Efficient Energy Consumption Scheduling: Towards Effective Load Leveling

Abstract

Different agents in the smart grid infrastructure (e.g., households, buildings, communities) consume energy with their own appliances, which may have adjustable usage schedules over a day, a month, a season or even a year. One of the major objectives of the smart grid is to flatten the demand load of numerous agents (viz. consumers), such that the peak load can be avoided and power supply can feed the demand load at anytime on the grid. To this end, we propose two Energy Consumption Scheduling (ECS) problems for the appliances held by different agents at the demand side to effectively facilitate load leveling. Specifically, we mathematically model the ECS problems as Mixed-Integer Programming (MIP) problems using the data collected from different agents (e.g., their appliances' energy consumption in every time slot and the total number of required in-use time slots, specific preferences of the in-use time slots for their appliances). Furthermore, we propose a novel algorithm to efficiently and effectively solve the ECS problems with large-scale inputs (which are NP-hard). The experimental results demonstrate that our approach is significantly more efficient than standard benchmarks, such as CPLEX, while guaranteeing near-optimal outputs.

Privacy Preserving and Collusion Resistant Energy Sharing

“…microgrids) on the power grid. For instance, global and local load balancing can be manipulated and further optimized via the cooperation among microgrids (e.g., scheduling [14,15]). We intend to propose a privacy preserving cooperative model for them to jointly improving the global and local performance of the power generation, supply, storage and consumption.…”

Securely Solving the Distributed Graph Coloring Problem

“…We plan to explore this problem and develop solutions for it. Secondly, while graph coloring has many applications, there are several other combinatorial optimization problems, such as mixed integer programming [44], scheduling [28,26], knapsack and vehicle routing [36] and even some data mining problems [30,32,29,20,31] which can be naturally distributed. We plan to explore how our privacy preserving tabu search can be applied in these domains.…”

Comparison of Advanced Charge Strategies for Modular Cascaded Battery Chargers

“…For a fair comparison of each control strategy, the application and used capacity of the battery bank needs to be the same mean-wise, as well as the dispersion of each battery in the battery bank. The application chosen is Energy time shift [43] for a PV system, the same charge released is deployed for the system, and the same mean DoD m is adopted for both of the strategies. The PV power plant considered in this study has the following characteristics: The sizing of the battery bank was performed, as detailed in [44].…”