A MILP Approach to Accommodate More Building Integrated Photovoltaic System in Distribution

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Haiming WangSupervisors: Dr Ke Meng

Professor Joe Dong

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Outlines

• Introduction• System Modeling• MILP Formulation• Case Studies• Conclusion

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Introduction

The feasibility of customers’ participation has beencontinuously inspired by new technologies and regulatorysupport. Buildings will be active participants in the electricitymarket in this regard.

Buildings bring about both challenges and opportunities. Electrical energy cannot be easily stored in large quantities

and existing energy storage technologies are relativelyexpensive

Thermal buffering capability of buildings opens anopportunity for buildings to participate in demand response,and they are very pervasive and with minimum cost.

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IntroductionObjectiveThis study proposes an implementation of a smart buildingenergy management system (SBEMS) through a MILPoptimization to benefit residential customers. The developedmodel is to minimize the overall energy expenditure of aresidential building as a whole under dynamic pricing policy. Thefocus is to obtain a strategy to accommodate more BIPV and tominimize the energy cost a residential building spends throughmanaging all the devices’ operational statuses, and as such tomaximize the power utilization efficiency of the proposednetwork while satisfying the various devices’ constraints.

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System Modeling-Thermal Appliances

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Modelling of Thermal Appliances:

Aggregation of Thermal AppliancesMonte Carlo simulation is employed to aggregate thermostatically controlled

loads considering their versatility in sizes of thermal appliances, thermalproperties and occupants’ comfort levels, which employs repeated randomsampling to generate various test scenarios within the uncertainty ranges ofall variables. The generated scenarios imitate realistic combinations of thestochastic variables; hence, the aggregated thermal behaviors of dispersedTCLs could be formulated.

System Modeling-BIPV System

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To maximize the utilization of solar power, we can assume that a buildingcould be equipped with both facade and roofing systems. And it is assumedthat the day ahead forecasting information of local solar radiation isaccessible through local meteorological departments, and therefore the solarPV generation could be predicted with a relatively high degree of accuracygiven the installed tilt angle of BIPV, solar insulation and its efficiency. Forsimplicity, the study assumes that the power generation from the solar PVsystem is known in advance, and solar power is flowing either to the systemor to the grid at any time.

System Modeling-BESS

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Due to the intermittent DERs within a building, aBESS with a reasonable capacity has to serve asan energy reservoir. It is noted that the powerflow between the BESS and the rest of thesystem is only one direction, i.e., power fromthe BESS can either flow to the grid or the houseat a time.

System Modeling-Interaction with Grid

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The house can be solely powered by theelectricity grid when necessary.The direction of power flow between the gridand house can be only one direction on anyoccasion.

Formulation of MILP Model-Planning Horizon

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• The study adopts a similar approach to handlethe time subdivisions that is admitted byother literature so as to provide a round-the-clock planning. The planning horizon is dividedinto N adjacent time steps with identicalduration.

Formulation of MILP Model-Objective Function

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• The objective function is formulated tominimize expectation of energy cost for thehousehold:

• （10）

Formulation of MILP Model-Thermal System Constraints

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(11)

(12)(13)

(14)

(15)

(16)(17)

Formulation of MILP Model-Other Constraints

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• Solar PV Constraints

（18）

• BESS Constraints（19）

（20）（21）（22）

• System Balance Constraint（23）

Case Studies

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Fig. 1 Power flow from BESS and SOC of BESS

Case Studies

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Fig. 2 Power supplied to system from grid

Case Studies

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Fig. 3 Operational status of Air-Conditioners

Case Studies

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Fig. 4 Indoor temperature of representative flat for ATLs

Case Studies

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Fig. 5 Wall temperatures of representative flat for ATLs

Conclusions

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• This paper developed a intuitive MILP optimization forefficient building energy management through a coordinationof BIPV, controllable thermal loads and BESS.

• The proposed algorithm can effectively minimize energyexpenditure based on information of day ahead pricing,weather condition and customer preferences provided bybidirectional communication of smart metering.

• A case study is conducted to identify the effectiveness of theproposed system. The most obvious finding to emerge fromthe study is that the proposed algorithm can helpaccommodate more BIPV in distribution networks.

References

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Thank you！Any questions?

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