Linfeng Zhang's and Xingguo Xiong's poster on multi-objective optimization and life-cycle-cost analysis of a smart homes

Xiong, Xingguo

Zhang, Linfeng

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1. IntroductionMulti-objective optimization and life-cycle-costanalysis of a smart homeLinfeng Zhang, Xingguo XiongDepartment of Electrical EngineeringUniversity of Bridgeport, Bridgeport, CT 06604In order to improve the power systemperformance and protect the environment, newtechnology advances in the power generation,transmission and distribution, and energystorage. A smart home is a miniature powersystem. It has an automatic control through ahome energy management system (HEMS) onhousehold appliances. With its own generatorsor home backup batteries, the home can beoperated in two modes: standalone or gridconnected. For a grid-connected home, thereare two-way flows of power and information.This information includes the electricity tariff onthe grid and it enables the HEMS to schedulethe running of the household appliances. Thus,the load profile can be shaped to improve thereliability of the power grid, reduce the utilitycost, and maintain the comfort of the residentsReferences1. Alsayegh, O., S., Grid-connected renewableenergy source systems: Challenges andproposed management schemes. EnergyConversion and Management, 2010. 51: p. 1690-1693.2. ModelFigure 4 The profiles of EV battery residualcapacity with different initial residual capacityFig. 4 shows the residual capacity change of anEV battery in the night from 7PM to 7AM.Although the initial residual capacities aredifferent, all of them are eventually above 80kWharound 7AM in the next morning.Figure 5 The power from the grid to the house inthe night, starting from 7PM, with different initialEV battery residual capacitiesFigure 6 The life cycle cost of the power system athomeWith the increasing of the PV size, the LCCincreases at a rate of $200/m2.3. Control of the power flowPerformance evaluationPerformance of the home power system is defined andevaluated with the consideration of utility cost andresident comfort, the comfort is converted touncomfortableness and an overall performance index Qis the weighted total of cost, C, and uncomfortableness,U:(1)Particle swarm optimizationIt is an evolutionary computation techniquedeveloped in 1995. The particle swarm conceptoriginated as a simulation of a simplified social systemand the original intent was to graphically simulate thegraceful but unpredictable choreography of a birdflock. PSO is initialized with a population of randomsolutions called as particles and these particles are keptupdating to get a final best solution.The objective fitness function is based on equation (1) as(2)The objective in the optimization is to minimize equation (2) with the following constraints:o The EV battery should be around fully charged at 7 AM in the next morning.o The SOC of the home backup and the EV batteries should be between 20% and 100%.o There is a power balance among loads, batteries, and grid.o There is a current limit for the batteries as 20A.4. Results and discussionFigure 1 The structure of a smart homeFig.1 shows the model of a smart home, whichincludes a rooftop PV system, fixed and flexibleloads, a backup battery, and an EV. This homeis connected to the grid with a predictable day-ahead dynamic price.Figure 2 Electricity tariffSince the home is connected to the grid, thereis a two-way information and powertransmission between the home and the grid.The day-ahead prices of electricity are shown inFig. 2: one price is paid by the residents to theutility company and it includes the cost of theelectricity generation, transmission anddistribution. The other one is the credit to theresidents who sell their spare electricity to thegrid5. ConclusionsThe PV size doesn’t affect the comfort but the LCC.The power from the PV system flows to the loadsand the spare goes to the grid for electricity creditsin the day time.8 10 12 14 16 18 20 22 24 26 28 300.000.020.040.060.080.100.120.140.16  Price ($/kWh)Time (hour, starting from 7AM) electricity+T&D electricity cost1 2Q w C w U= +1 2   1 1(p l )n nj G j f j f jj jf w price p w= == + −∑ ∑8 10 12 14 16 18-30-20-10010203040 fixed load flexible load solar energy grid  Power (kW)Time (hour, strating from 7AM)PV size (m2) Cost ($)200 -5.71150 -4.62100 -3.5550 -2.46Figure 3 Day time power flow with the PV array size as 200m2Table 1 The total day-time electricity costFig. 3 shows the maximum PV output power is 20kWand it is much higher than the loads. The spareelectricity flows to the grid for the creditsshown inTable 1.8 10 12 14 16 18 2020406080100 20 kWh 30 kWh 40 kWh 50 kWh 60 kWh  Residual capacity in the EV battery (kWh)Time (hour)8 9 10 11 12 13 14 15 16 17 18 19 20024681012141618  Power (kW)Time (hour) 20 kWh 30 kWh 40 kWh 50 kWh 60 kWh  20 30 40 50 6050k60k70k80k90k100k110k120k130k140k150k  LCC ($)Initial residual capacity in the EV Battery (kWh) 50m2 100m2 150m2 200m2 0m2

Multi-objective optimization and life-cycle-cost analysis of a smart home

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Multi-objective optimization and life-cycle-cost analysis of a smart home

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