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Energy management in the residential sector contributes to energy system dispatching and security with the optimal use of renewable energy systems (RES) and energy storage systems (ESSs) and by utilizing the main grid based on its state. This work focuses on optimal energy flow, ESS parameters, and energy consumption scheduling based on demand response (DR) programs. The primary goals of the work consist of minimizing electricity costs while simultaneously extending the lifetime of ESSs in conjunction with extracting maximum benefits throughout their operational lifespan and reducing CO2 emissions. Effective ESS and photovoltaic (PV) energy usage prices are modeled and an efficient energy flow management algorithm is presented, which considers the life cycle of the ESSs including batteries, electrical vehicles (EVs) and the efficient use of the PV system while reducing the cost of energy consumption. In addition, an optimization technique is employed to obtain the optimal ESS parameters including the size and depth of discharge (DOD), considering the installation cost, levelized cost of storage (LCOS), winter and summer conditions, energy consumption profile, and energy prices. Finally, an optimization technique is applied to obtain the optimal energy consumption scheduling. The proposed system provides all of the possibilities of exchanging energy between EV, battery, PV system, grid, and home. The optimization problem is solved using the particle swarm optimization algorithm (PSO) in MATLAB with an interval time of one minute. The results show the effectiveness of the proposed system, presenting an actual cost reduction of 28.9% and 17.7% in summer and winter, respectively, compared to a base scenario. Similarly, the energy losses were reduced by 26.7% in winter and 22.3% in summer, and the EV battery lifetime was extended from 9.2 to 19.1 years in the winter scenario and from 10.4 to 17.7 years in the summer scenario. The integrated system provided a financial contribution during the operational lifetime of EUR 11,600 and 7900 in winter and summer scenarios, respectively. The CO2 was reduced by 59.7% and 46.2% in summer and winter scenarios, respectively. [ABSTRACT FROM AUTHOR]

Energy management in the residential sector contributes to energy system dispatching and security with the optimal use of renewable energy systems (RES) and energy storage systems (ESSs) and by utilizing the main grid based on its state. This work focuses on optimal energy flow, ESS parameters, and energy consumption scheduling based on demand response (DR) programs. The primary goals of the work consist of minimizing electricity costs while simultaneously extending the lifetime of ESSs in conjunction with extracting maximum benefits throughout their operational lifespan and reducing CO2 emissions. Effective ESS and photovoltaic (PV) energy usage prices are modeled and an efficient energy flow management algorithm is presented, which considers the life cycle of the ESSs including batteries, electrical vehicles (EVs) and the efficient use of the PV system while reducing the cost of energy consumption. In addition, an optimization technique is employed to obtain the optimal ESS parameters including the size and depth of discharge (DOD), considering the installation cost, levelized cost of storage (LCOS), winter and summer conditions, energy consumption profile, and energy prices. Finally, an optimization technique is applied to obtain the optimal energy consumption scheduling. The proposed system provides all of the possibilities of exchanging energy between EV, battery, PV system, grid, and home. The optimization problem is solved using the particle swarm optimization algorithm (PSO) in MATLAB with an interval time of one minute. The results show the effectiveness of the proposed system, presenting an actual cost reduction of 28.9% and 17.7% in summer and winter, respectively, compared to a base scenario. Similarly, the energy losses were reduced by 26.7% in winter and 22.3% in summer, and the EV battery lifetime was extended from 9.2 to 19.1 years in the winter scenario and from 10.4 to 17.7 years in the summer scenario. The integrated system provided a financial contribution during the operational lifetime of EUR 11,600 and 7900 in winter and summer scenarios, respectively. The CO2 was reduced by 59.7% and 46.2% in summer and winter scenarios, respectively.

Recently, the number of Battery Energy Systems (BESs) connected to the grid has grown significantly. 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 a 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 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 and decrease grid costs. To consider the inherent uncertainty associated with demand and RES generation, a two-stage Stochastic Programming (SP) method is implemented in the 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 a day-ahead Real-Time Pricing (RTP) program was chosen to apply the proposed algorithm and assess its efficiency.

Battery service time is a critical constraint on the availability and functionality of mobile devices. Equipping larger batteries may mitigate such deficiency yet it raises the challenges on thermal limit and physical size. Changing the battery chemistry is another solution, which however usually benefits the energy efficiency of only part of the applications, depending on their software behaviors. To address the challenges of battery energy efficiency and heat dissipation in limited physical space, we propose CAPMAN, a management framework that jointly optimizes the cooling and active power management in a smartphone, a typical mobile device, equipped with a hybrid battery pack. We establish the framework with three components. First, we abstract the correlation among the batteries, devices and software into a finite state machine model, whose state transitions can be triggered by actions like system calls and user activities. Second, we propose a battery scheduling algorithm that determines the more suitable battery for cooling/active power use, with respect to the dynamic software behaviors and their impact on the hardware states, based on a Markov decision process (MDP). Third, we design a facility for joint cooling and active power management by coordinating TECs and batteries. With the three major designs, CAPMAN realizes software defined management that schedules heterogeneous batteries and TEC cooling in a timely manner. In addition, CAPMAN provides an online algorithm with a proved O(1.05)-competitiveness performance. With a pair of big.LITTLE batteries, we prototype CAPMAN on multiple popular smartphones and a PYNQ development board. The evaluation with real-world workloads shows that compared to the current mainstream, CAPMAN can achieve 114 percent longer battery service time under skewed loads; compared to the state-of-the-practice baselines, CAPMAN shows 55 percent performance gain and 53 percent less energy use on average. Those results approve that big.LITTLE batteries with sophisticated software defined management is an effective way to prolong the battery service times on mobile devices. [ABSTRACT FROM AUTHOR]

Mobile charging is proposed as a brand-new charging solution in response to the relatively slow construction of charging facilities. Operating a mobile charging service system involves scheduling mobile charging vehicles (MCVs) and batteries owned by the mobile charging service operator (MCSO), which is important to improve its economic efficiency and has a nonnegligible impact on the power system. In this paper, a bilevel optimization framework for logistics and electricity is developed for MCSOs to achieve joint optimization of planning and operation of the mobile charging service system. For transportation logistics, the upper level plans the size of the MCV fleet and routes MCVs in the dynamic traffic network, which can accommodate dynamic changes in the traffic network and make a reasonable route arrangement. For battery energy management, the lower level plans the battery number and optimizes battery charging and discharging at energy service stations, which can track the electric quantity and the state of each battery during the whole process and accurately describe the matching relationship between batteries in a battery swapping scenario. The upper and lower levels are coupled through the battery swapping behavior between MCVs and energy service stations. Through the iteration and adjustment of the two levels, the results optimize the MCSO's total net profit as much as possible and provide assistance to the power system using service capacity margins. Numerical experiments of a certain scale are used to verify the validity of the proposed framework. • A bilevel optimization framework combines vehicle routing with battery scheduling. • A mobile charging vehicle routing model considers the dynamic traffic network. • An optimization model for battery scheduling tracks each battery's behavior. • Comparison of the proposed framework and benchmarking method using a case study. [ABSTRACT FROM AUTHOR]