From Classification to Optimization Techniques in

Demand Response: From Classification to Optimization Techniques in Smart Grid Ashfaq Ahmad 1 , Nadeem Javaid1,∗, Umar Qasim2, Zahoor Ali Khan3 1 COMSATS

Institute of Information Technology, Islamabad, Pakistan 2 University of Alberta, Alberta, Canada 3 CIS, Higher Colleges of Technology, Fujairah Campus, UAE ∗ www.njavaid.com, [email protected], [email protected] Abstract—In conventional grids, consumer has not been considered for solving the problems associated with electric industry. In order to meet the ever increasing consumers’ demand, conventional methods primarily rely on increasing generation capacity which is not a feasible solution due to limited resources. Thus, the overall efficiency of electrical networks needs to be improved. From this perspective, the idea of smart grids has transformed the conventional power system into an intelligent and smart one. Smart grid is not a single technology, rather, it is merger of electrical power networks with communications network. Moreover, there are two basic players in the smart grid; utility and consumer. In response to different pricing schemes, introduced by the utility, smart grid transforms the consumer into a prosumer via Demand Response (DR). Thus, enabling the consumer to become an important player in energy management and optimization. This paper embeds a two fold contribution; (i) classification of DR techniques based on the chosen criteria, and (ii) distinctive discussion of latest DR optimization techniques. It is foreseen that this paper will help in determining future research directions and design efforts for developing DR techniques. Keywords Smart Grid, Demand Response, Home Energy Management, Advanced Metering Infrastructure, Peak to Average Ratio, Demand Side Management, Optimization, Heuristics

I.

I NTRODUCTION

In most parts of the world, especially in developed countries, transmission and distribution systems have become aged. Old grid system needs renovation to meet challenges like increased electricity demand, cost reduction, enhanced reliability, etc. Modern information and communication technologies have been incorporated as a part of grid system infrastructure in recent years. European technology platform (European Commission, 2006) defines smart grid as, “a smart grid is an electricity network that can intelligently integrate the actions of all users connected to it–generators, consumers and those that do both in order to efficiently deliver sustainable, economic and secure electricity supplies”. Smart grid has revolutionized the performance of all the sections of conventional grid. In case of conventional grid, energy can only flow from generation side to consumer, whereas, in case of smart grid consumer can also sell its extra electricity generated through domestic sources, e.g., solar, wind, etc. Introduction of smart grid infrastructure on distribution section has manifold impact where retailers and consumers are important players of distribution section. From retailer perspective, goal is to increase profit and reduce peak to average load ratio. On the other hand, from consumer perspective, goal is to reduce cost without compromising the

comfort level. From the global perspective, main drivers behind smart grid are capacity, efficiency, reliability, sustainability and customer engagement [1], [2]. In smart grids, engagement of consumer would enable demand side management to reduce the peak load. Thus, not only decreases the required cost but also increases the overall efficiency. Another major issue in distribution system is the losses that may be tackled by using new technologies like advanced metering infrastructure, home energy management systems, intelligent appliances with communication capabilities, local generation and energy storage systems. Furthermore, legislation is required to provide legal cover about consumers’ usage profile. This would significantly help in designing new tariff plans with improvement which would ultimately lead to increased customer participation rate in smart grids [3]. Demand response (DR) is the action taken by the consumer against the varied electricity prices [4]. DR covers about 45% of smart grid benefits. Customer participation has revolutionized the concept of efficient electricity usage and load management. Thus, the system stability increases and the cost decreases [3]. By taking the advantage of two way information flow in SG, DR information is communicated to consumer in almost real time. Consumer may also implement energy management system by using two way information exchange between retailer and consumer. Different pricing schemes like time of use pricing, critical peak pricing, and real-time pricing can be effectively implemented in smart grids. The electricity price in case of time of use pricing and critical peak pricing is known in advance to the consumer. In case of real time pricing, price may change on hourly basis or even more frequent. Thus, real time pricing is more flexible in comparison to time of use pricing and critical pricing. However, real time pricing has some inherent drawbacks. Firstly, it requires frequent communication between consumer and retailer–consumer has to respond accordingly every time the price changes which is difficult to perform manually and retailer has to wait and see the response of consumer for his future action. Secondly, load is shifted to hours with low price, which would lead to a higher electricity peak demand and peak-to-average ratio during the low price time [4]. In order to limit the peak demand, DR programs often use mechanisms for convincing the consumers to reduce their demands, however, in doing so they may also lead to increased demand during off-peak hours. Since DR may limit

the consumers’ comfort which means that DR may increase the time during which consumers may be exposed to discomfort. Therefore, automation, monitoring and control technologies are needed to make DR less hindering in these situations [5]. When customers participate in DR, there are three possible ways in which they can change their use of electricity [6], [7]. •

Reducing their energy consumption through load curtailment strategies.

•

Moving energy consumption to a different time period.

•

Using on site stand by generated energy, thus limiting their dependence on the main grid.

Load curtailment strategies can be attained, for example, by dimming lighting levels, decreasing the temperature set points of air conditioners, etc. Power consumption shifting may be, instead, achieved by commercial or residential customers by pre-cooling their facilities and shifting load from higher to lower cost time periods. Industrial facilities may also benefit from lower-cost off-peak energy by using storage technologies in order to postpone some production operations to an overnight shift, or by transferring their production to other industrial facilities in other service areas [5]. In literature, many articles are presented that are subjected to DR in smart grids. For example, [8] discusses DR in smart grids with renewable energy sources and the associated costs, [9] focuses on advanced metering infrastructure, [33] provides overview of the latest associated technologies, [34] surveys value added energy services to consumers, [35] deals with planning, implementation and evaluation techniques for reducing energy consumption and peak electricity demand, and [10] shows optimization techniques are yet effective to facilitate intelligent decision making in smart grids. This paper provides DR survey in two aspects (classification, and optimization techniques) while discussing distinctive characteristics of the existing techniques. We suggest that this paper will help researchers in future to tackle issues related with DR in smart grids. II.

R ELATED W ORK

Smart grid is combination of conventional grid infrastructure and information and communication technology. Since the role of advanced metering infrastructure for control/command signals and implementation of DR strategies is of prime importance that is why [9] discusses advanced metering infrastructure. According to this paper, advanced metering infrastructure has enabled to get real time data acquisition from consumer, transmit the data and return commands to the load. On the basis of this data, optimization algorithms and strategies are implemented. Advanced metering infrastructure can also be used for diagnostic purposes while detecting any leakage, fault or cyber attack and thus saves huge maintenance cost. Advance services like fire detection, monitoring and consumer consumption control using mobile applications have gained attention of people. However, availability of consumer’s personal data poses serious data security and privacy implications. Moreover, different advanced metering infrastructure aspects like communications, data analysis, data security and control schemes are also discussed.

In [8], DR is discussed in smart grids equipped with renewable energy sources (wind power generation, photo voltaic resources, and solar systems). The authors also discuss various DR benefits and costs; benefits in terms of economics, environment and system reliability, whereas, cost in terms of initial deployment and on-going operations. Initial deployment cost is further divided into program design, marketing, metering communication, and business integration. Whereas, ongoing operations costs include incentive payments to customer, maintenance and administrative and customer opportunity. Renewable energy resources are inherently intermittent and cannot be employed as dispatch-able resources. The paper also describes the attempts that have been recently made to mitigate the intermittency behavior of renewable energy resources. In [2] smart grid concepts, architecture and technological demonstration implemented in different parts of the world are discussed. The survey is based on initiatives taken by EU and IEA (e.g., ETP, EEGI, EERA and IEA demand side management) and description of projects conducted in Europe and US like FENIX, ADDRESS, EU-DEEP, ADINE, GridWise and SEESGEN-ICT. This report is very helpful in providing visions and road maps for developing world wide smart grids (including China and India). Moreover, the authors focus on various smart grid concepts like development of virtual power plants, active demand in consumer networks, DER aggregation business, active distribution network, and technological applications for developing intelligent future grids. Comparison is also carried out on the bases of commercial, technological, and regulatory aspects. In addition, existing features of smart grid technology and challenges faced in its implementation in Finnish environment are addressed. The survey in [10] shows that game theory is a simple yet an effective technique to facilitate intelligent decision making in smart grids. Game theory has taken the attention of research community for effective communication in smart grid scenario. The range of game theoretic optimization techniques is very wide, i.e., ranging from distributed load management to micro storage management in smart grid. Interestingly, the authors conclude that different researchers have different objectives or problem scopes for adopting game theory in smart grids. Nevertheless, all the game theoretic approaches for making effective smart grid solutions have a common aspect, namely the Nash equilibrium, arriving at which may lead to an optimal solution to the relevant problem. [33] presents an overview on smart home research as well as the associated technologies. Different building blocks and their relationship with each other is also discussed in this work. Sensors, multimedia devices, communication protocols, and systems, are considered to be the building blocks for smart home implementation. This study presents a general overview of smart home projects that are arranged according to their intended services. It also discusses the significance and limitations of smart home building blocks. The taxonomy of devices, media, protocols, algorithms, methods, and services presents an informative comparison between the associated technologies. Furthermore, this paper identifies several future directions of smart home research. The authors focus on various trends that indicate the increasing popularity of using middle-ware for integrating heterogeneous devices. In [34], a survey is presented which focuses on providing

new value-added energy services to end-users. An analysis of the survey results and key messages firstly identifies the research gaps and then proposes promising directions that may be followed in the design process of applications and services for residential smart grid. Additionally, the survey tries to gauge that how willing people would engage with their community and share their energy resources.

A. Classification of DR programs

In [35], DR and demand side management; planning, implementation and evaluation techniques for reducing energy consumption and peak electricity demand are discussed. Different home energy management and communication schemes are also discussed that will lead to efficient energy utilization. The authors then focus on the survey of existing software and hardware tools, platforms, and test beds for evaluating the performance of the information and communication technologies. Finally, a comprehensive overview of the state of the art in home area communications and networking technologies for energy management is provided which is followed by a review of the affordable smart energy products offered by different companies. The paper finally highlights possible challenges in the design process of future energy management systems like the need for interoperability and network security.

1) Based on control information: This class deals with the way in which decisions for the execution of DR programs are made. Further, we divide this class into two sub-classes; centralized and distributed [14], [15], [16], [17], [18], [19], [20], [21]. Both sub-classes are discussed as follows.

III.

DR P ROGRAMS : FROM C LASSIFICATION O PTIMIZATION T ECHNIQUES

Reduction in consumer cost.

•

Increase in retailer profit.

•

Reduction in PAR load ratio.

•

Reduction in carbon discharge.

a) Centralized programs: In these programs, the DR program is coordinated/monitored by a central controller in a way that it collects demand related information from the consumers. Based on this information, DR decisions are then made to schedule demand. Applications areas of centralized DR programs include thermostatically controlled appliances, centralized charging stations (for PHEVs), buildings, etc. -Advantages: reduction of deployment cost, homogeneity in action, standardized procedure, better coordination, etc. -Disadvantages: delayed communication, high malfunction probability due to complete dependency on the central controller, etc.

TO

US department of energy defines DR as “a tariff or program established to motivate changes in electric use by end-use customers, in response to changes in the price of electricity over time, or to give incentive payments designed to induce lower electricity use at times of high market prices or when grid reliability is jeopardized” [11]. DR leads to economic benefits not only for customers but also for the utility. In this regard, one of the mega trends in DR is customer evolution. Now-a- days customers have more options for managing electricity that leads towards the adoption of on-site improved generation technologies, energy management with DR, and energy storage. Thus, creating a situation in which customers hold the option to serve all or some part of their own needs. In addition, they can potentially sell excess energy/services to the market. Thereby, giving rise to a new class of customers known as “prosumers” who not only produce but also consume electricity [12]. Following are the major DR objectives [13]. •

Although, there are many attempts made in literature for the classification of DR programs. However, we present a more detailed classification of DR programs which is based on control information, decision variable, and offered motivation (refer figure 1).

Participation of customers in DR leads toward three possibilities in which customers are able to change their electricity usage; (i) use on-site standby energy generation, (ii) move load (energy consumption) to a different slot in time, and (iii) use load curtailment strategies [12], [13]. Subjected to these, research community aims on the utilization of optimization techniques. However, prior to the discussion of optimization techniques in DR, we focus on the classification of DR programs.

b) Distributed programs: In these programs, the demand information is not centrally collected such that consumers have the ability to directly access grid state indicators. Thus, enabling the consumers to react during system’s critical state(s). Application areas of distributed DR programs include congestion pricing in internet traffic control, distributed charging systems for PHEVs, etc. -Advantages: faster communication among peer devices, less system malfunction probability, etc. -Disadvantages: increased deployment cost, less or no uniformity/homogeneity in action, difficult coordination/synchronization, etc. Remarks: Whether to select a centralized or a distributed DR program, well there are many legitimate arguments in favor of both. In fact, in micro grids where home area network devices are expected to act with greater freedom (multi-tasking), decentralization makes sense to allow flexibility. However, in larger grids, decentralization may result in inefficiencies and redundancies. From consumers point of view, a decentralized utility may be less consistent in terms of quality of service. In such situations, centralization seems to be the best choice. 2) Based on decision variable: DR programs can be also sub-classified as per decision variable into event based and scheduling based programs [22], [23], [24], [25], [26], [27]. Both sub-classes are explained as follows. a) Event based programs: The key function of event based DR programs is control of the activation time of the requested loads. In other words, these DR programs aim at when to activate the given/requested loads. Application areas of these DR programs exist where reduction in power consumption is subjected; load shifting from on-peak hours to off-peak hours. An upper bound is typically realized that should not be reached in peak hours. -Advantages: easy to implement (only event monitoring is required), robust in response, less generated overhead, etc. -Disadvantages: consideration of multiple events makes these

Fig. 1.

Classification of DR programs

programs difficult to implement with relatively more overhead and slow response time. b) Energy management/scheduling based programs: These programs are subjected to reduce the power consumption of certain/specific loads in a way that the aggregate power consumption is reduced. Here, both on-peak and off-peak hours are considered. Realization of these DR programs is made by deciding the amount of energy that can be allocated to each appliance (consumer) during each time slot such that appliance’s operation is controlled to consume relatively less power when the system is under stress. Application areas of these DR programs are typically bounded by quality of experience, i.e., customer satisfaction degree on the performance of grid. -Advantages: only centralized control is needed, reduced deployment cost, uniformity in action (i.e., a standard procedure is followed), -Disadvantages: relatively difficult to implement, time drift between any two consecutive slots leads to disturbed system performance, etc. Remarks: If the number of events are relatively less then event based programs are preferred because such programs only wait for something to happen. However, with more events in number management/scheduling based programs are preferred due to easy synchronization. A more precise approach would stress on using a hybrid program. 3) Based on offered motivation: Another way to classify DR programs is the motivation offered to customers in response to their efforts that were made to shift/reduce their power demands. In this regard, following are the two subclasses with details [28], [29], [30], [31], [32]. a) Price/rate based programs: In these programs customers are offered time varying prices based on electricity cost in different time slots. Customers receive the time varying information based on which they decide when to consume

how much power. The pricing model can either be static or dynamic. The former category further include flat tiered and time of use pricing models. Whereas, the later category include critical peak pricing and real time pricing models. -Advantages: Customers can reduce their electricity bills, utilities can overcome the peak hour issue by setting higher rates in these hours, etc. -Disadvantage: Due to greedy nature of customers, there are chances of creating peak in off-peak hours, etc. b) Incentive based programs: In these programs, utilities offer either fixed or time varying incentives to customers, thereby, reducing power consumption during system stress conditions. These programs are further sub-classified into classical and market based. The leading one, usually offers participation payments to customers in the form of discounted rates or bill credits. The lagging one offers reward customers in the form of money depending on their performance during stress conditions. -Advantages: utilities can overcome the peak hour issue by setting higher incentives in these hours, customers happily participate in these programs which ultimately leads to customer satisfaction, etc. -Disadvantage: Although most of the incentive based programs offer voluntary customer participation, however, once events are decided, some of these programs also penalize customers that fail in terms of contractual response. Remarks: The price/rate based programs are usually more suitable for residential customers, whereas, the incentive based programs are more suitable for industrial consumers. B. Optimization techniques in DR A mathematical optimization problem has the form; min f0 (x) OR max f0 (x)

(1)

fi (x) ≤ ci ∀i ∈ Z +

(2)

subject to:

TABLE I. Optimization target

Min Cost

Min Aggregated Power

Min (Cost and Power)

DR OPTIMIZATION PROGRAMS Ref. # [36] [37] [38] [39] [40] [41] [42] [43] [44] [45] [47] [48] [49]

Optimization method Integer Linear Program–Author’s software Mixed Integer Linear Program–Simulated annealing Mixed Integer Non-Linear Program–Commercial software Convex Optimization–Relaxed convex programming technique Heuristics–Greedy search algorithm Heuristics–Evolutionary algorithm Integer Linear Program–Branch and bound method Convex Optimization–Parallel distribution computation Particle Swarm Optimization–Signalled PSO Mixed Integer Linear Program–Model predictive control Mixed Integer Linear Program–Commercial software Mixed Integer Linear Program Game Theory–Author’s software

where x = (x1 , ..., xn ) is the optimization variable of the problem, f0 : Rn → R is the objective function, fi : Rn → R are the constraints or constraint functions, and bi are the limits or bounds of the problem for constraints. x∗ is called optimal solution of the problem if it has the smallest or greatest (to minimize or maximize the objective function) objective value among all vectors that satisfy the constraints, i.e., for any z with f1 (z) ≤ b1 , ..., fn (z) ≤ bn , we have f0 (z) ≥ f0 (z ∗ ). The target of a DR optimization problem is to: •

Minimize the electricity cost.

•

Minimize the aggregated power consumption.

•

Minimize both (electricity cost and aggregated power consumption).

In the first as well as second case, a single objective function is needed and the third case deals with the optimization of two objective functions. 1) Minimize the electricity cost: Formulation of cost minimization problem hold its basis in the derivation of a procedure for optimal load scheduling that in turn depends on appropriate pricing model(s). Thus, the main challenge is to design of an optimal load scheduling technique while considering consumers’ needs (i.e., comfort level). Subjected to electricity cost minimization, there are many research articles [36], [37], [38], [39], [40], [41]. [36] uses Integer Linear Programming to minimize electricity cost of not only a single house but also of multiple houses. In both cases, the proposed Integer Linear Programming based techniques decide the schedules for switching ON/OFF appliances, and activation/deactivation of generators. An important feature of the proposed technique is preservation of consumers’ comfort via prediction errors. However, the proposed technique does not account for time slots beyond set window which leads to sub-optimal solutions. Similarly, [37] and [38] use Mixed Integer Linear Programming and Mixed Integer Non-Linear Programming to minimize the electricity cost. In the same way, [39] uses Convex Programming knowing that it can be applied to a relatively large number of consumers. The authors relax binary decision variables (associated with appliances’ status) from integer to continuous domain. The aforementioned optimization techniques yield efficient solution(s). However, a major issue with these techniques is high time complexity whenever the number of consumers are increased. In such situations, heuristic based techniques can not only provide fast but also near optimal solutions. For example, [40] uses a greedy search heuristic to effectively flatten the demand curve, and [41] minimizes

the utility bills of consumers (residential, commercial and industrial) by using heuristic based evolutionary algorithm. 2) Minimize the aggregated power consumption: By finding an optimal scheduling solution, the consumers can usually minimize their aggregated power consumption by utilizing the found schedules in on-peak and off-peak hours. Incentives, offered by the utilities to consumers, play a very important role here. When different appliances with distinct power consumption demands are under consideration, the scheduling decisions must take into account the applied pricing model, consumers’ preferences, flexible and non-flexible loads, and other constraints. In this regard, many optimization techniques are proposed [42], [43], [44], [45]. [42] uses Integer Linear Programming to optimally schedule the daily load with the consideration of different load types. Their proposed solution is equally valid for for residential as well as local areas (in residential environment and local area, the scheduling decisions are made by the home energy management unit and central controller, respectively). Similarly, [45] uses Mixed Integer Linear Programming to optimally schedule the load This work also factors in power storage devices and renewable generators. Convex Optimization technique is used in [43] to effectively reduce the associated time complexity and communication cost. By using a parallel optimization, this technique accounts for the distributed power generation sources as well as the electricity prices. Authors in [44] uses a heuristic approach, Signaled Particle Swarm Optimization, that is valid for relatively large number of consumers with reduced execution time as well as absolute error. 3) Minimize both (electricity cost and aggregated power consumption: Typically, there are two solution methods to optimization problems which consider multiple objective functions; decomposition and aggregated weight functions [46]. The leading one decouples the power system into subsystems. These subsystems, with reduced complexity, are then independently optimized. The lagging one combines the objective functions into a single one such that weights are assigned to each function with respect to its significance in the original objective. [47] uses Mixed Integer Linear Programming to solve the dual problem. Besides the consideration of distributed energy resources, the proposed technique factors in a single household only. [48], on the other hand, factors in 60 residential users, each one with three controllable loads. Moreover, in this technique, customers’ participation in the DR program is motivated by incentives. In [49], a two level optimization technique is proposed to solve the dual problem. The authors use Game Theoretic model in the upper level and Convex

TABLE II.

C OMPARISON OF DR OPTIMIZATION TECHNIQUES (+ = Ref. #

Multiple load types

Response time

Scalability

[37] [38] [39] [40] [42] [44] [47] [48] [49]

not supported supported supported supported not supported not supported supported supported supported

+++ +++ ++ +++ + ++ + ++ ++

+++ +++ +++ +++ ++ +++ + ++ +

LOW,

++ = MEDIUM , AND +++ =

HIGH )

Communication requirement +++ +++ +++ + ++ ++ + +++ +

Optimization in the lower level. The upper level aims to capture interaction among players subjected to an optimal demand schedule that maximizes the long term payoff. The lower level aims at an optimal solution for the scheduling of each user.

programs should also assure the methods by which consumers are charged to achieve fairness.

Remarks: Depending on the characteristics of objective function and its constraints (refer table I), we can categorize the optimization techniques in DR as linear, non linear, convex, etc. The linear as well as non linear problems can be either integer or mixed integer. Furthermore, based on the nature of the involved variables optimization problem are stochastic or deterministic. The former case deals renewable energy resources. Thus, based on the type of optimization problem, a specific technique is either defined or adopted to derive the solution. Therefore, for a mixed integer non linear problem, a mixed integer non linear technique is applied to derive an optimal solution. It is worth noting that optimization does not necessarily mean exact reachability to an optimum solution because there are optimization problems like NP-hard, where an optimal solution is not always feasible to find or the computational time is very high. In these situations, classical optimization techniques like linear or quadratic programming are not applicable. Therefore, due to fast convergence rate and near optimal solution, heuristic optimization techniques are preferred (refer table I and table II).

[1]

R EFERENCES

[2]

[3]

[4]

[5] [6]

[7]

[8]

IV.

C ONCLUSION

Conventional grid is outdated and needs renovation. By using the latest technology and implementing new strategies, it is possible to increase grid capacity, efficiency, reliability, power quality, and sustainability. Smart grid has the potential to fulfill these requirements by exploiting two way communications between consumer and retailer. In this article, a general overview of the latest DR techniques is presented. Firstly, the DR programs are classified on the bases of control information, decision variable and offered motivation. Secondly, different DR optimization techniques are analyzed in terms of the solution methods, response time, scalability, communication requirement and multiple load types support. From this study, we conclude that successful implementation of DR programs majorally depends on consumers’ participation. This participation does not means that they have to compromise their comfort level. It is up to the consumers to decide their degree of participation in these programs. However, depending on the contribution of consumers to achieve desired objectives, the DR programs do not assure rewards because some consumers are also penalized (pay high prices in peak hours for base load) whereas the non participants get unfair benefit. Thus, in addition to the minimization or maximization objective, the DR

[9]

[10]

[11]

[12]

[13] [14]

[15]

[16]

L. Gelazanskas, and K. A. Gamage, “Demand side management in smart grid: A review and proposals for future direction”, Sustainable Cities and Society 11 (2014): 22-30. M. Hashmi, S. Hanninen, and K. Maki, “Survey of smart grid concepts, architectures, and technological demonstrations worldwide”, Innovative Smart Grid Technologies (ISGT Latin America), 2011 IEEE PES Conference on. IEEE, 2011. T. Hubert, and S. Grijalva, “Modeling for Residential Electricity Optimization in Dynamic Pricing Environments”, Smart Grid, IEEE Transactions on 3.4 (2012): 2224-2231. Z. Zhao, W-C. Lee, Y. Shin, and K-B. Song “An Optimal Power Scheduling Method for Demand Response in Home Energy Management System”, Smart Grid, IEEE Transactions on 4.3 (2013): 1391-1400. P. Siano, “Demand response and smart gridsA survey”, Renewable and Sustainable Energy Reviews 30 (2014): 461-478. N. Motegi, M. A. Piette, D. S. Watson, S. Kiliccote, and P. Xu, “Introduction to commercial building control strategies and techniques for demand response”, Lawrence Berkeley National Laboratory LBNL59975 (2007). S. Mohagheghi, J. Stoupis, Z. Wang, Z. Li, and H. Kazemzadeh, “Demand response architecture: Integration into the distribution management system”, Smart Grid Communications (SmartGridComm), 2010 First IEEE International Conference on. IEEE, 2010. J. Aghaei, and M-I. Alizadeh, “Demand response in smart electricity grids equipped with renewable energy sources: A review”, Renewable and Sustainable Energy Reviews 18 (2013): 64-72. R. R. Mohassel, A. Fung, F. Mohammadi, and K. Raahemifar, “A survey on advanced metering infrastructure”, International Journal of Electrical Power and Energy Systems 63 (2014): 473-484. Z. M. Fadlullah, Y. Nozaki, A. Takeuchi, and N. Kato, “A survey of game theoretic approaches in smart grid”, Wireless Communications and Signal Processing (WCSP), 2011 International Conference on. IEEE, 2011. US Department of Energy, Benefits of Demand Response in Electricity Markets and Recommendations for Achieving them, report to the United States Congress, Feb. 2006. P. D. Martini, “A Future of Customer Response”, DR 2.0, Newport Consulting, Prepared for the Association for Demand Response and Smart Grid, USA, Department of Energy, 2013. P. Siano, “Demand response and smart gridsA survey”., Renewable and Sustainable Energy Reviews, 30 (2014) 461478. M. Alizadeh, A. Scaglione, and R. J. Thomas, “From packet to power switching: Digital direct load scheduling”, IEEE J. Sel. Areas. Commun., vol. 30, no. 6, pp. 1027-1036, Jul. 2012. H. Hildmann, and F. Saffre, “Influence of variable supply and load flexibility on demand-side management”, in Proc. 8th IEEE EEM, pp. 63-68, Zagreb, Croatia, 25-27 May 2011. N. Lu and Y. Zhang, “Design Considerations of a Centralized Load Controller Using Thermostatically Controlled Appliances for Continuous Regulation Reserves”, IEEE Trans. Smart Grid, vol. 4, no. 2, pp. 914921, June 2013.

[17]

P. Palensky, G. Zucker, F. Judex, R. Braun, F. Kupzog, T. Gamauf, and J. Haase, “Demand response with functional buildings using simplified process models”, in Proc. 37th IEEE IECON, pp. 3230- 3235, Melbourne, Australia, 7-10 Nov. 2011.

Z. Tan, P. Yang and A. Nehorai, “An Optimal and Distributed Demand Response Strategy With Electric Vehicles in the Smart Grid”, IEEE Trans. Smart Grid, vol. 5, no. 2, pp. 861-869, Mar. 2014. [19] H. Hermanns and H. Wiechmann, “Demand-Response Management for Dependable Power Grids”, Embedded Syst. Smart Appliances Energy Managem, vol. 3, pp. 1-22, 2013. [20] Z. Fan, “Distributed demand response and user adaptation in smart grids”, in Proc. IFIP/IEEE IM 2011, pp. 726-729, Dublin, Ireland, 2327 May 2011.

[38]

[39]

[18]

[21]

Z. Fan, “A distributed demand response algorithm and its application to PHEV charging in smart grids”, IEEE Trans. Smart Grid, vol. 3, no. 3, pp. 1280-1290, Sept. 2012. [22] I. Koutsopoulos, and L. Tassiulas, “Optimal Control Policies for Power Demand Scheduling in the Smart Grid”, IEEE J. Sel. Areas Commun., vol. 30, no. 6, pp. 1049-1060, Jul. 2012. [23] G. Xiong, C. Chen, S. Kishore,and A. Yener, “Smart (in-home) power scheduling for demand response on the smart grid”, in Proc. IEEE PES ISGT 2011, Manchester, UK, 5-7 Dec. 2011. [24]

M. Rastegar, M. Fotuhi-Firuzabad, and F. Aminifar, “Load commitment in a smart home”, Appl. Energy, vol. 96, pp. 45-54, Aug. 2012.

[25]

M. Shinwari, A. Youssef, and W. Hamouda, “A Water-Filling Based Scheduling Algorithm for the Smart Grid”, IEEE Trans. Smart Grid, vol.3, no. 2, pp. 710-719, Jun. 2012. N. Gatsis and G. Giannakis, “Cooperative multi-residence demand response scheduling”, in Proc. 45th Annu. Conf. Inf. Sci. Syst., Baltimore, MD, USA, 23-25 Mar. 2011. A.-H. Mohsenian-Rad, V. Wong, J. Jatskevich, and R. Schober, “Optimal and autonomous incentive-based energy consumption scheduling algorithm for smart grid”, in Proc. IEEE PES ISGT 2010, Gothenburg, Sweden, 11-13 Oct. 2010. J. Aghaei and M.-I. Alizadeh, “Demand response in smart electricity grids equipped with renewable energy sources: A review”, Renew. Sustain. Energy. Rev., vol. 18, pp. 64-72, Feb. 2013. M. Doostizadeh and H. Ghasemi, “A day-ahead electricity pricing model based on smart metering and demand-side management”, Energy, vol. 46, no. 1, pp. 221-230, Oct. 2012.

[26]

[27]

[28]

[29]

[30]

K. Herter and S.Wayland, “Residential response to critical-peak pricing of electricity: California evidence”, Energy, vol. 35, no. 4, pp. 15611567, Apr. 2010. [31] S. Mohagheghi, J. Stoupis, Z. Wang, Z. Li, and H. Kazemzadeh, “Demand response architecture: Integration into the distribution management system”, in Proc. IEEE SmartGridComm 2010, pp. 501-506, Gaithersurg, MA, USA, 4-6 Oct. 2010.

[32]

M. Parvania, and M. Fotuhi-Firuzabad, “Demand response Scheduling by Stochastic SCUC”, IEEE Trans. Smart Grid, vol. 1, no. 1, pp. 89-98, Jun. 2010. [33] M. R. Alam, M. B. I. Reaz, and M. A. M. Ali, “A review of smart homesPast, present, and future”, Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on 42.6 (2012): 11901203. [34]

P. G. D. Silva, S. Karnouskos, and D. Ilic, “A survey towards understanding residential prosumers in smart grid neighbourhoods”, Innovative Smart Grid Technologies (ISGT Europe), 2012 3rd IEEE PES International Conference and Exhibition on. IEEE, 2012. [35] A. Kailas, V. Cecchi, and A. Mukherjee, “A survey of communications and networking technologies for energy management in buildings and home automation”, Journal of Computer Networks and Communications 2012 (2012). [36] A. Molderink, V. Bakker, M. G. Bosman, J. L. Hurink, and G. J. Smit, “Domestic energy management methodology for optimizing efficiency in smart grids”, in Proc. IEEE PowerTech 2009, Bucharest, Romania, 28 Jun.-2 Jul (2009). [37]

T. Sousa, H. Morais, Z. Vale, P. Faria, and J. Soares, “Intelligent energy resource management considering vehicle-to-grid: A simulated annealing approach”, IEEE Trans. Smart Grid, vol. 3, no. 1, pp. 535-542, Mar. 2012.

[40]

[41]

[42]

[43]

[44]

[45] [46] [47]

[48]

[49]

J. Soares, T. Sousa, H. Morais, Z. Vale, and P. Faria, “An optimal scheduling problem in distribution networks considering V2G”, in Proc.IEEE CIASG 2011, Paris, France, 11-15 Apr. 2011. K. M. Tsui and S. C. Chan, “Demand Response Optimization for Smart Home Scheduling Under Real-Time Pricing”, IEEE Trans. Smart Grid, vol. 3, no. 4, pp. 1812-1821, Dec. 2012. J. Xiao, J. Y. Chung, J. Li, R. Boutaba, and J. K. Hong, “Near optimal demand-side energy management under real-time demand-response pricing”, in Proc. IEEE CNSM 2010, Niagara Falls, Canada, 25-29 Oct. 2010. T. Logenthiran, D. Srinivasan, and T. Z. Shun, “Demand side management in smart grid using heuristic optimization”, IEEE Trans. Smart Grid, vol. 3, no. 3, pp. 1244-1252, Sept. 2012. Z. Zhu, J. Tang, S. Lambotharan, W. H. Chin, and Z. Fan, “An integer linear programming based optimization for home demandside management in smart grid”, in Proc. IEEE PES ISGT 2012, Washington, DC, USA, 16-20 Jan. 2012. P. Yang, P. Chavali, P. and A Nehorai, “Parallel autonomous optimization of demand response with renewable distributed generators”,in Proc SmartGridComm 2012, pp. 55-60, Tainan City, Taiwan, 5-8 Nov. 2012. J. Soares, M. Silva, T. Sousa, Z. Vale, and H. Morais, “Distributed energy resource short-term scheduling using Signaled Particle Swarm Optimization”, Energy, vol. 42, no. 1, pp. 466-476, Jun. 2012. P. O. Kriett and M. Salani, “Optimal control of a residential microgrid”, Energy, vol. 82, no. 1, pp. 321-330, Jun. 2012. D. E. Goldberg, “Genetic algorithms in search optimization and machine learning”, Addison-Wesley, 1989. J. Wang, Z. Sun, Y. Zhou, and J. Dai, “Optimal dispatching model of Smart Home Energy Management System”, in Proc. IEEE PES ISGT ASIA 2012, Tianjin, China, 21-24 May 2012. T. H. Chang, M. Alizadeh, and A. Scaglione, “Coordinated home energy management for real-time power balancing”, in Proc. IEEE PES Gen. Meet. 2012, San Diego, CA, USA, 22-26 Jul. 2012. Q. Zhu, Z. Han, and T. Basar, “A differential game approach to distributed demand side management in smart grid”, in Proc. IEEE ICC 2012, pp. 3345-3350, Ottawa, Canada, 10-15 Jun. 2012.