Method for Improving the Performance of the Energy Management in a Nearly Zero Energy Building

A method for the optimal energy management of a nearly zero energy building (nZEB) that is based on the genetic algorithm technique and it can provide an optimal balance between the objectives of energy saving, comfort of the building residents and maximum exploitation of the generated electric energy by the wind turbines and photovoltaics through the proper utilization of a battery storage system (BSS), comprising the following steps that are referred to a time period of 24-hours ahead which is divided into N discrete time intervals:

• a) considering the residents' selection of the acceptable starting and ending time-slots of the programmable appliances (PAs) (denoted by aPAi and bPAi, respectively for each i appliance), the energy consumption vectors for the PAs are estimated, for N time-slots, (denoted by EPA), where PAs are electric loads that their operating time can be planned (such as, the electric cooker, the washing machine, dishwasher, ironing, vacuum cleaner, etc.),
• b) considering the residents' selection of the acceptable minimum and maximum indoor temperature of the nZEB (denoted by Tinmin and Tinmax, respectively), the energy consumption vectors of the controllable appliances (CAs) are estimated, for N time-slots, (denoted by ECA), where CAs are electric loads that their operation is regulated by the temperature (such as, the heat pump, the air-conditioner, etc.),
• c) for N time-slots, the energy consumption vectors of the uncontrollable appliances (UAs) is reported (denoted by EUA), by utilizing data obtained from energy meters, where UAs are electric loads that cannot be programmed and they are switched on/off either manually by the residents (such as, the personal computers, TV, security lighting, etc.) or automatically (such as, the lights regulated by a movement control system) and also, they are appliances that the residents do not have any control action (such as, the refrigerator, water cooler, etc.),
• d) for N time-slots, the energy production vectors of the wind turbines and the photovoltaics (denoted by EWT and EPV, respectively) are estimated considering meteorological data obtained by a weather forecast utility, and thus, the estimated total energy generated by the renewable energy sources (wind turbine and photovoltaics) is determined by ERES=EWT+EPV,
• e) for N time-slots, the energy vectors of the BSS for charging and discharging modes (denoted by E′BSSch and E′BSSdis, respectively) are estimated,
• f) for N time-slots, the electric energy price vectors of buying and selling is constructed (denoted by EEPbuy and EEPsell, respectively), by the estimated buying and selling prices, respectively, obtained by an electric energy utility,
• g) the values of the acceptable starting and ending time-slots of each i appliance (aPAi and bPAi, respectively), and the acceptable minimum and maximum inside temperature (Tinmin and Tinmax, respectively) provided by the residents in the aforementioned steps a) and b), and the energy vectors EWT, EPV, EPA, ECA, EUA, EBSSch, EBSSdis, and EEP which are calculated in the aforementioned steps b) to f), are imported to a cost function and then, by its minimization through the genetic algorithm technique, are provided the optimal values, for the N time-slots ahead, of the:
• control vectors of the starting time-slots of each PA, the reference indoor temperature, and the charging or discharging operating modes of the BSS, and
• optimal value of a parameter denoted by x, that can take values 0 and 1, and it assures that the energy generated by the RES and stored in the BSS is equal or higher the amount of energy which has been provided to the appliances by the BSS (specifically, x is equal to 1 when the BSS is active and 0 when the BSS is inactive),
• h) the optimal values, that are calculated in step g), of the:
• control vectors of the starting time-slots of each PA, the reference indoor temperatures for the N time-slots and the charging or discharging operating modes of the BSS for the N time-slots are provided as input signals to a system that realizes the optimal energy management method, while,
• the control variable x is used as input variable at the optimization procedure of the next time sampling, that is realized in step g).