Recently, there has been a tremendous growth in the number of Battery Energy Systems (BESs) connected to the grid. These assets can alleviate some operational issues such as demand surges and occasional power fluctuations associated with the Renewable Energy Sources (RESs) connected to the grid. Nonetheless, both overcharging and frequent usage severely affect their health status and shorten their life expectancy. In this paper, an Energy Management System (EMS) framework with linearised algorithm and in-depth analysis on BES life extension is presented, which optimises the techno-economic aspects of an Active Distribution Network (ADN) connected to BESs and RESs. By applying a mathematical linearisation formulation, a Mixed-Integer Linear Programming (MILP) model is proposed for linearising the Optimal Power Flow (OPF) problem. This technique, which has the merit of fair accuracy while having high speed, is used for scheduling BESs to increase their durability as well as to decrease grid costs. To consider the inherent uncertainty associated with demand and RES generation, a two-stage Stochastic Programming (SP) method is implemented in our proposed model. In terms of battery Loss of Health (LoH) assessment, a linearised battery lifetime method is introduced. Ultimately, a modified 33-bus radial distribution test system with the day-ahead Real-Time Pricing (RTP) program was chosen to apply the proposed algorithm and assess its efficiency.

There has been a wide-spreading use of battery-based Energy Storage Systems (ESSs) in power grid recently. Nevertheless, frequent usage, as well as overcharge or overdischarge, can severely damage their health and reduce their life expectancy. This paper proposes an Energy Management (EM) algorithm in an Active Distribution Network (ADN) in the presence of ESSs. The problem is formulated as a Mixed-Integer-Linear-Programming (MILP) model to determine an optimal schedule for ESS units, not only to maximize their lifetime but also to minimize grid expenses. In order to precisely measure the deterioration percentage of battery cells and their cost, a modified, linearised battery lifetime model, based on its operating profile and a cycle counting technique, is introduced. Eventually, a test case based on the IEEE 33-bus distribution test system with Day-Ahead (DA) pricing scheme has been assessed to demonstrate both the validity and effectiveness of the proposed methodology.

With more hybrid heating systems available, there is a need to optimize energy use intelligently from the end-consumer perspective. This paper focuses on a multi-criteria heating system optimization to optimize cost, carbon emission, and comfort level of building occupants. A discrete Multi-Objective Model Predictive Controller (MO-MPC) algorithm is proposed to optimally utilize two heating sources connected to a building, namely district heating (DH) and a building-integrated electrical heat pump (HP). The model is tested on a real-world building case simulated with a gray box building model. The results are compared to a conventional PID controller as well as the MPC scheme, each with a single heating input, and eight different cases are constructed to make this comparison more visible. The results indicate that, using MO-MPC, a cost saving of up to 10% and emission saving of up to 13% can be reached without additional thermal discomfort, while the potential savings on cost and emission with the hybrid system can be up to 25% and 77%, respectively. Further, a sensitivity analysis on price and emission parameters is conducted to investigate the changes in the provided solution.