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Energy Management and Renewable Energy Integration in Smart Grid System Mohamed A. Mohamed1, Ali M. Eltamaly2, **, SMIEEE, Hassan M. Farh3, and Abdulrahman I. Alolah4, SMIEEE Abstract- Smart grid is a concept by which the existing electrical grid infrastructure is being upgraded with integration of multiple technologies such as, two-way power flow, two-way communication, automated sensors, advanced automated controls and forecasting system. Smart grid enables interaction between the consumer and utility which allow the optimal usage of energy based on environmental, price preferences and system technical issues. This enables the grid to be more reliable, efficient and secure, while reducing greenhouse gases. This paper presents a survey of the recent literature on integrating renewable energy sources into smart grid system. Various management objectives, such as improving energy efficiency, maximizing utilization, reducing cost, and controlling emission have been explored.

I. INTRODUCTION

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owadays, increasing energy demand and dependence on fossil fuel become an important issue facing the world. Therefore, there is a big trend for the use of renewable energy sources to address electricity generation. But, as the penetration of renewable energy sources increases, serious improvements and modifications for the existing electric grid would be needed to accommodate and integrate these intermitted nature sources. Smart grid is a system to add monitoring, management, control and communication capabilities to the national electrical delivery infrastructure to move electricity around the system as efficiently and economically as possible [1-3]. Many examples of smart grid system are available in many countries, such as, U.S., Germany, Canada, Japan, China and India [4], [5]. With the use of smart grid, smart home technologies and time-varying energy pricing models, there is need for smart energy management system [6], [7]. This system respond to varying cost of energy, by reducing or shaving the peak demand automatically, reducing the number of required standby power plants, and saving millions of dollars for the utility and user which are very much possible in a smart grid scenarios [8]. A comparison between the traditional grid and smart grid is shown in Fig.1. Reviews of recent work in smart grid system have been done to indicate the promising potential of this system in the future. The paper presents a survey on the recent literature to study the opportunities and challenges of integrating renewable energy into smart grid system. A survey on smart grid control, distributed generation, demand response, energy efficiency and emission control has been introduced.

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Electrical Engineering Dept., King Saud University, Riyadh, Saudi Arabia, [email protected], [email protected] 2 Sustainable Energy Technology Center, King Saud University, Riyadh, Saudi Arabia, [email protected] 3 College of Engineering Research Center, King Saud University, Riyadh, Saudi Arabia, [email protected] ** Electrical Engineering Dept., Mansoura University, Mansoura, Egypt.

II. SMART GRID CHALLENGES There are many challenges that can be addressed based on smart grid technology which can be summarized in Fig.2. Renewable energy integration and energy management are the major challenges for developers and practitioners of smart grid system [9]. Therefore, these challenges were our interest in this paper. The following sections present and discuss the research work on these topics. III. RENEWABLE ENERGY INTEGRATION Renewable energy is a promising option for electricity generation especially the solar PV and wind energy systems as they are clean energy sources and became overripe technology. Today, the integration of renewable energy sources into smart grid system is increasingly gaining importance and widely studied by many researchers [1013]. The following sections survey the work done on renewable energy system integration, distributed generation, smart grid technologies and smart metering infrastructure. A. Renewable Energy and Smart Grid Interfacing Options Integrating renewable energy sources into the smart grid system enabling reduction the cost of sources required for building extra generators, improved power quality, reliability and achieve the customer satisfaction [14-16]. Geviano et al. [17] surveyed and summarized the smart grid applications for renewable energy generation and its potential study in the future. The authors ensured that the communication between the electronic devices is a key technology in order to adapt renewable energies to the future grid infrastructure. Kohsri et al. [18] presented an energy management and control system for smart renewable energy generation. They used LAB-View technology as a basic design for the overall system. Their proposed prototype is constructed of 1.8 kW PV, 18 kWh Battery and 5 kW Generator. The system itself can forecast and make a decision for future power management. Ayompe et al. [19] presented a validated real-time energy models for small-scale grid-connected PV systems suitable for domestic application. The models were used to predict real-time AC power output from a PV system in Dublin, Ireland using 30-min intervals of measured performance data between April 2009 and March 2010. Their proposed models are suitable for predicting PV system AC output power at time intervals suitable for smart metering. B. Smart Grid Technology Development and Demonstration Smart grid control gives the capability of maintaining system operation, predicting system behavior, anticipatory operation, reduce the cost of operation, handling distributed resource, security, stochastic demand and optimal response to smart appliances [20],[21]. The self-managing and reliable smart grid is seen as the future of protection and control systems [22], [23].

•Sensors all over •Digital system •Distributed generation •Self monitoring - Self restoration •Two-way communication •Adaptive and islanding •Prevalence control - Remote test •Many customer choices

•Few sensors •Electromechanical system •Centralized generation •Manual monitoring-Manual restoration •One-way communication •Failures and blackouts •Limited control - Manual test •Limited customer choices

Smart Grid

Traditional Grid

installations to create an endd-to-end smart grid control center. In this lab, a variety of experiments, tests, and validation efforts can be programmed and carried out. For a long time the term smaart grid, especially from the markets’ side, has been synonyymous to smart metering, the smart metering infrastructure being the main focus of discussion involving smart gridds [26]. Smart metering can offers manny potential benefits for the customer and utility as well. For F customer, smart metering provides a real-time pricing, whhich enables them to manage their energy consumptions to reeduce bills. For the utility, it can use the customer’s demandd profile and manage the peak demand shaving, outage detectiion and islanding [27]. Large amount of data and infformation will be generated from meters, monitors and othher remote digital electronic devices, which can be reaached through a two-way communication network. Theerefore, smart grid support advanced information managem ment to organize these data and information and make them m accessible to various users within the utility organization [228]. IV. SMART ENERGY MA ANAGEMENT SYSTEM

Fig.1. Comparison between traditional gridd and smart grid. •Improved reliability & Efficiency

•Optimize asset utilization & Self healing Renewable integration & Energy storag

Two-wayy power flow

A. Management Objectives With the framework of smaart grid and SEMS, many management challenges becom me possible and easy. So far, the works for SEMS mainlly focus on the following objectives:

Smart Grid Advanced meter infractructure

•Increased energy security & Power quality

The Smart Energy Managem ment System (SEMS) uses advanced technologies to streaamline processes, cut down costs, reduce energy consum mption, improving demand profile, control carbon emissionns and enhance efficiency to the energy industry, both to t utilities and consumers [29],[30]. SEMS has been firrstly classified according to management objectives and theen according to management methods and tools.

Energy ent manageme

•Reduced green house gas emissions

Fig.2. Smart grid challenges and benefits. b

This philosophy requires finding a way to implement it in the laboratory scale and then in the reeal power system. Therefore, considerable number of researcches is going on in this field to study and identify the issues involved in smart grid operation. Such as FREEDM system m which proposed by the NSF FREEDM Systems Center, Raleigh, R NC, [24]. This system is a power distribution systtem that interfaces with residential and industry customers.. Also, a research development and demonstration micro-griid was installed on the Burnaby campus of the British Columbia Institute of Technology (BCIT) in Vancouver, British B Columbia, Canada. The BCIT’s smart micro-grid iss a test bed where multitudes components, technologies, annd applications of smart grid are integrated to qualify the merits m of different solutions, showcase their capabilities, and a accelerate the commercialization of technologies and sollutions for it [25]. The BCIT’s smart grid development Lab includes powerful servers, routers, analyzers, and networkiing equipment and integrated with multiple base stations annd smart-metering

1) Energy Efficiency Minimize the system energy loss l is one of the important objectives of the SEMS. Thherefore, Atwa et al. [31] proposed a methodology appplied to a typical rural distribution system for optimallly allocating different types of renewable distributed genneration (DG) units in the distribution system to minimiize the annual energy loss. Their methodology is formulateed as mixed integer nonlinear programming (MINLP); with coonstraints include the voltage limits, the feeders' capacity, thee maximum penetration limit, and the discrete size of the available DG units. Ochoa and Harrison [32] used a multi-period alternative current optimal power flow (OP PF) to determine the optimal accommodation of renewable DG D to minimizes the system energy losses considering timee-varying characteristics. The authors also investigated thee trade-off between energy losses and more generation capaacity. Aquino-Lugo and Overbye [33] [ used the agent based technologies to implement deceentralized control algorithms to minimize power losses in thee distribution grids. 2) Demand Profile Improvement Demand profile shaping can be b accomplished by shifting, scheduling, or reducing dem mand in order to obtain a smoothed demand profile, or reduce peak demand of the total energy demand. This, in turn, will reduce the overall

plant and capital cost requirements, and also will increase the system reliability [34], [35]. The following are the research works related to this trend. Caron and Kesidis [36] proposed a dynamic pricing scheme incentivizing consumers to achieve an aggregate load profile suitable for utilities, and studied how close they can get to an ideal flat profile depending on how much information they share. In addition, they provided distributed stochastic strategies that successfully exploit this information to improve the overall load profile when users have only access to the instantaneous total load on the grid. Bakker et al. [37] designed a three step control and optimization strategy and focused on the control algorithms used to reshape the energy demand profile of a large group of buildings and their requirements on the smart grid. They considered the amount of available communication bandwidth and exploited the available computation power distributed in the grid. Earle et al. [38] proposed an approach to measure the capacity impacts of demand response by California Statewide Pricing Pilot (SPP). They found that the uncertainty of the level of response is likely to have little effect on the capacity and reliability value of the demand response program. Kishore and Snyder [39] first presented a simple optimization model for determining the timing of appliance operation to take advantage of lower electricity rates during off-peak periods. They proposed a distributed scheduling mechanism to reduce peak demand within a neighborhood of homes. Their mechanism provides homes a guaranteed base level of power and allows them to compete for additional power to meet their needs. Finally, they introduced a more powerful energy management controllers (EMCs) optimization model, based on dynamic programming, which accounts for the potential for electricity capacity constraints. Mohsenian-Rad and Leon-Garcia [40] proposed an optimal and automatic residential energy consumption scheduling framework, which attempts to achieve a desired trade-off between customers who are more willing to reduce their aggregate demand over the entire horizon, rather than shifting their load to off-peak periods, which tend to receive higher incentives, and vice versa. Ibars et al. [41] used the network congestion game to smooth the electric load curve and avoid grid overloading. In this game each user allocates demand as a response to other users actions. Ghosh et al. [42] developed an optimization mechanism incentivizing the energy customers. This mechanism depends on the trade-off between minimizing the electricity bills and minimizing the waiting time for the operation of each device. Mohsenian-Rad et al. [43] discovered that by adopting pricing tariffs which differentiate the energy usage in time and level, the global optimal performance is achieved at Nash equilibrium of the formulated energy consumption scheduling game. 3) Cost Optimization, and Price Stabilization Increasing profit, reducing cost and improving utility, are also an important management challenges. Researchers realize these challenges in various levels and from various perspectives [44], such as literatures on individual user bill, profit and cost of electricity industry. For an example,

Conejo et al. [45] described an optimization model to adjust the hourly load level in response to hourly electricity prices. The objective of the model is to maximize the utility of the consumer subject to a minimum daily energy consumption level, maximum and minimum hourly load levels, and ramping limits on such load levels. The authors also modeled the price uncertainty using robust optimization techniques. Samadi et al. [46] proposed a novel real-time pricing algorithm for the future smart grid and focused on the interactions between the smart meters and the energy provider through the exchange of control messages which contain subscribers' energy consumption and the real-time price information. Each subscriber is equipped with an energy consumption controller (ECC) unit as part of its smart meter. Hatami and Pedram [47] formulated mathematically the electrical energy bill minimization problem for cooperative networked consumers who have a single energy bill, such as those working in a commercial/industrial building. They presented two different methods to minimize the energy cost of such users under non-interruptible or interruptible jobs. The methods relay on a quasi-dynamic pricing function for unit of energy consumed, which comprises of a base price and a penalty term. The methods minimize the energy cost of the users while meeting all the scheduling constraints and heeding the pricing function. Guan et al. [48] considered the scheduling problem of building energy supplies with the practical background of a low energy building. The objective function is to minimize the overall cost of electricity and natural gas for a building operation over a time horizon while satisfying the energy balance and complicated operating constraints of individual energy supply equipment and devices. In [49], [50] the authors focused on two of demand load control methods that aim at minimizing the grid operational cost. They considered online scheduling of power demand tasks that have time flexibility in being activated, in terms of a deadline. In addition, they discussed the use of stored energy for serving part of the demand at peak load times. Price stabilization is also an important topic in smart grid technology, as a cause of the volatility and instability of the wholesale market prices. Roozbehani et al. [51] proposed a mechanism for real-time pricing of electricity in smart power grids, with price stability as the primary concern. They developed a mathematical model for characterization of the dynamic evolution of supply, demand, and market clearing prices under real-time pricing. It is assumed that the real-time prices for retail consumers are derived from the Locational Marginal Prices (LMPs) of the wholesale balancing markets. Joe-Wong, et al. [52] presented a stabilizing pricing algorithm and proposed a mathematical scheme for characterizing the dynamic evolution of utility loads and market clearing prices under real-time pricing. 4) Emission Control Emission control is an important management objective in the electric power industry and has a significant influence on environment protection. Therefore, many researchers have investigated how to optimize emission reduction. Bakker et al. [53] presented a three step control strategy to optimize the overall energy efficiency and increase generation from renewable resources with the ultimate goal

to reduce the CO2 emission caused by electricity generation. In [54] Saber and Venayagamoorthy presented cost and emission reductions in a smart grid by maximum utilization of gridable vehicles (GVs) and renewable energy sources (RESs). They presented possible models for GV applications, including the smart grid model; these models offer the best potential for maximum utilization of RESs to reduce cost and emission from the electricity industry. Liu and Xu [55] developed a load dispatch model to minimize the emission due to oxides of nitrogen (NOx). This model takes into account both thermal generators and WTs. They derived a closed-form in terms of the incomplete gamma function (IGF) to characterize the impact of wind power. The model is implemented in a computer program and a set of numerical experiments for a standard test system is reported. B. Management Methods and Tools As seen above, management objectives are more important as a result of their great effect both on utility and consumers. Therefore researchers have adopted various methods and tools to solve these objectives. Currently, researchers mainly use optimization approaches, game theory and machine learning to solve these objectives. Next, some of the research works related to this topic. 1) Optimization Approaches Convex programming and dynamic programming are the commonly used mathematical tools for optimization. Kallitsis et al. [56] for an example, proposed a framework using convex programming for coupling the communication network of a smart grid with the power distribution network in an effort to better utilize the scarce energy resources. Sortomme et al. [57] developed three optimal charging algorithms to minimize the impacts of plug-in hybrid vehicle (PHEV) charging on the connected distribution system using convex programming. Anderson et al. [58] demonstrated the load and source control necessary to optimize management of distributed generation and storage within the smart grid using approximate dynamic programming (ADP) driven. Han et al. [59] proposed an aggregator that makes efficient use of the distributed power of electric vehicles to produce the desired grid-scale power for vehicle-to-grid (V2G) frequency regulation services. They applied the dynamic programming algorithm to compute the optimal charging control for each vehicle. Newsham et al. [60] and Faruqui et al. [61] presented a methodology for quantifying the benefits of dynamic pricing programs to customers and utilities and also they proposed an approach for quantifying customer response to dynamic pricing. Cui et al. [62] proposed a dynamic pricing framework incentivizing users to create full load profile appropriate for them and utilities, and almost approaching to an ideal flat profile. Other optimization techniques are used in the case of a time-varying process (i.e. renewable energy sources), such as, robust programming, stochastic programming and PSO. Clement-Nyns et al. [63] proposed coordinated charging using stochastic programming to minimize the power losses and to maximize the main grid load factor. They computed the optimal charging profile of PHEV by minimizing the power losses. Liu [64] developed a load dispatch model for

the system consisting of both thermal generators and WTs using stochastic programming. The stochastic wind power is included in the model as a constraint. 2) Game Theory Game theory is one of the strong analysis tools used for smart grid management. Because of the inability to predict the extent of users cooperative. With the use of game theory, effective schemes can be designed to cope with this case. For example, Ibars et al. [65] proposed a distributed solution based on a network congestion game, which can be demonstrated to converge in a finite number of steps to a pure Nash equilibrium solution. Their favorable result is that the optimal local solution of each selfish consumer is also the solution of a global objective. 3) Machine Learning Machine learning used to design and development algorithms which allow control systems to release behaviors based on empirical data, such as the data of phasor measurement unit (PMU). Researchers believed that machine learning will play an important role in the analysis and processing of user data and grid states. In [66] Fang et al. used online machine learning to find the one among the distributed renewable energy resources (DRERs) in a microgrid, which can supply the power most efficiently, effectively and reliably, as its power supply source. In order to solve this problem, they first proposed a distributed DRER discovery approach to discover all the available DRERs within a microgrid. Furthermore, they proposed two distributed algorithms according to the information the user can obtain, in order to compute a good DRER access strategy, with no assumption on what distribution the power patterns of the DRERs follow. V. CONCLUSION The power system operators and planners still face the challenge of deep integrating renewable energy sources into the electricity grid. This challenge offers a rich set of research problems, many of which require systems, programs and control methods in their solution. Smart grid incorporates multiple technologies to form an intelligent system aims at achieve this challenge. Due to the potential importance of smart grid, this paper introduced a survey on the smart grid challenges, technologies, optimization and the smart management system. This survey explores the challenges and technologies used in integrating smart grid with renewable energy sources and achieve the demand side management. For the smart grid integration, the works on implementation of smart grid renewable energy system, distributed generation, smart grid technologies and smart metering system have been reviewed. For demand side management, The works on improving energy efficiency, demand profile improvement, reduce costs and emissions, real time pricing, economic dispatch, load forecasting, load shifting and increase utility, based on the advanced smart grid smart infrastructure have been reviewed. In summary, there is no doubt that within the advanced framework of smart grid, many challenges, services and applications which are tricky to be achieved in existing power grids, have become more easy and possible which will lead to a more environmentally sound future, better power supply services, and full improvement in our daily lives.

measurement systems," IEEE Transactions on Smart Grid, vol. This survey comprehensively discussed the recent literature 2 (1), pp. 206-211, 2011. on renewable energy integration and smart management system and outline challenges and future research directions [21] Molderink, A., et al., "Management and control of domestic smart grid technology," IEEE Transactions on Smart Grid, vol. for this system. The novelty of this survey is in the 1 (2), pp. 109-119, 2010. classification, volume of information provided, and [22] Liserre, M., et al., "Future energy systems: Integrating outlining of future research in this system. This would be renewable energy sources into the smart power grid through useful for researchers, developers, practitioners of smart industrial electronics," Industrial Electronics Magazine, IEEE, grid. vol. 4 (1), pp. 18-37, 2010.

REFERENCE [1] Zhang, P., et al., "Next-generation monitoring, analysis, and control for the future smart control center," IEEE Transactions on Smart Grid, vol. 1 (2), pp. 186-192, 2010. [2] Chen, S.-y., et al., "Survey on smart grid technology," Power System Technology, vol. 33 (8), pp. 1-7, 2009. [3] "IEEE Guide for Smart Grid Interoperability of Energy Technology and Information Technology Operation with the Electric Power System (EPS), End-Use Applications, and Loads," IEEE Standards Coordinating Committee 21, 2011. [4] Wenyuan, Z. D. Y. L. M., "Development Strategies of Smart Grid in China and Abroad," Proceedings of the Chinese Society of Electrical Engineering, vol. 33 (31), pp. I0001I0028, 2013. [5] Balijepalli, V. M., et al., "Deployment of microgrids in India," IEEE Power and Energy Society General Meeting, 2010, pp. 17. [6] Siano, P., et al., "Strategic placement of wind turbines in smart grids," International Journal of Emerging Electric Power Systems, vol. 13 (2), pp. 1-23, 2012. [7] Garrity, T. F., "Getting smart," Power and Energy Magazine, IEEE, vol. 6 (2), pp. 38-45, 2008. [8] Varaiya, P. P., et al., "Smart operation of smart grid: Risklimiting dispatch," Proceedings of the IEEE, vol. 99 (1), pp. 40-57, 2011. [9] Fadaeenejad, M., et al., "The present and future of smart power grid in developing countries," Renewable and Sustainable Energy Reviews, vol. 29, pp. 828-834, 2014. [10] Papavasiliou, A. and S. S. Oren, "Large-Scale Integration of Deferrable Demand and Renewable Energy Sources," IEEE Transactions on Power Systems, vol. 29 (1), 2014. [11] Li, L., "The Effect Research of Smart Grid to Residential Users Intelligent Response Behavior," Advanced Materials Research, vol. 860, pp. 2452-2455, 2014. [12] De Castro, L. and J. Dutra, "Paying for the smart grid," Energy Economics, vol. 40, pp. S74-S84, 2013. [13] Vojdani, A., "Smart integration," Power and Energy Magazine, IEEE, vol. 6 (6), pp. 71-79, 2008. [14] Vallee, F., et al., "System reliability assessment method for wind power integration," IEEE Transactions on Power Systems, vol. 23 (3), pp. 1288-1297, 2008. [15] De Jong, S. and J. Wouters, "Making the Transition: EU-China Cooperation on Renewable Energy and Carbon Capture and Storage," Leuven Centre for Global Governance Studies Working Paper, (66), 2011. [16] Clastres, C., "Smart grids: Another step towards competition, energy security and climate change objectives," Energy Policy, vol. 39 (9), pp. 5399-5408, 2011. [17] Gaviano, A., et al., "Challenges and Integration of PV and Wind Energy Facilities from a Smart Grid Point of View," Energy Procedia, vol. 25, pp. 118-125, 2012. [18] Kohsri, S. and B. Plangklang, "Energy Management and Control System for Smart Renewable Energy Remote Power Generation," Energy Procedia, vol. 9, pp. 198-206, 2011. [19] Ayompe, L., et al., "Validated real-time energy models for small-scale grid-connected PV-systems," Energy, vol. 35 (10), pp. 4086-4091, 2010. [20] Shahraeini, M., et al., "Comparison between communication infrastructures of centralized and decentralized wide area

[23] Bose, A., "Smart transmission grid applications and their supporting infrastructure," IEEE Transactions on Smart Grid, vol. 1 (1), pp. 11-19, 2010. [24] Crow, M. L., et al., "Intelligent Energy Management of the FREEDM System," Power and Energy Society General Meeting, IEEE, 2010, pp. 1-4. [25] Farhangi, H., "The path of the smart grid," Power and Energy Magazine, IEEE, vol. 8 (1), pp. 18-28, 2010. [26] Asgeirsson, H., "Distribution automation—The foundation for the smart grid at DTE energy," Power and Energy Society General Meeting, IEEE, 2010, pp. 1-3. [27] Yan, Y., et al., "An efficient security protocol for advanced metering infrastructure in smart grid," Network, IEEE, vol. 27 (4), 2013. [28] Mak, S. T., "Knowledge based architecture serving as a rigid framework for smart grid applications," Innovative Smart Grid Technologies (ISGT), 2010, pp. 1-8. [29] Lee, P. and L. Lai, "A practical approach of smart metering integration in micro-grid," Power and Energy Society General Meeting, 2010 IEEE, 2010, pp. 1-5. [30] Thornley, V., et al., "User perception of demand side management," CIRED Seminar 2008: Smart Grids for Distribution, pp. 1-4, 2008. [31] Atwa, Y., et al., "Optimal renewable resources mix for distribution system energy loss minimization," Power Systems, IEEE Transactions on, vol. 25 (1), pp. 360-370, 2010. [32] Ochoa, L. F. and G. P. Harrison, "Minimizing energy losses: Optimal accommodation and smart operation of renewable distributed generation," Power Systems, IEEE Transactions on, vol. 26 (1), pp. 198-205, 2011. [33] Aquino-Lugo, A. A. and T. Overbye, "Agent technologies for control applications in the power grid," System Sciences (HICSS), 2010 43rd Hawaii International Conference on, 2010, pp. 1-10. [34] Chao, H.-p., "Price-responsive demand management for a smart grid world," The Electricity Journal, vol. 23 (1), pp. 720, 2010. [35] Cecati, C., et al., "Smart Grids Operation with Distributed Generation and Demand Side Management," Modeling and Control of Sustainable Power Systems, ed: Springer, 2012, pp. 27-46. [36] Caron, S. and G. Kesidis, "Incentive-based energy consumption scheduling algorithms for the smart grid," Smart Grid Communications (SmartGridComm), 2010 First IEEE International Conference on, 2010, pp. 391-396. [37] Bakker, V., et al., "Demand side load management using a three step optimization methodology," IEEE International Conference on Smart Grid Communications (SmartGridComm), 2010, pp. 431-436. [38] Earle, R., et al., "Measuring the capacity impacts of demand response," The Electricity Journal, vol. 22 (6), pp. 47-58, 2009. [39] Kishore, S. and L. V. Snyder, "Control mechanisms for residential electricity demand in smartgrids," IEEE International Conference on Smart Grid Communications (SmartGridComm), 2010, pp. 443-448. [40] Mohsenian-Rad, A.-H. and A. Leon-Garcia, "Optimal residential load control with price prediction in real-time electricity pricing environments," IEEE Transactions on Smart Grid, vol. 1 (2), pp. 120-133, 2010. [41] Ibars, C., et al., "Distributed demand management in smart grid with a congestion game," IEEE International Conference on

Smart Grid Communications (SmartGridComm), 2010, pp. [60] Newsham, G. R. and B. G. Bowker, "The effect of utility time495-500. varying pricing and load control strategies on residential [42] Ghosh, S., et al., "Incentive design for lowest cost aggregate summer peak electricity use: a review," Energy Policy, vol. 38 energy demand reduction," Smart Grid Communications (7), pp. 3289-3296, 2010. (SmartGridComm), 2010 First IEEE International Conference [61] Faruqui, A. and S. George, "Quantifying customer response to on, 2010, pp. 519-524. dynamic pricing," The Electricity Journal, vol. 18 (4), pp. 53[43] Mohsenian-Rad, A.-H., et al., "Autonomous demand-side 63, 2005. management based on game-theoretic energy consumption [62] Cui, T., et al., "Concurrent optimization of consumer's scheduling for the future smart grid," Smart Grid, IEEE electrical energy bill and producer's power generation cost Transactions on, vol. 1 (3), pp. 320-331, 2010. under a dynamic pricing model," Innovative Smart Grid Technologies (ISGT), 2012 IEEE PES, 2012, pp. 1-6. [44] Faria, P., et al., "Demand response management in power [63] Clement-Nyns, K., et al., "The impact of charging plug-in systems using a particle swarm optimization approach," hybrid electric vehicles on a residential distribution grid," Intelligent Systems, IEEE vol. 28 (4), pp. 43 - 51 2011. Power Systems, IEEE Transactions on, vol. 25 (1), pp. 371[45] Conejo, A. J., et al., "Real-time demand response model," 380, 2010. Smart Grid, IEEE Transactions on, vol. 1 (3), pp. 236-242, [64] Liu, X., "Economic load dispatch constrained by wind power 2010. availability: A wait-and-see approach," Smart Grid, IEEE [46] Samadi, P., et al., "Optimal real-time pricing algorithm based Transactions on, vol. 1 (3), pp. 347-355, 2010. on utility maximization for smart grid," Smart Grid [65] Ibars, C., et al., "Distributed demand management in smart grid Communications (SmartGridComm), 2010 First IEEE with a congestion game," Smart Grid Communications International Conference on, 2010, pp. 415-420. (SmartGridComm), 2010 First IEEE International Conference [47] Hatami, S. and M. Pedram, "Minimizing the electricity bill of on, 2010, pp. 495-500. cooperative users under a quasi-dynamic pricing model," [66] Fang, X., et al., "Online strategizing distributed renewable Smart Grid Communications (SmartGridComm), 2010 First energy resource access in islanded microgrids," Global IEEE International Conference on, 2010, pp. 421-426. Telecommunications Conference (GLOBECOM 2011), 2011 [48] Guan, X., et al., "Energy-efficient buildings facilitated by IEEE, 2011, pp. 1-6. microgrid," Smart Grid, IEEE Transactions on, vol. 1 (3), pp. 243-252, 2010. [49] Adika, C. O. and L. Wang, "Smart charging and appliance scheduling approaches to demand side management," International Journal of Electrical Power & Energy Systems, vol. 57, pp. 232-240, 2014. [50] Koutsopoulos, I. and L. Tassiulas, "Challenges in demand load control for the smart grid," Network, IEEE, vol. 25 (5), pp. 1621, 2011. [51] Roozbehani, M., et al., "Dynamic pricing and stabilization of supply and demand in modern electric power grids," Smart Grid Communications (SmartGridComm), 2010 First IEEE International Conference on, 2010, pp. 543-548. [52] Joe-Wong, C., et al., "Optimized day-ahead pricing for smart grids with device-specific scheduling flexibility," Selected Areas in Communications, IEEE Journal on, vol. 30 (6), pp. 1075-1085, 2012. [53] Bakker, V., et al., "Demand side load management using a three step optimization methodology," Smart Grid Communications (SmartGridComm), 2010 First IEEE International Conference on, 2010, pp. 431-436. [54] Saber, A. Y. and G. K. Venayagamoorthy, "Plug-in vehicles and renewable energy sources for cost and emission reductions," Industrial Electronics, IEEE Transactions on, vol. 58 (4), pp. 1229-1238, 2011. [55] Liu, X. and W. Xu, "Minimum emission dispatch constrained by stochastic wind power availability and cost," Power Systems, IEEE Transactions on, vol. 25 (3), pp. 1705-1713, 2010. [56] Kallitsis, M. G., et al., "A framework for optimizing measurement-based power distribution under communication network constraints," Smart Grid Communications (SmartGridComm), 2010 First IEEE International Conference on, 2010, pp. 185-190. [57] Sortomme, E., et al., "Coordinated charging of plug-in hybrid electric vehicles to minimize distribution system losses," Smart Grid, IEEE Transactions on, vol. 2 (1), pp. 198-205, 2011. [58] Anderson, R. N., et al., "Adaptive stochastic control for the smart grid," Proceedings of the IEEE, vol. 99 (6), pp. 10981115, 2011. [59] Han, S., et al., "Development of an optimal vehicle-to-grid aggregator for frequency regulation," Smart Grid, IEEE Transactions on, vol. 1 (1), pp. 65-72, 2010.

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