Method for improving the performance of the energy management in a nearly zero energy building

A method comprising:

• managing optimal energy of a nearly zero energy building (nZEB);
• basing the managing on a genetic algorithm technique;
• providing an optimal balance between objectives of energy saving, comfort of building residents;
• using generated electric energy by wind turbines and photovoltaics;
• storing and using energy stored in a battery storage system (BSS), and further comprising:
• the following steps that are referred to a next time period of 24-hours ahead, dividing the next time period into N discrete time slot intervals:
• a) providing opportunity for residents' selecting acceptable starting and ending time considering residents' selection of acceptable starting and ending time-slots for programmable electrical appliances (PAs) (denoted by aPAi and bPAi, respectively for each of appliance), estimating energy consumption vectors for the PAs, for N time-slots, (denoted by EPA), where the PAs are electric loads each with a planned operating time (of an electric cooker, a washing machine, a dishwasher, an iron, a vacuum cleaner),
• b) providing temperature selection opportunity for residents' selecting of minimum and maximum indoor considering the residents' selection of acceptable minimum and maximum indoor temperature of the nZEB (denoted by Tin min and Tinmax, respectively), estimating energy consumption vectors of the controllable appliances (CAs), for N time-slots, (denoted by ECA), where the CAs are electric loads having operation is regulated by temperature, (heat pumps, and air-conditioners),
• c) estimating for N time-slots energy consumption vectors of the uncontrollable appliances (UAs) (denoted by EuA), by utilizing data obtained from energy meters, where UAs are electric loads that cannot be programmed and are switched on/off either manually by the residents (personal computers, TV, security lighting) or automatically (lights regulated by a movement control system) and appliances that residents do not have any control, (refrigerators, water coolers),
• d) estimating for N time-slots, energy production vectors of the wind turbines and the photovoltaics (denoted by EWT and EPV, respectively), considering meteorological data obtained by a weather forecast utility, and estimating total energy (ERES) generated by the renewable energy sources, the wind turbine and the, photovoltaics determined by ERES=EWT+EPV,
• e) estimating for N time-slots, energy vectors of the BSS for charging and discharging modes (denoted by E′BSSch and E′BSSdis, respectively),
• f) estimating for N time-slots, electric energy price vectors of buying and selling (denoted by EEPbuy and EEPsell, respectively), from estimated buying and selling prices, respectively, obtained from an electric energy utility,
• g) determining values of the acceptable starting and ending time-slots of each i appliance (aPAi and bPAi , respectively), and the acceptable minimum and maximum indoor 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 EEPbuy 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 optimal values, for the N time-slots ahead, of:
• controlling vectors of the time-slots of each PA, the reference indoor temperature, and charging or discharging operating modes of the BSS, and
• providing optimal value of a parameter denoted by x, that can take values 0 and 1,
• assuring that energy generated by RES renewable energy sources and stored in the BSS is equal TO or higher than an amount of energy which has been provided to the appliances by the BSS (x is equal to 1 when the BSS is active and 0 when the BSS is inactive),
• h) using optimal values, that are calculated in step g), of the:
• controlling vectors of the starting time-slots of each PA, 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 is the optimal energy management method, while,
• using a control variable x as input variable at an optimization procedure of a next time sampling, that is realized in step g.