Optimal scheduling of battery energy storage systems of residential solar PV system for reverse power flow mitigation and peak load shaving | |
| Author | Myo Min Htwe |
| Call Number | AIT Thesis no.ET-19-13 |
| Subject(s) | Energy storage--Technological innovations Electric batteries Solar cells Renewable energy sources |
| Note | A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Energy |
| Publisher | Asian Institute of Technology |
| Abstract | Renewable energy application for the residential power supply has been encouraged and increased, due to the energy depletion and rising environmental pollution. While incorporating the distributed generation into the grid, there still remain the challenges. Proper scheduling strategy of the battery energy storage system (BESS) helps to solve those challenges arising due to the intermittent production nature of the renewable energy. More proficient scheduling of the generating units needs updated methods in power system operation to lessen large instabilities in renewable generation outputs while keeping power system reliability. The battery scheduling model proposed in this study is to tackle those challenges with the main objectives to reduce the negative power flow, and peak loads, and to add the economical savings for the customers. This study proposes an updated optimization-based algorithm for the optimal scheduling of residential battery energy storage with solar PV, in the context of mitigating reverse power flow and peak load. Based on the proposed algorithm, it provides the cost saving estimation of the residential system for the current Australian energy pricing (fed-in-tariff) system. The study took the real historical data from online Australian distribution network provided by Ausgrid, in the New South Wales area. To achieve the three main objectives, 1) reducing the negative power flow, 2) shaving peak load, and 3) the cost saving for the customer, RHC based algorithm was developed with a new factors consideration such as limiting battery charge/discharge capacity, application of custom solver. Collectively, the developed model showed the reduction of 78% negative power flow, and nearly 73.6% shaving in peak load. Consequently, it reduces 72.5% electricity cost for the customer, compared to the base line case which the customer pays higher. In addition, the peak load shaving percentage of this developed algorithm is about 23.6% higher than the lasts previous study conducted on the Australian network with the additional factors’ consideration. The statistical forecasting methodology was used to predict a day-ahead loads and PV generation, and demand profiles. MATLAB was used to do the QP simulation. It used the real historical data and conducted simulation for a whole day with 30 min time interval steps. The battery life extension was also considered and thus applied the 10%-90% SOC capacities. The storage capacity was taken as 10 kWh, based on the actual PV generation capacity and the SOC limits. A new custom solver was coded to avoid the repetitive feed in steps in simulation, thus it improves the simulation process. At each time step of RHC, the forecasted data of residential data are applied to the QP based algorithm to schedule the battery storage to mitigate reverse power flow and peak load. The simulation was done for both base line case (as a comparison), and the case with battery storage. Overall, the results showed that the developed RHC energy-shifting algorithm confirmed the significant reduce in reverse power flow and peak load. The scheduling of battery storage using the proposed RHC based optimization algorithm also increased savings considerably for the residential customer. |
| Year | 2019 |
| Type | Thesis |
| School | School of Environment, Resources, and Development (SERD) |
| Department | Department of Energy and Climate Change (Former title: Department of Energy, Environment, and Climate Change (DEECC)) |
| Academic Program/FoS | Energy and Environment (EE) |
| Chairperson(s) | Weerakorn Ongsakul; |
| Examination Committee(s) | Singh, Jai Govind;Loc, Thai Nguyen; |
| Scholarship Donor(s) | Swan Arr Electronic and Precision Industry, Myanmar ;Asian Institute of Technology Fellowship; |
| Degree | Thesis (M.Eng.) - Asian Institute of Technology, 2019 |