The cost of electricity for a building can be lowered by applying optimization strategies that provide optimal purchasing schedules for the building operation under a variable electricity price. From the DSO perspective, this introduces benefits to the grid. The optimal schedule better follows the electricity price, which in turn reflects the DSO requirements. However, most optimization tools are intended for minimizing the costs of a single user and do not take into account the grid operation. If large customers in the same area optimize their schedules, unwanted ‘artificial’ peaks may occur on the feeder that the DSO has to deal with. Peaks height can be lowered by applying an upper bound to the purchased electricity in the mathematical formulation of the optimization problem. However, introducing a further constraint reduces the economic benefit for optimizing customers. This study assesses the peak reduction potential of introducing the additional constraint while keeping appealing economic benefits for the customers. DER-CAM, an optimization tool developed at LBNL was used for simulating the operation of three large typical commercial customers over a week. Results prove the peak shaving ability of the proposed model that delivers schedules where the peaks height can be reduced by a given degree. The additional power constraint reduces overload time by 54% while maintaining the electricity bill substantially equal to the optimal unconstrained formulation (1% cost increase). Reducing overload feeder time allows for larger electric load s to be optimized and fosters the diffusion of microgrids and DERs.
Proceedings of Cyseni 2014, 2014, p. 112-119
Smartgrids; Microgrids; Optimization; Energy storage