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Optimum Sizing of Distributed Generation and Storage Capacity in Smart Households Salman Kahrobaee University of Nebraska-Lincoln, [email protected]

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Optimum Sizing of Distributed Generation and Storage Capacity in Smart Households Salman Kahrobaee University of Nebraska-Lincoln, Skahrobaee@Huskers.Unl.Edu University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln Faculty Publications from the Department of Electrical & Computer Engineering, Department of Electrical and Computer Engineering 2013 Optimum Sizing of Distributed Generation and Storage Capacity in Smart Households Salman Kahrobaee University of Nebraska-Lincoln, [email protected] Sohrab Asgarpoor University of Nebraska-Lincoln, [email protected] Wei Qiao University of Nebraska-Lincoln, [email protected] Follow this and additional works at: http://digitalcommons.unl.edu/electricalengineeringfacpub Part of the Computer Engineering Commons, and the Electrical and Computer Engineering Commons Kahrobaee, Salman; Asgarpoor, Sohrab; and Qiao, Wei, "Optimum Sizing of Distributed Generation and Storage Capacity in Smart Households" (2013). Faculty Publications from the Department of Electrical and Computer Engineering. 280. http://digitalcommons.unl.edu/electricalengineeringfacpub/280 This Article is brought to you for free and open access by the Electrical & Computer Engineering, Department of at DigitalCommons@University of Nebraska - Lincoln. It has been accepted for inclusion in Faculty Publications from the Department of Electrical and Computer Engineering by an authorized administrator of DigitalCommons@University of Nebraska - Lincoln. IEEE TRANSACTIONS ON SMART GRID, VOL. 4, NO. 4, DECEMBER 2013 1791 Optimum Sizing of Distributed Generation and Storage Capacity in Smart Households Salman Kahrobaee, Student Member, IEEE, Sohrab Asgarpoor, Senior Member, IEEE,and Wei Qiao, Senior Member, IEEE Abstract—In the near future, a smart grid will accommodate DI Decision interval in h. customers who are prepared to invest in generation-battery sys- tems and employ energy management systems in order to cut down Time-step of the simulation in h. on their electricity bills. The main objective of this paper is to determine the optimum capacity of a customer’s distributed-gen- Battery capacity in kWh. eration system (such as a wind turbine) and battery within the framework of a smart grid. The proposed approach involves devel- Optimum battery capacity in kWh. oping an electricity management system based on stochastic vari- ables, such as wind speed, electricity rates, and load. Then, a hybrid Generation capacity in kW. stochastic method based on Monte Carlo simulation and particle swarm optimization is proposed to determine the optimum size of Optimum generation capacity in kW. the wind generation-battery system. Several sensitivity analyses demonstrate the proper performance of the proposed method in Levelized generation cost in cents/kWh. different conditions. Levelized cost per unit of battery capacity in Index Terms—Capacity planning, distributed generation, en- cents/kWh. ergy storage, load management, Monte Carlo simulation, particle swarm optimization (PSO), smart homes. Total electricity cost of the residential customer in cents/day. I. NOMENCLATURE Renewable generation in kWh during . A. Acronyms Electricity in kWh bought from the grid by the HEMS Household electricity management system. customer during . SDS Smart distribution system. ElectricityinkWhsoldtothegridbythe MCS Monte Carlo simulation. customer during . PSO Particle swarm optimization. Electricity in kWh charged in the battery during . RTP Real-time pricing. Maximum sellback electricity limit in kWh. Customer electricity demand in kWh during B. Parameters and Variables . Base load in kW. Total number of sequential time steps in Shiftable load in kW. MCS-PSO study. UnscheduledloadinkW. IterativedurationofMCS-PSOstudyinh. EPR Electricity purchase rate in cents/kWh. Number of particles in MCS-PSO study. ESR Electricity selling rate in cents/kWh. Available battery charge in kWh. Rate of battery charge/discharge in kW. Initial battery charge in kWh at the start of iteration. DOD Depth of battery discharge (%). Wind speed in m/s. Cut-in wind speed of a wind turbine in m/s. Manuscript received May 22, 2012; revised October 31, 2012; accepted Au- gust 13, 2013. Date of publication October 11, 2013; date of current version Cut-out wind speed of a wind in m/s. November 25, 2013. The work of W. Qiao was supported in part by the U.S. National Science Foundation under CAREER Award ECCS-0954938. Paper no. Rated wind speed of a wind turbine in m/s. TSG-00298-2012. The authors are with the Department of Electrical Engineering, Uni- Position vector of particles in MCS-PSO versity of Nebraska-Lincoln, Lincoln, NE 68588-0511 USA (e-mail: model. [email protected]; [email protected]; [email protected]). Color versions of one or more of the figures in this paper are available online Velocity vector of particles in MCS-PSO at http://ieeexplore.ieee.org. model. Digital Object Identifier 10.1109/TSG.2013.2278783 1949-3053 © 2013 IEEE 1792 IEEE TRANSACTIONS ON SMART GRID, VOL. 4, NO. 4, DECEMBER 2013 Best position vector of all particles in Incorporation of bidirectional communication and power MCS-PSO model. flow in a smart grid will provide the infrastructure and an opportunity for both the electricity providers and customers to Best position vector of individual particle efficiently use their assets and cut down on their costs through in MCS-PSO model. demand side management [4], real-time pricing [5], power Fitness (objective) function. sell-back opportunities [6], etc. Indeed, electricity customers of the future smart grid may no longer be perceived as passive Inertia weight of PSO algorithm. loads. Installation of distributed energy resource (DER)-based Cognitive parameter of PSO algorithm. generation, storage devices, and smart appliances will enable Social parameter of PSO algorithm. customers to function as integrated entities who help the grid by contributing to peak-load shaving, ancillary services, relia- Cognitive random number between 0 and 1. bility improvement, and investment postponement [7]. These Social random number between 0 and 1. contributions will benefit the customers of a smart grid as well. Residential customers, for example, will be able to minimize Normal distribution. their electricity costs by investing in renewable generation-bat- Mean of the fitted for load. tery systems with appropriate capacities. They will have the Standard deviation of the fitted for load. opportunity to buy and store electricity and sell back extra power to the grid according to the electricity rates and subject Mean of the fitted for electricity rates. to their resource availability [6]. In addition, the ability to Standard deviation of the fitted for control end-use appliances will allow residential customers to electricity rates. manipulate their loads and shift part of their loads to off-peak hours using electricity management systems [8]. Lognormal distribution. The challenge in minimizing the electricity costs of a smart Location parameter of fitted for household is determining the optimum capacities of the renew- electricity rates. able generation-battery system best suited to that customer’s electricity management system. The optimum capacities depend Scale parameter of fitted for electricity on various factors, such as electricity rates, stochastic behavior rates. of renewable resources, load profile, and grid connection poli- Weibull distribution. cies. The problem of determining the optimum capacities for Scale parameter of the fitted for wind different types of renewable generation and storage systems has speed. been studied in the literature [9]–[20]. Some researchers have determined the optimum capacity for stand-alone wind gen- Shape parameter of the fitted for wind eration-battery systems [9], [10]. There are some studies that speed. have been specifically conducted for large-capacity wind tur- bines [11]–[13]. Meanwhile, some research [14]–[18] has been C. Indices conducted to optimize the size of hybrid wind/solar or genera- tion-battery systems which generally can be installed on the de- Index of the particles in MCS-PSO study. mand side of the grid with lower capacities. A number of these Index of the time steps in MCS-PSO study. systems have been designed to primarily operate off grid in re- Index of MCS-PSO iterations. mote areas. For example, Ekren et al. [16] developed a proba- bilistic approach to find the optimum stand-alone solar-wind en- ergy conversion system with battery storage in order to supply a II. INTRODUCTION remote cell phone base station. However, none of those studies has fully incorporated the previously described smart home fea- HE CONCEPT OF the smart grid is generally accepted tures, such as load management and an electricity sell-back op- T to mean the integration of communication, computing, tion, and, therefore, do not provide an optimum capacity so- control, and information technologies to enhance the reliability, lution from the perspective of the customers in the projected flexibility, efficiency, and sustainability of the electricity grid smart grid environment. Schroeder [21] have presented a sto- [1]. Restrictions of energy resources, aging infrastructure, envi- chastic method for investment optimization in a smart distribu- ronmental concerns, and increasing expectations of customers tion system (SDS). However, the study was from the perspective are some of the drivers of the transition toward a smarter elec- of the distribution system operator as opposed to the customer. trical grid [2]. The adventofthesmartgridwillinfluence plan- Likewise, in [22] the required generation capacity has been cal- ning, operation, and maintenance of the power system,
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