Scientists in Iran have developed a new framework to optimize the capacity of PV and battery storage in smart houses, using a two -stage stochastic programming model. They considered the uncertainties in the grid, the market price and the PV output, while they also looked at various business cases.
Researchers from the Babol Noshirvani University of Technology have developed a new framework for optimizing the capacity of PV and battery storage in Smart Homes (SHS).
The new technology uses a two-stage stochamatic programming model, which is responsible for uncertainties in the grid, market prize and PV output.
“Despite the progress in Home Energy Management Systems (HEMSS), existing approaches often see over the head of the synergy optimization of capacity planning of distributed Energy Resource (der) and operational planning under Real-World uncertainties,” said the academics. “The optimal PV-Battery-Maatvoeding is strongly influenced by the strategy for planning devices, while most studies treat these decisions as individual elements that require a model that is more integrated. On the other hand, PV generation, electricity prices and grid and grid and raster drop out are often seen.”
The study assumes that SHS uses a smart meter for power trade with the schedule, based on the price for use use (TOU). Decisions are made on the basis of information such as the use of the house device, user preferences and limitations of network operators, which are sent to a Hems. These hems then returns the optimum operating schedule and the series for the PV and storage systems.
Based on this SHS structure, the optimization framework uses consumer information, the information and shiftable and non-moving taxes as input. Those parameters are then entered in a two-stage stochastic programming problem, which determines the PV and storage capacity in the first phase, and the optimum planning of SH-Electric sources and devices in the second phase.
“Uncertainties of PV output and market price and network availability are modeled as a series of scenarios,” the researchers explains. “For this purpose, for each of the aforementioned parameters, 1,000 scenarios are generated and then reduced to five scenarios using the Back Reduction (BR) algorithm. The BR -algorithm is a method that is used to reduce the number of scenarios in a large number of scenar.”
The optimization was carried out using a representative 24-hour example day for a one-year planning horizon. TV, fridge and lighting were considered non-disposable, which means that their operation could not be postponed or planned; While devices such as a washing machine, EV charger and air conditioner were considered shiftable. The initial investment costs of a PV system were considered $ 1,000/kW, while batteries were priced at $ 250/kWh. They had a life of 12 and eight years respectively.
In the simulation, the PV system had a capacity of 0-2.5 kW, with increases of 0.25 kW, while the battery had a range of 0.5 kWh to 5 kWh, with steps of 0.5 kWh. In total, the framework considered six case studies. Case 1 was a reference case of a normal SH without a planning optimization system, PV or storage. Case 2 underwent a planned operation without PV or storage, while case 3 used PV and storage with planning optimization. In case 4, the optimization considered that uncertainty in the PV production. Case 5 added the uncertainty of the market price and case 6 included the uncertainty of the schedule operation.
Because of this analysis, the scientists discovered that case 6 had an optimal PV size of 1 kW and a battery capacity of 0.5 kWh, case 5 had no PV but 5 kWh storage, and both cases 4 and 3 had 1 kW PV and 1 kWh storage. The annual costs were estimated at $ 1,965.25 in case 1, $ 1,572.19 in case 2, $ 1,533.80 in case 3, $ 1,552.20 in case 4, $ 1,510.41 in case 5 and $ 1,468.49 in case 6.
“Under complex uncertainties (PV generation, electricity market price and grid availability), give priority to PV integration (1 kW) for storage expansion (0.5 kWh) reaches superior costs and grid dependence and a better demand profile,” the group concluded. “In addition, the state of cargo (SOC) profiles show that although a larger storage makes the price-controlled arbitration wide range (48.6-100% in case 5) possible, smaller capacities combined with dynamic control (20.4-96.4% in case 6) effectively bringing an unclarable energy sources, grilles and grid corresponds.
The findings of the investigation were presented in “Optimizing the capacity of photovoltaic sources and battery storage in smart houses, taking into account the impact of the planning of devices and the availability of rasters“Published in Energy.
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