Abstract
Several approaches of energy management systems reduce power consumption of heating demand and electricity storage based on static or dynamic tariffs. However, such methodologies impose uncertainties due to forecasting errors of energy consumption and generation, while evaluating electricity prices. Alternatively, this paper proposes a novel methodology of residential energy management to decrease electricity consumption of space-heating units and grid-connected batteries without incorporating price signals, while maintaining their characteristic operation. The proposed algorithm of energy management develops seasonal calculations of heating load and storage power to achieve energy savings in smart homes based on mixed-integer linear programming, considering photovoltaic electric generation. Power consumption of heating systems is estimated considering heat losses of conduction and ventilation through buildings in addition to other important parameters such as outdoor and indoor temperatures. Charging and discharging patterns of grid-connected batteries are modelled consistent with residential loads. Simulation results show that the proposed algorithm of energy management is able to reduce energy consumption of space-heating loads by 15%, mitigating their environmental impact while keeping their functioning usage. Moreover, the algorithm decreases charging demand of grid-connected batteries by 13%, maintaining their state-of-charge levels between 10 and 90%.
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Abbreviations
- DR:
-
Demand response
- DSM:
-
Demand-side management
- EMS:
-
Energy management systems
- ESS:
-
Energy storage systems
- PV:
-
Photovoltaic
- RES:
-
Renewable energy sources
- TCLs:
-
Thermostatically controlled loads
- TOU:
-
Time of use
- \(\alpha_{a}\) :
-
Air density in kg/m3
- \(\psi_{ag}\) :
-
Air leakage flow in m3/s
- \({\text{P}}_{av}\) :
-
Average power consumption of residential loads in W
- \({\text{P}}_{{{\text{CH}}}}\) :
-
Charging power of the of the grid-integrated battery at time step (\(t\)) in W
- \({\mathbb{W}}\) :
-
Coefficient matrix of a one row of elements
- \({\mathbb{X}}\) :
-
Decision variables in a vector of a single column
- \(\theta_{s}\) :
-
Degree of the temperature in standard testing conditions in °C
- \({\text{P}}_{{{\text{DI}}}}\) :
-
Discharging power of the of the grid-integrated battery in W
- \(\eta_{{{\text{CH}}}}\) :
-
Efficiency of charging controller in the grid-connected battery system
- \(\eta_{{{\text{DI}}}}\) :
-
Efficiency of inverter in the grid-connected battery system
- \(P_{PVs} \left( t \right)\) :
-
Power generation from the photovoltaic module at time step (\(t\)) in W
- \({\mathbb{K}}_{1}\) :
-
Factor evaluated based on the area of the photovoltaic module, while considering efficiencies of charging controller and inverter in standard testing conditions
- \({\mathbb{K}}_{3}\) :
-
Factor evaluated based on the nominal operating cell temperatures of the photovoltaic module
- \({\text{A}}_{b}\) :
-
Total area of the considered building elements in m2
- \(SIR_{G} \left( t \right)\) :
-
Global component of solar radiation at time step (\(t\)) in W/m2
- \({\text{P}}_{{{\text{cond}}}} \left( t \right)\) :
-
Heat loss due conduction of a building element at time step (\(t\)) in W
- \({\text{P}}_{ag} \left( t \right)\) :
-
Heat loss of air leakage at time step (\(t\)) in W
- \(\theta_{{{\text{in}}}}\) :
-
Indoor temperature in °C
- \(soc_{1}\) :
-
Initial amount of the state-of-charge level of the grid-connected battery during charging performance
- \(soc_{2}\) :
-
Initial amount of the state-of-charge level of the grid-connected battery during discharging performance
- \({{\mathbb{L}}{\mathbb{B}}}\) :
-
Lower boundaries that decision variables should not exceed them over optimization processes.
- \({\mathbb{B}}\) :
-
Magnitude of inequality constraints while adjusting decisions variables during the optimization
- \({\mathbb{N}}\) :
-
Number of integers provided in optimal solutions
- \(F_{s - h}\) :
-
Objective function to be minimized considering energy consumption of space-heating systems
- \(F_{{{\text{GCB}}}}\) :
-
Objective function to be minimized considering energy consumption of grid-connected batteries
- \({\mathbb{F}}\) :
-
Objective function to be minimized
- \(\theta_{{{\text{out}}}} \left( t \right)\) :
-
Outdoor temperature in °C
- \({\text{P}}_{h} \left( t \right)\) :
-
Power consumption of space-heating systems at time step (\(t\)) in W
- \({\text{P}}_{{{\text{GCB}}}} \left( t \right)\) :
-
Power of the grid-integrated battery at time step (\(t\)) in W
- \({\text{E}}_{ratedG}\) :
-
Rated energy capacity of the grid-connected battery considered in Wh
- \(\rho_{d}\) :
-
Ratio of regular operation of space-heating systems
- \(\rho_{{{\text{tem}}}}\) :
-
Ratio of temperature during heat recovery processes
- \({\text{P}}_{\text{res}} \left( t \right)\) :
-
Residential loads at time step (\(t\)) in W
- \(\beta_{a}\) :
-
Specific heat capacity of air in Ws/kgk
- \(soc\left( t \right)\) :
-
State-of-charge levels of the grid-connected battery at time step (\(t\)) in \(\%\)
- \(\psi_{a}\) :
-
Supply air flow of space-heating systems in m3/s
- \({\mathbb{K}}_{2}\) :
-
Temperature coefficient calculated for each centigrade of the photovoltaic module
- \(\theta_{r} (t)\) :
-
Temperatures of air recovery in °C
- \(\theta_{p}\) :
-
Temperature of the air propelled by space-heating systems in °C
- \({\Lambda }_{b}\) :
-
Thermal heat loss coefficient of the considered building elements in W/m2k
- \({\text{P}}_{s - h} \left( t \right)\) :
-
Thermostatic power consumption of space-heating systems
- \({\text{P}}_{\text{res}}^{N} \left( t \right)\) :
-
Per-unit power of residential loads normalized based on the maximum household demand
- \(P_{PVs}^{N} \left( t \right)\) :
-
Per-unit power of the residential photovoltaic system normalized based on its maximum amount of generation
- \({{\mathbb{U}}{\mathbb{B}}}\) :
-
Upper boundaries that decision variables should not exceed them over optimization processes
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Al Essa, M.J.M. Energy management of space-heating systems and grid-connected batteries in smart homes. Energ. Ecol. Environ. 7, 1–14 (2022). https://doi.org/10.1007/s40974-021-00219-0
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DOI: https://doi.org/10.1007/s40974-021-00219-0