1 Impact of Demand Response on Distribution System Reliability

Salman Mohagheghi, Member, IEEE, Fang Yang, Member, IEEE Bamdad Falahati, Student Member, IEEE

Abstract—Demand response (DR) is a market driven and aspect of the Smart Grid. Modern distribution automation sometimes semi-emergency action performed at the utility level systems adopt new techniques for fault detection, isolation or at the Demand Response Service Provider (aggregator) with and restoration so that the smallest possible part of the system the objective of reducing the overall demand of the system during is de-energized as a result of a fault while the rest of the peak load hours. If implemented successfully, DR helps postpone customers are supplied through alternative paths and the capacity expansion projects related to the distribution network, and provides a collaborative framework for the alternative resources [3], [4]. In addition, solutions such as liberalized energy market of the Smart Grid. Customers Uninterruptible Power Supplies (UPS), energy storage devices subscribed to the DR program are requested to reduce their and distributed energy resources are some of the alternatives demand or turn off one or more energy consuming appliances in introduced at the distribution level in order to boost the system exchange for financial incentives such as extra payments or reliability for the customers. Although these devices are discounted electricity rates. This would change the concept of efficient in providing short term energy, their role and distribution system reliability as is traditionally known. From one hand, DR could lead to a higher amount of unserved energy; effectiveness is still complementary to that of the grid, and on the other hand, it does not qualify as an unwanted lost load. cannot replace the need for high reliability provided by it. This paper tries to provide a qualitative analysis on the impact of Clearly, higher levels of reliability can be achieved by demand response on distribution system reliability. adding to the redundancy level of the distribution grid through

Index Terms—Distribution system, reliability, availability, capacity expansion (CAPX) projects. This is however smart grid, demand response, demand dispatch. restricted in part due to the cost of additional investments on the infrastructure and partly because of environmental I. INTRODUCTION concerns triggered by installing more overhead lines and ISTRIBUTION System Reliability, according to the generation units. The limited capacity of the distribution DIEEE dictionary definition, is defined as the ability of the system jeopardizes its performance especially during the peak distribution system to perform its function under stated hours where all or most of the system capacity is being conditions for a stated period of time without failure [1]. utilized and the grid functions at little or no safety margin. Reliability of the distribution system is specifically important Under such circumstances, a simple overheating of a line or as it is directly associated with the satisfaction level of the transformer, or malfunction of a component can potentially customers. However, in practice, many factors may affect the lead to catastrophic consequences. Reducing the power losses performance of the distribution network. For one, these can help to some extent, as in the United States 5-12% of all networks are often exposed to natural phenomena and may be the power generated is lost, and 60% of this figure is directly impacted by severe weather conditions. Moreover, in many attributable to the distribution system [5]. Advanced cases due to the radial structure instead of a more complicated distribution and feeder automation systems can adopt ways to and more redundant meshed/loop structure, especially in reduce these power losses in the distribution system, for North America, any single outage and failure can potentially instance through voltage and Var optimization (VVO). affect a large number of customers. Components that are However, the amount of losses that can be reduced this way is normally prone to failure are the distribution lines, distribution limited and may not be adequate for relieving the strain from cables, power transformers, service transformers, capacitors the power network. and voltage regulators. In fact, a typical distribution system Active demand is another alternative that can help reshape accounts for 80% of customer reliability problems [2]. the demand profile of the system by partial curtailment of the With the advent of new sensitive electronic devices in the load, or shifting it from peak hours to off-peak hours, thereby grid for the residential, commercial and industrial consumers, reducing the peak demand and relieving the system capacity the quality of supply and continuity of service are becoming during the peak hours. Although, this is not considered as a more important than before. This has made reliability a major permanent solution, in the short term it can help delay the construction of new lines and generation units. The cost S. Mohagheghi and F. Yang are with ABB Corporate Research, Raleigh, savings, in conjunction with the environmental impacts of NC 27606 USA (email: salman.mohagheghi, [email protected]). postponing system expansion plans, has made demand B. Falahati is with the Department of Electrical and Computer response an attractive solution for the utilities worldwide. Engineering, Mississippi State University, Starkville, MS 39762 USA (email: [email protected]). During this work, he was an intern with ABB Corporate What further strengthens the position of DR as one of the Research, Raleigh, NC. 978-1-4577-1002-5/11/$26.00 ©2011 IEEE 2 pillars of the Smart Grid paradigm is the financial benefits it this category are listed below [10], [11]: creates for both the utility and the consumers by reducing the • System Average Interruption Frequency Index (SAIFI) volatility of the electricity market, deferring investments and [int/yr] = total number of customer interruptions in one the reduction in electricity rates. year / total number of customers served. Sometimes it is Regardless of the type of DR program, the consequence of defined as the number of supply interruptions per 100 a DR event triggered by the utility as a semi-emergency action connected customers. could be involuntary reduced consumption for one or more • Customer Average Interruption Frequency Index (CAIFI) customers1. Although this can help alleviate the system [int/yr] = total number of customer interruptions in one capacity and reduce the chances of service unavailability on year / total number of customers affected. This index the large scale, it would still add to the number of shows trends in customers interrupted and helps to show interruptions experienced by the customers, which in the the number of customers affected out of the whole traditional sense of power system reliability, could have been customer base. interpreted as lower reliability indices and poorer service • System Average Interruption Duration Index (SAIDI) performance. This clearly contradicts with the widely [hr/yr] = sum of customer interruptions durations / total accepted notion [6]-[8] that demand response can improve the number of customers served. This is sometimes also reliability of the distribution system. Hence, the traditional referred to as availability index indicating the minutes lost distribution system reliability assessment should be modified per connected customer. to integrate DR event properly so that the resulting reliability • Customer Average Interruption Duration Index (CAIDI) indices can reasonably reflect the impact of DR on the overall [hr/int] = sum of customer interruptions durations / total distribution system reliability. It is therefore imperative that number of customer interruptions. CAIDI gives the the impact of demand response is evaluated in conjunction average outage duration that any given customer would with the reliability of the power system both at the system experience, and can also be viewed as the average level and for the individual customers. The objective of this restoration time. paper is to provide a qualitative assessment on the impact of • Momentary Average Interruption Frequency Index DR on distribution system reliability in the light of the new (MAIFI) [int/yr] = total number of customer interruptions Smart Grid paradigm. less than the defined time / total number of customers served. This index is not as often reported as the other II. POWER SYSTEM RELIABILITY indices. It helps track the momentary power outages Power system reliability can be evaluated in two aspects: caused by transient faults that are otherwise hidden in the adequacy, which is related to the existence of sufficient notion of SAIDI or SAIFI. Of course, the definition of the facilities in the system to satisfy the load demands within the momentary interruption varies from one utility to another. system constraints, and security, which is the ability of the • Average Service Availability Index (ASAI) = customer distribution system to overcome the disturbances occurring hours of available service / customer hours demanded. inside it [9]. Adequacy is related to the static balance between This index has also been referred to as the Index of load and generation (and existence of system facilities to meet Reliability (IOR). the demand), while security is associated with the dynamic • Average System Unavailability Index (ASUI) = customer response of the system to disturbances [10]. hours of unavailable service / customer hours demanded. The utility normally conducts a contingency analysis in Based on the definitions above, the following relations can order to detect the weakest spots in the network. Most utilities be developed between the indices: design their network to a specific contingency level, for CAIDI = SAIDI / SAIFI (1) instance a single contingency level, which means that the system is designed in a way that due to the existing sufficient ASUI = 1 – ASAI (2) redundancy and switching alternatives, failure of one In all these calculations, a customer interruption is component will not cause any customer outages. Higher considered as one interruption to one customer. Also, contingency levels (N – k) can also be defined and are likely regardless of number of interruptions, the customer affected to be adopted for the modern distribution grid which is by the interruption should only be counted once. equipped with more sensitive loads. B. Energy Related Indices The reliability of the power system is often measured in This class of indices assess adequacy of generation systems terms of certain reliability indices. These are broadly during steady state as well as during corrective and preventive classified as the following three categories: maintenance. The most common ones are listed below [10], A. Customer Service Related Indices [11]: These are indices that are related to the service • Expected Energy Not Supplied (EENS) [hr/yr] = expected performance offered by the utility. The most common ones in number of hours in a period during which the load will exceed generation capacity. This index is also referred to

1 Of course, this excludes the DR programs that are proactively performed as Loss of Load Expectation (LOLE). It does not indicate by the customers, i.e., rate-based DR and demand reduction (see Section III). the severity of the shortage. LOLE can be normalized as a 3

Loss of Load Probability (LOLP), which in conjunction 1MW of DR capacity is of the order of $240,000 versus with Value of Lost Load (VOLL) can be used in the $400,000 for a gas fired plant; while at the same time DR calculation of electricity prices. capacity can potentially be dispatched in less than 5 minutes, • Loss of Energy Expectation (LOEE) [MWh/yr] = loss of whereas a peaking power plant can take up to 30 minutes to load duration considering the amount of load lost. This ramp up to full capacity [12]. Successful deployment of a DR index can be used for comparison between the systems of program can lead to substantial financial benefits. A Federal different sizes or between different load levels of a single Energy Regulatory Commission (FERC) initiated study system. reported that a moderate amount of demand response could • Average Energy Not Supplied (AENS) [kWh/yr] = total save about $7.5 billion annually by 2010 [13]. However, not energy not supplied / total number of customers served. all consumers need to respond simultaneously for markets to This is sometimes referred to as Average Load benefit from DR. Some reports suggest that 20% of customers Interruption Index. account for 80% of price response, and that only as few as 5% • Average System Curtailment Index (ASCI) = total annual of all customers are needed to discipline electricity market curtailment / total number of customers served. It prices [7]. determines the kWh of load interruptions per customer Demand response programs can be roughly classified into served. three groups according to the party that initiates the demand • Average Customer Curtailment Index (ACCI) = total reduction action: annual curtailment / total number of customer affected. It • Reliability-Based DR Programs: in this category of DR determines the kWh of connected load interrupted for programs, also referred to as incentive-based DR or load each affected customer in one year. response DR, a set of demand reduction signals (i.e., DR signals) are issued by the utility or the DR Service C. Load Point Indices Provider (aggregator) and sent to the participating At the distribution level, the basic load point indices at a customers in the form of voluntary demand reduction customer load point are [10]: requests or mandatory commands. Various types of • Failure probability resources can be utilized under this program, namely, • Failure rate (frequency) λ (failure/yr) directly controllable loads (mostly at the residential • Average outage time r customer sites) and loads that can be interrupted or • Annual average outage time U reduced upon receipt of a signal from the utility (mostly The individual customer indices are aggregated with the at the commercial and industrial customer sites). average connected load of the customer and the number of Examples of programs in this category are Direct Load customers at each load point in order to obtain service related Control (DLC) and Interruptible & Curtailable Load indices such as SAIFI, SAIDI, CAIDI and ASAI. (I&C). DLC loads can be remotely cycled or turned off by the utility, and can normally be deployed within a III. DEMAND RESPONSE IN POWER SYSTEMS relatively short notice. In more modern DR applications, Electric demand response (DR) refers to the changes in the these loads may be directly dispatched by the utility based electricity usage by the end-use customers from their nominal on the balance between the load and generation [14]. consumption patterns in response to changes in the price of Typical household appliances and electric vehicles may electricity over time, or to incentive payments designed to fit well in this scheme. I&C loads on the other hand are induce lower electricity use at times of high wholesale market often larger scale and may include lighting, A/C, process prices or when the system reliability is jeopardized [7]. heating and cooling, and/or scheduling of production. Demand response is often associated with the short-term Notification time for this category of loads varies from a changes targeted for the critical hours during a day/year when few minutes to a few hours [8]. the demand is high or when the reserve margin is low, • Rate-Based DR Programs: in this program category the whereas the long-term changes in the electricity consumption price of electricity is changed at preset times or achieved through investments in energy efficiency or changes dynamically based on various times of the day/week/year in customer behavior are often referred to as Demand Side as well as the available reserve margin. The customers Management which is an effort realized by the demand side would pay the highest prices for peak hours and lowest only to improve energy efficiency. prices for off-peak hours. The prices can be set a day in In addition to improving the reliability of the power advance on a daily or hourly basis, or in real-time. The system, and making short-term impacts on the electricity customer would respond voluntarily to the changes in the markets leading to financial benefits for both the utility and electricity prices. the customers, DR can reduce the system peak load in the long • Demand Reduction Bids: customers participating in this term and therefore postpone the need for building new power category of programs initiate and send demand reduction plants, resulting in considerable environmental impacts. This bids to the utility or the aggregator. The bids would would clearly lead to cost efficiency for all the DR normally include the available demand reduction capacity participants. For instance, the capital cost required to build out and the price asked for. This program encourages mainly 4

large customers to provide load reductions at prices for being able to take any precautionary steps. Furthermore, which they are willing to be curtailed, or to identify how demand response normally targets less sensitive loads (for much load they would be willing to curtail at the posted instance air conditioners and washer/dryers in the case of price [15]. residential customers); and hence sensitive loads may be able Figure 1 illustrates the schematic diagram of the demand to stay energized. More specifically, only a portion of the load response architecture implemented at the DMS level. The DR is curtailed under demand response, allowing the customer to engine would receive the meter data from the Meter Data manually or automatically shift some of its demand to the non- Management System (MDMS), which in turn collects the data peak hours. Finally, the customer sees benefits from demand from individual customers or the aggregator(s) through reduction in a DR event, which can be in the form of different communication means. The DR engine also relies on discounted electricity rates or incentive payments. the load forecast module that provides forecast of the future In this context, load curtailment can be broadly classified demand either at the overall system level or at a more granular as follows: level, for instance at the meter or the service transformer • Interruption – this is the loss of power in the traditional levels. The engine performs as a semi-emergency application sense of interruption of service without advance notice or that is triggered only when the load forecast module predicts a prior agreement. shortage of supply for a future time window. In addition, • Demand Response – partial reduction of demand either electricity rates are provided to the DR engine through performed by the user in response to a previously external resources. This information activates the rate-based received advanced notice, or instantly without advance module of the overall DR engine, and is also used to notice but based on a mutual agreement between the determine whether a DR event should be issued instead of utility and the customer. purchasing the required power from the spot market. • Demand Shifting – under this category, demand is reduced for certain duration of time and can be resumed at a specific time in the future. In other words, the demand is shifted from peak hours to non-peak hours. Examples are smart charging of electric vehicles after midnight, or automatically delaying the operation of washers, dryers or dishwashers to after peak hours.

Fig. 1. Schematic diagram of demand response engine at the DMS level.

The performance of the system is continuously monitored at the DMS by calculating the forecasted demand, thereby estimating the capacity margin for the future time intervals. The capacity margin is defined as the difference between the demand limit (taking into account the reserve margin) and the forecasted demand. A decrease in this capacity margin or a Fig. 2. Capacity margin is used to trigger demand response. negative margin would cause the utility to trigger a DR event (Fig. 2) [16]. B. Reliability Indices in the Presence of DR IV. IMPACT OF DEMAND RESPONSE ON RELIABILITY In order to distinguish between the DR events issued by the utility –in accordance with the mutual agreement with the A. Demand Response vs. Interruption customer and sometimes with advanced notice– and the Even though demand response would lead to load unannounced and unplanned interruptions in the distribution curtailment, it is necessary to distinguish between DR based system it seems reasonable to define two classes of reliability load reduction and interruption as considered in the traditional indices as related to the frequency and duration of the service reliability analysis. First and foremost, demand response often interruptions in the system: The first class of indices would be curtails loads with advance notice whereas in traditional load pertinent to the interruptions in the traditional sense and curtailment, loads are shed instantly without the customer would exclude the DR-related loss of service. These include 5 the traditional SAIDI, SAIFI, CAIDI, CAIFI, etc. The second the reliability indices with and without demand response can class of indices is newly defined in this paper and is be quantified. For this purpose, the reliability index can be associated with the planned and sometimes voluntary calculated in the presence of DR (as explained in the next interruptions in response to DR events. A list of these indices section) and compared with the base case (no DR in the is provided below: system). This could be provided as an indication of the service • SADFI – System Average Demand response Frequency improvement as a result of demand response. For instance for Index [event/yr] = total number customer demand the SAIFI index, the Improvement in System Average reductions in one year / total number of customers served. Interruption Frequency Index (ISAIFI) can be defined as: • CADFI – Customer Average Demand response ISAIFI = SAIFINoDR – SAIFI (3) Frequency Index [event/yr] = total number of customer where SAIFINoDR denotes the reliability index when no DR demand reductions in one year / total number of event is applied to the system. customers that received DR requests. This index shows C. Modeling DR for Reliability Analysis how many customers were requested to reduce/curtail The improvement on system reliability brought by DR their demand out of the whole customer base. comes from two aspects: when system demand approaches the SADDI – System Average Demand response Duration • demand limit, DR can be applied to shed/shift the load in Index [min/yr] = sum of the durations of customer DR order to increase the capacity margin, and also, after a fault events / total number of customers served. This index occurs, DR can be used to increase the restoration capacity indicates the minutes of lost service as a result of DR and reduce the load interruption duration. The DR-related events. reliability enhancement can be measured by evaluating the • CADDI – Customer Average Demand response Duration changes in the reliability index values with and without taking Index [min/event] = sum of the durations of customer DR into account DR events in the reliability analysis procedure. events / total number of customer DR events. CADDI This section proposes a DR modeling for reliability analysis indicates the average DR event duration that any and a systematic approach that is able to integrate the DR customer would experience if subscribed to the DR events in the general distribution reliability evaluation program. framework. These indices can be calculated from the historical data To facilitate the integration of the DR event in the available on the load profile of the system and the past distribution reliability analysis, a probability DR model is demand response events issued by the utility. However, it proposed. In practice, during a DR event, when a customer should be noted that intelligent demand shifting should not be receives the DR signal from the utility, it may or may not included in the DR-related indices as it is not exactly a loss of comply with the signal to reduce or shift its consumption level. This depends on many factors such as the time/date of service and since it does not target critical appliances it does the event and its duration, possible penalties associated with not lead to the same level of consumer discomfort as the declining a DR request, the amount of incentives offered for traditional interruption, or even a demand curtailment under compliance, as well as nondeterministic factors such as the DR. number of people in the household, the behavioral patterns of It is worth noting that demand response requests that the household, etc. Such uncertainty in the customer behavior require approval by the customers are not necessarily can be probabilistically (or stochastically) modeled. The complied with by all the parties who receive them. In fact, the historical records of the individual customer’s consumption utility often takes this uncertainty factor into consideration by patterns and past responses to the DR events can be retrieved sending more demand reduction requests that actually desired. in order to help determine the probability value. Accordingly, This issue can also be incorporated into the calculations of the two DR related probabilities are introduced for each DR DR-related reliability indices in order to give a more accurate involved customer as following: the probability for customer sense of the DR event durations and frequency experienced by to respond to DR signal (PDR) and the probability for customer the customers. not to respond to the DR signal (P¬DR). The two probabilities Since demand response in general improves the reliability satisfy PDR = 1 – P¬DR. of the system –adequacy in the current context– it can be Distribution reliability analysis techniques include two safely assumed that higher values for the SADFI, SADDI, etc general approaches: analytical techniques and Monte Carlo could mean lower values for the corresponding traditional simulation (MCS) based techniques. Both approaches have the indices such as SAIFI, SAIDI, etc. However, for an average following analysis procedure which consists of three major consumer, the distinction between the two might not be easy steps: to perceive, as both can be interpreted as lower comfort level 1. Select a contingency with probability PC, with which a as a result of loss of power. Although compliance with DR fault occurs to the distribution system. events is in general rewarded financially through discounted 2. Evaluate the contingency impact (load curtailment evaluation) and weight the contingency impact with P rates or incentive payments, due to the complicated billing C 3. Aggregate the contribution from each contingency to schemes and practices that may vary from one utility to calculate the reliability indices another, it is not easy to incorporate that information into the The DR model can be integrated in the step 2, i.e., DR DR-related reliability indices. However, an improvement in event will influence both the contingency impact evaluation 6 and the probability used to weight the contingency impact. energy price for their services [18]. Specifically, in step 2, for a selected contingency, one can apply the available DR-related load shedding/shifting first, V. CONCLUDING REMARKS then evaluate the contingency to figure out the resulting load Demand response is an important aspect of the Smart Grid curtailment. The contingency impact or its contribution to the paradigm that enables the end-use consumers to take control reliability index should be weighted with the multiplication of of their consumption patterns and indirectly impact the probabilities P and P . C DR electricity market. By temporarily reducing the consumption D. Demand Response and Security level of individual customers, and perhaps shifting it from It is relatively straightforward to see how DR would peak hours to off-peak hours, the utility manages to reduce the improve adequacy, as a reduction in the peak demand restores overall system demand during peak times. This helps partially the safety margin of the system and reduces the strains on the relieve the capacity of the system and provide a safety margin network constraints. But DR can also benefit the security should the system be exposed to faults and disturbances. aspect of reliability. This is specifically true for faster types of Furthermore, certain demand response programs are designed demand response programs that provide emergency load to provide these services in a fast manner which helps reduction and can be viewed as a type of ancillary service. In improve the dynamic performance of the system. It is this case, demand response will not be solely used for therefore believed that demand response can improve the reducing the peak demand; rather, it can function as a true reliability of the system, both in terms of availability of alternative supply of intermittent energy that when necessary service, and security. can balance load and generation in the system in order to This paper provides a qualitative analysis on the impact of achieve voltage control, frequency regulation and suchlike. demand response on distribution system reliability, and The North American Energy Standards Board (NAESB) proposes a new framework for analyzing the reliability indices has defined the timing for a DR event (Fig. 3) [17]. For some in the presence of demand responsive loads, thereby assessing DR programs at the residential or commercial level, due to the the impact of active demand on the overall reliability of the dynamics of the loads, it takes a certain amount of time (ramp distribution system. period) until the consumer demand is reduced to the requested This paper concludes that in general system reliability can levels. However, for certain types of customers for instance potentially benefit from applying demand response. However, some large scale industrial plants or direct load control for it should be noted that successful implementation of demand specific consumers, this process can be further expedited. In response requires usage of smart devices (sensors, meters and general, the ramp period can take anywhere from a few actuators) that by themselves additional reliability seconds to a few minutes. considerations associated with each device functioning properly. These assets would need further protection as the performance of the grid depends more and more on them. Not to mention, the reliability of the power system from the cyber- security standpoint as more devices can be remotely accessed, manipulated and activated.

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[10] R. Billinton and R.N. Allan, “Reliability of Electric Power Systems: An VII. BIOGRAPHIES Overview,” in H. Pham (Ed.), Handbook of Reliability Engineering, Springer-Verlog: London, UK, 1st edition, 2003, pp. 511-529. Salman Mohagheghi (S’99, M’07) received the B.Sc from University of [11] R. Billinton and R. N. Allan, Reliability Evaluation of Power Systems, Tehran, Iran, and PhD from Georgia Institute of Technology, Atlanta, GA, New York, NY: Plenum, 1996. USA, both in Electrical Engineering. From 2007-2010 he was a Senior R&D [12] Thomas Weisel Partners, “A Primer on Demand Response,” White Engineer at ABB Corporate Research, Raleigh, NC, USA. Currently, he is an Paper, October 2007. Assistant Professor at the Engineering Department, Colorado School of [13] S. Kiliccote, M.A. Piette and D. Hansen, “Advanced Controls and Mines, Golden, CO, USA. His research interests include situational awareness Communications for Demand Response and Energy Efficiency in in power systems, communication networks in power systems, and distribution Commercial Buildings,” in Proc. 2nd Carnegie Mellon Conference in automation systems. Electric Power Systems, Pittsburgh, PA, USA, January 2006, pp. 1-10. Fang Yang (M’07) is a Sr. R&D engineer with ABB US Corporate Research [14] A. Brooks, E. Lu, D. Reicher, C. Spirakis and B. Weihl, “Demand Center in Raleigh, North Carolina. Her research interests include distribution Dispatch,” Power & Energy Magazine, vol. 8, no. 3, May/June 2010, pp. automation, power system reliability analysis, and application of artificial 21-29. intelligence techniques in power system control. [15] J. Han and M.A. Piette, “Solutions for Summer Electric Power Shortages: Demand Response and its Applications in Air Conditioning Bamdad Falahati (S'08) received the B.S. and M.S. degrees in electrical and Refrigeration Systems,” Journal of Refrigeration, Air Conditioning engineering from Sharif University of Technology, Iran in 1999 and 2008 and Electric Power Machinery, vol. 29, issue 1, pp. 1-4, January 2008. respectively. He is currently working toward the Ph.D. degree at the [16] W. Peterson, X. Feng, Z. Wang, S. Mohagheghi, E. Kielczewski, Mississippi State University, Starkville. From 2004 to 2008, he was with “Closing the Loop: Smart Distribution Management Systems are Moshanir Co. as an R&D Engineer. His research interests include substation Helping to Provide More Efficient and Reliable Services,” ABB Review, automation systems, power systems reliability, distribution grid management vol. 1, March 2010, pp. 38-43. and micro grids. [17] S. Coe, A. Ott and D. Pratt, “Demanding Standards,” Power & Energy Magazine, vol. 8, no. 3, May/June 2010, pp. 55-59. [18] Thomas Weisel Partners, “A Primer on Demand Response,” White Paper, October 2007.