditional SCADA to Energy Management Systems

(EMS). During the last years, more concern has been paid to

distribution systems giving place to DMS, due to several

reasons as [1]:

- Distribution grids are no longer passive load systems due

to the installation of Distributed Generation, making the

network operation more complex.

- The need of a platform capable of supporting advanced

computer operational applications.

Nowadays, Distribution Management Systems (DMS) are atthe center of any Smart Grid, as they help to operate the

Juan P. Palacios is with Universidad Nacional de San Juan, Av. LibertadorSan Martin 1109 Oeste, San Juan Capital, Argentina (e-mail:

[email protected]).Mauricio E. Samper is with Universidad Nacional de San Juan, Av.

Libertador San Martin 1109 Oeste, San Juan Capital, Argentina (e-mail:[email protected]).

Alberto Vargas is with Universidad Nacional de San Juan, Av. LibertadorSan Martin 1109 Oeste, San Juan Capital, Argentina (e-mail:[email protected]).

Distribution Grid more effectively avoiding power outages

and reducing consumer outage duration. DMS leverage

Advanced Metering Infrastructure, Distributed Generation as

well as Alternative Energy and Demand Resources. The DMS

models, simulates, manages intelligent automated field

devices, it also offers more options and capabilities for more

refined grid management [2]: Integration of Advanced

Metering Infrastructure (AMI), Integration of Distributed

Generation, Energy Storage Control, Demand Response,

Electrical Vehicle Charging Management, Asset

Management/Equipment Diagnostics, Power Quality

Management, Integration with Microgrids and Demand

Response for Reliability.

Smart Grid technology, specifically AMI and Metering

Data Management (MDM), represent a great source of

information about customers behavior and consumption

patterns. AMI systems generally utilize two-way

communication to obtain meter reads, remotely

disconnect/reconnect customers and alert utilities of other

meter issues, thereby reducing operating costs and equipment.

MDM systems facilitate the implementation of AMI,

dynamic pricing, and energy conservation as well as the

automation of utility distribution operations and maintenance

activities. MDM systems serve as a recording system for allmeter data, provide real-time access to the data and provide

the pricing for dynamic pricing programs. The MDM system

also serves as the integration point between the AMI network

and the utilitys enterprise systems, ensuring the availability of

meter data to the rest of the Smart Grid functions [3].

Real time data from consumers and producers provided by

MDM and communication infrastructure are useful for the

implementation of Demand Response. Based on real time data

and demand/production forecasts, if the DMS determines the

need to adjust the demand, clients can be influenced to modify

their consumption by means of different kind of incentives as

dynamic pricing, price rebates or other means. In this sense a

key issue for the implementation of an effective DemandResponse Program is the determination of optimal dynamic

electricity pricing to satisfy certain operational objective,

which in this paper will be the minimization of distribution

system operational costs.

Distribution networks have historically received fewerinvestments in capacity compared to generation andtransmission systems. Some consequences of the above are theviolation of feeder loading and voltage operative limits, withhuge economic consequences as Energy Not Served Costs(ENSC) and penalty costs for poor voltage quality. In this

Juan P. Palacios, Graduate Student Member, IEEE, Mauricio E. Samper,Member, IEEEand AlbertoVargas, Senior Member, IEEE

Dynamic Pricing for Smart DistributionNetworks Efficient and Economic Operation

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sense demand response may be useful to avoid the high costsof load shedding and voltage quality penalties, by assigningpricing incentives to off-peak demand times, with the aim thatcustomers shift their consumption from peak to off-peakdemand times, or simply reduce their peak demand.

II. DEMAND RESPONSE

The Federal Energy Regulation Commission defines

Demand Response as the change in normal patterns ofelectricity consumption of end customers, in response tochanges in the price of electricity over time, or in response topayment of incentives with the aim that: 1) Clients pay theactual market price; and 2) clients shift their consumption totimes of low market prices or when reliability is jeopardized.

An example of pricing program is Real Time Pricing (RTP).In RTP, hourly wholesale market prices are assigned to clientsas a pass-through, expecting that they reduce their demandwhen electricity prices are high, with the effect that wholesalemarket prices are also reduced in peak demand times.

Recent studies have shown that despite the manyadvantages that the dynamic pricing models bring; lack ofawareness among users about how to respond to variable ratesover time and the lack of effective residential automationsystems are two barriers to the efficient use of benefits ofdynamic rates. In this sense a reliable and effective demandresponse simulation model is fundamental to describe theclients price responsiveness.

A. Static Rates vs. Dynamic Rates

Static or flat rates include a hedging or risk becausecustomers pay the same amount regardless of the cost impacton the supplying utility. The utility is responsible forpurchasing power in the wholesale market exposing itself tomarket volatility; this purchasing cost is eventually passedthrough to customers in the long term, in the flat rate.

The purpose of dynamic pricing is to provide customerswith more accurate price signals (i.e., wholesale marketprices), with the aim of incentivizing demand response,thereby helping the utility to avoid high wholesale marketprices, as in the case of Market-Based RTP.

From a rate design perspective, a static (or flat) rate iseconomically inefficient because it shields customers fromwholesale market price volatility. As we move from traditionalflat rates to more flexible rate options such as TOU, CPP, andRTP, wholesale price signals are passed on to customers andthese customers are given the option to respond by shiftingdemand.

B. Demand Response Characterization

Demand response programs can be classified in Price-baseddemand response and Incentive-based demand response.Price-based demand response is related with changes inclients consumption in response to variation in electricityprices. This group includes time-of-use (TOU), real timepricing and critical-peak pricing (CPP) rates. Clients energybills can be reduced if they take advantage of pricedifferentials throughout the day. TOU is a rate that includesdifferent prices for usage during different periods throughoutthe day. This rate reflects the average cost of generating anddelivering power during those periods.

RTP is a rate in which the price of electricity is defined forshorter periods of time, usually 1 hour, reflecting changes inwholesale price of electricity.

Customers usually have the information of prices on a day-ahead or hour-ahead basis. In figures 1 and 2 we present atime of use pricing rate and real time pricing rate respectively.

Fig. 1. Price-based demand response: 3-rates TOU pricing

Fig. 2. Price-based demand response: Real time pricing

In figure 3, we show the effects of Market-Based RTP on the

wholesale markets prices. We show the marginal costs curveof a generation system, and demand curves, for different timesof the day. Assuming that demand is responsive to prices, itcan be disaggregated in elastic and inelastic demand. Energyprice in this curve is limited by a price cap (Pcap). If a flat ratepricing program was adopted, customers would not have asignal to react to. At 18:00 if RTP scheme was not adopted,electricity market price would be P3. Thanks to RTP, themarket price at 18:00 is P2, which is lower than P3.


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Fig. 3. Price-based demand response: Real time pricing

Incentive-based demand response includes programs thatgive customers incentives that are additional to their electricityrate, which may be fixed or time-varying. This group includes

programs such as Direct Load Control (DLC),Interruptible/Curtailable Service (ICS), et al. Some of theseprograms penalize customers that fail the contractual responsewhen events are declared. DLC is program that considers aremote shut down or cycle of a customers electricalequipment.

C. Price elasticity

With the aim to estimate how much the demand will vary aselectricity price changes, distribution planners need a model todescribe the consumers response to prices. The priceelasticity rate is a normalized measure of the intensity of howthe usage of electricity changes as price changes by one

percent. The price elasticity of demand can be defined as:

/

/ 1

Where is the energy demand (kWh), and is theelectricity rate ($/kWh).

Price elasticity of demand is negative by definition, hence

sign is usually omitted. If 1demand is inelastic, while if 1demand is elastic.

Two types of price elasticity can be defined [4]: 1) Own-price elasticity which measures how customers will change the

consumption at time due to changes in price at time , thiselasticity takes a negative value as customers reduce theirconsumption in response to prices. 2) Cross elasticity which is

defined as the change in demand at time due to changes inprice at time . Cross elasticity will be either positive or zerodepending on whether the costumer is willing to shift the loador not.

, // 2

, /

/ 3

Self-elasticity is a measure of load curtailment by theconsumer while cross elasticity is a measure of load shifting.Both these constituents put together make the concept of priceelasticity matrices and DR.

For a RTP scenario that has hourly varying rates, PEM will

be of the order 24 x 24. The diagonal elements of the PEMrepresent self-elasticity coefficients and the off-diagonalelements represent cross elasticity coefficients. The overall

change in load at time due to change in price throughout theday can be obtained by summing up the entire row

corresponding to as shown in (4).

,

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D. Customer Behavior Demand Response Models

To understand the customers motivation and likelihood to

respond to different types electrical pricings a customerbehavior model must be assessed. The following reviewconsiders only customer behavior models. The price elasticitymatrix concept has been used in [4, 5] for demand responseestimation. But in both papers authors assume that priceelasticities are static and cannot be trained.

In [5] a day-ahead wholesale bidding mechanism fordemand response was presented. The authors used the conceptof price elasticity matrices to model the demand response.They describe different kind of loads in terms of priceelasticity matrices. Nevertheless this paper does not report amethodology to define a price elasticity matrix based in fielddata.

In [4] the consumers responsiveness behavior to prices wasmodeled using 24-hour price elasticity matrices. Differentprice elasticity matrices models were developed for fivecategories of consumers which were grouped with the aid ofconsumers responsiveness behavior cognitions. The DRmodel was tested with 24-hour real time pricing rates, in theIEEE 123 node test feeder. From this test it was demonstratedthat DR has a great potential to boost the distribution systemsvoltage at most of critical nodes at the end of the feeders. Adrawback of this model is that it is neither dynamic noradaptive, as it assumes that the price electricity matrices areconstant from day to day, and moreover the work in [4] doesnot contribute with a methodology to determine a model based

in field data.In [6] a micro-economic model estimates the customerresponse to economic incentives. The model estimates thedemand response of single customers by classifying themsocio-demographically. The model was initialized withobserved price/demand correlations in pilot DR programs.Adaptive Neuro-Fuzzy Inference System - ANFIS) wasemployed to minimize errors between measurements andmetered values. After obtaining the individual models, thesewere aggregated to obtain the DR at substations feeders bays.A disadvantage of this micro-economic model is that it needs


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too many inputs, and requires high computation time toconverge, making it difficult to use as a real time application.

In [7] the model presented in [6] was used to determine a

group of clients aggregated DR. However, the computation

time increased due to the number of clients influenced by the

DR, making it difficult to use the model of [6] for real-time

applications. In this sense, in [8] a different model that

estimates directly the DR of a group of customers was

proposed and simulated. Based on Artificial Neural Networks(Artificial Neural Networks-ANN) the model presented in [6]

was "trained" with electricity pricing and correlated demand

data. This model has the advantage to be retrained periodically

with the latest data, updating the clients responsiveness to

electricity prices. An interesting review of ANN for Short-

Term Load Forecasting can be found in [9].

In [8] an electricity pricing optimization was performed

with the aim of minimizing the daily demand profile variance,

with the restriction that there is no change in the profit margin

for energy sales. Based on an optimization problem, TOU

rates with two and four different prices throughout the day are

obtained and compared. The optimization problem determines

the electricity prices and prices temporal duration. Betterresults are obtained with the implementation of TOU rates

with four different prices.

In the model presented in [8] however, distributed

generation is not considered (Distributed Generation - DG),

hence this consideration would modify the problem statement,

as it would give the customer the choice to manage the

consumption of their own generation (i.e. residential

photovoltaic generation). This model however does not take

into consideration the costs associated to the distribution

network congestion and the costs associated to voltage quality.

III. THE SMART DISTRIBUTIONNETWORK EFFICIENT ANDECONOMIC OPERATION PROBLEM

A. Proposed Approach

Information about pricing and consumption patterns

collected through AMI and aggregated by MDM systems can

be analyzed for the development and implementation of

dynamic pricing programs. This kind of research is commonly

known as customer behavior studies [10] and its goal is to

analyze the customers response to different electricity rate

levels. With the results of these studies a dynamic pricing plan

can be prepared to comply with technical constraints and

economic goals of utilities. In this way dynamic pricing

programs can be implemented to adjust price levels, to

establish energy saving policies and to defer system

investments [11].

Customers can receive dynamic pricing information through

smart meters with two-way communication capability or

internet web sites. End-user decisions about energy usage can

be manually implemented and also automatically with the use

of smart home appliances.

Once the data is extracted from MDM systems and is

conveniently arranged, the element that should be analyzed is

the customers price responsiveness, which gives the change

in quantity demanded in response to change in price.

The information used to develop a dynamic pricing program

varies continuously and can be automatically organized and

analyzed to update the dynamic pricing schemes if an

intelligent decision-making application is available.

B. Problem Framework

The problem is proposed from the Distribution NetworkOperator (DNO) point of view. The DNO is able to purchaseenergy from the electrical market and from residentialcostumers photovoltaic generation. The market offers energyat wholesale-market RTP price and costumers at a feed-inprice. Feed-in-tariffs are established to incentivize theintegration of renewable energy in the electricity and havedifferent rates in different countries [12]. With these tariffsrenewable energy producers are better remunerated per kWhproduced than traditional energy producers.

In the proposed framework, the regulation permits the DNOto propose a distribution pricing different than RTP in anethical manner. In this sense the distribution pricing cannot begreater than RTP.

The PV system does not consider an Energy StorageSystem; in this sense the energy produced is directly used by

the consumers or injected to the distribution network, in anuncontrollable manner.

The objective of this work will be minimize the distributionnetwork operational costs by means of an optimal distributionpricing, considering also PV generation from residentialconsumers.

C. Problem Statement

With the demand/prices information collected andaggregated through the MDM system and the residentialcustomers solar power forecasts, we will implement anintelligent decision-making application for distributionnetwork operation with the aim of accomplishing these main

tasks:

1) Obtain a demand response model of a group of residentialcustomers enrolled in a DR program. As output of thismodel we will determine 24-hour price elasticity matrices.

2) Evaluate the DR model response to market-based RTPrate variations through the day, in matters of feedercongestion levels.

3) Optimize the RTP rate (announced by the marketoperator) with the aim of minimizing the distributionnetwork operation costs which will be later described. Asa result of this optimization we will obtain a 24-hourdistribution pricing.

In figure 4 a flowchart of the proposed problem is shown.The input of the Demand Response Model are the market-based 24-hour RTP prices, which are announced at 17:00 theday before they are executed. We will assume that these pricesremain constant during the whole evaluation.


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Fig. 6. The price responsiveness ANN model

The motivation indicates the consumers willingness to

respond to the pricing in terms of load shifting. This

motivation varies between -1 and +1, standing -1 for

maximum motivation to switch-off loads and +1 for maximum

motivation to switch-on loads. A motivation of 0 implies that

there is no motivation for change in consumption patterns. The

standard deviation of motivation is trained to take into account

the consumers uncertainty. As the behavior of consumers is

not known, an analytical method cannot be used to describe

the consumers behavior. Hence ANNs are adequate todescribe the consumers behavior, because these systems are

able to map the price responsiveness from the available data

series. In [9] an interest review and evaluation of ANN for

short term load forecasting is available.

2) The model of consumers demand indicates the amount

of electrical power that residential consumers can increase or

reduce compared to the instantaneous power at the

corresponding time of the day. This model depends on three

factors: the forecasted load profile, the energy that was already

shifted during the day, and the frequency of load shifting

which is limited. This model can be described by (5).

, , 5

E. Optimal distribution pricing

Considering that the electricity pricing is the only

optimizable variable, it can be optimized to minimize

distribution network operating costs. The application user

must enter the rate framework that will be optimized (i,e.

TOU, RTP, etc). In figure 7 we show a TOU pricing with 2

rates which is the most understandable pricing for clients. This

rate can be defined in the optimization problem using 4

variables, 2 continuous (x3-x4) and 2 discrete (x1-x2), which

will be optimized.

Fig. 7. Framework of a TOU pricing with 2 rates [8]

The optimization problem is formulated as follows:

Minimize

14subject to

0 15

16where

: hour : Purchased energy costs at time

: Energy not supplied costs due to network

congestion at time : Voltage quality penalty costs at time : Technical losses costs at time : Sum of energy shifted and connected load

through the day

: The pricing rate optimizable variables at time : The RTP pricing rate at time

From (14) it can be understood that the objective of the

optimization problem is to minimize the purchased energy

costs, the energy not supplied costs, the voltage quality

penalty costs and the losses costs. As a result of the problem

an optimal distribution pricing will be obtained.The restriction from (15) means that the energy that has

been shifted must be consumed in another time of the day.

Restriction (16) limits the optimized pricing in a way that it

does not higher than the RTP rate in the corresponding time.

In [14] the heuristic optimization algorithm known as

MVMO was used to solve a daily demand profile variance

minimization problem, outperforming Particle Swarm

Optimization in terms of convergence time. Considering that

this application should be able to run in real-time, we will use

MVMO to solve this optimization problem.


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IV. CONCLUSIONS

In this paper as a part of Ph.D. thesis work, we have

proposed a distribution network efficient and economic

operation problem considering demand response. In this

problem the only optimizable variables are the electricity

pricing rates. We have reviewed papers that analyze the

demand response as a consumer behavior issue. The price

elasticity matrix model used by various authors to describe

demand response fails in sense that it is assumed as static. Inthe proposed model, demand response can be retrained when

weather or demand profile change from day to day.

Although we have proposed MVMO as an algorithm to

solve the optimal pricing problem due to its convergence

characteristics, we may compare its performance with other

heuristic optimization algorithms.

In a later work we may incorporate PV ESS to the problem,

in such a way that the charge/discharge schedules may be

optimized as well, in this case to maximize the economic

benefits of residential customers that are in fact the PV

owners.

V. REFERENCES

[1] M. P. Silva, J. T. Saraiva, and A. V. Sousa, "A Webbrowser based DMS-distribution managementsystem," in Power Engineering Society SummerMeeting, 2000. IEEE, 2000, pp. 2338-2343 vol. 4.

[2] Siemens, "Distribution Management Systems - ASiemens Smart Grid Solution "http://w3.usa.siemens.com/smartgrid/us/en/distributi

on-grid/products/distribution-management-system-

components/Pages/dms-components.aspx, 2013.[3] A. Ferreira and C. Dortolina, "Economics Behind

Dynamic Pricing Benefits in Smart Grids," in 21stInternational Conference on Electricity Distribution,

2011 CIRED, Frankfurt, 2011.[4] N. Venkatesan, J. Solanki, and S. K. Solanki,

"Demand response model and its effects on voltageprofile of a distribution system," in Power andEnergy Society General Meeting, 2011 IEEE, 2011,pp. 1-7.

[5] J. Wang, S. Kennedy, and J. Kirtley, "A newwholesale bidding mechanism for enhanced demandresponse in smart grids," in Innovative Smart GridTechnologies (ISGT), 2010, 2010, pp. 1-8.

[6] T. Holtschneider and I. Erlich, "Modeling demandresponse of consumers to incentives using fuzzy

systems," in Power and Energy Society GeneralMeeting, 2012 IEEE, 2012, pp. 1-8.

[7] T. Holtschneider and I. Erlich, "Assessment methodfor incentives and their optimization consideringdemand response of consumers," in Innovative SmartGrid Technologies (ISGT Europe), 2012 3rd IEEE

PES International Conference and Exhibition on,2012, pp. 1-6.

[8] T. Holtschneider and I. Erlich, "Optimization ofElectricity Pricing Considering Neural Networkbased Model of Consumers' Demand Response,"

presented at the Symposium Series on ComputationalIntelligence, 2013 IEEE, Singapore, 2013.

[9] H. S. Hippert, C. E. Pedreira, and R. C. Souza,"Neural networks for short-term load forecasting: areview and evaluation," Power Systems, IEEETransactions on, vol. 16, pp. 44-55, 2001.

[10] P. Lau, "AMI and Smart Grid at SMUD," inDistributech Conference & Exhibition, Tampa, 2010.

[11] A. Ferreira and C. Dortolina, "Implementation of fastand effective dynamic pricing schemes in SmartGrids," in Integration of Renewables into theDistribution Grid, CIRED 2012 Workshop, 2012, pp.1-4.

[12] E. McKenna and M. Thomson, "Photovoltaicmetering configurations, feed-in tariffs and thevariable effective electricity prices that result,"Renewable Power Generation, IET,vol. 7, 2013.

[13] W. Nakawiro, I. Erlich, and J. L. Rueda, "A noveloptimization algorithm for optimal reactive powerdispatch: A comparative study," in Electric UtilityDeregulation and Restructuring and Power

Technologies (DRPT), 2011 4th International

Conference on, 2011, pp. 1555-1561.[14] http://www.e-dema.de/en/.

VI. BIOGRAPHIES

Juan Pablo Palacios (SM05, GSM11) wasborn at Portoviejo in Ecuador, on June 28, 1980.He received the Electrical Engineer degree fromEscuela Politcnica Nacional, Ecuador, in 2007.

He is currently pursuing a Ph.D. degree inElectrical Engineering at Universidad Nacionalde San Juan in Argentina, with a scholarshipfrom the German Academic Exchange Service(DAAD). His areas of expertise include powersystems control systems modeling, distributionnetworks, smart grids, distributed generation.

Mauricio E. Samper (S07GS11) received theElectrical Engineer degree from the NationalUniversity of San Juan (UNSJ) in 2002 and thePh.D. degree from the Institute of ElectricalEnergy (IEE),UNSJ, Argentina in 2011.

Presently, he is an Assistant Research

Professor at the IEE-UNSJ. His areas of expertiseinclude competitive power markets, distributionnetworks, and quality of service, distributedgeneration, smart grids, and investments underuncertainty.

Alberto Vargas (M97,S M02) received theElectromechanical Engineer degree fromUniversidad Nacional de Cuyo, Argentina, in1975 and the Ph.D. degree in electricalengineering in 2001 from Universidad Nacionalde San Juan, Argentina.

He is currently a Professor at Instituto deEnerga Elctrica, Universidad Nacional de SanJuan (IEE-UNSJ), Argentina. Since 1985, he has

been the Head Researcher of the Regulating andPlanning team in electric markets, at IEE-UNSJ.He is a Consulting Program Manager of


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Asinelsa S.A, a specialized software company for electric distributiondevelopment dealing with electrical AM/\FM GIS and DMS applications.

dynamic pricing for smart distribution networks efficient and economic operation

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