Search

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]

The growing use of intermittent renewable energy sources requires an increased amount of storage capacity to match uncertain generation with uncertain demand. A possible solution is the use of thermal and electrical storages. This paper compares several model formulations: mixed integer linear programs (MILPs), nonlinear programs (NLPs), mixed integer nonlinear programs (MINLPs) for optimizing the operation of a multi-modal home energy system comprising heating and electricity subsystems. The respective optimization problems are then resolved within a model predictive control scheme and the final solutions are compared in terms of runtime and optimality. The results indicate that a thermocline-based thermal storage model leads to the overall lowest costs while not significantly impeding computing times. Additionally, the results show that a continuous heat pump model leads to reduced computing times without affecting the modelling accuracy. [ABSTRACT FROM AUTHOR]

The goal of this paper is to optimally combine day-ahead solar and demand forecasts for the optimal battery schedule of a hybrid solar and battery farm connected to a distribution station. The objective is to achieve the maximum daily peak load reduction and charge battery with maximum solar photovoltaic energy. The innovative part of the paper lies in the treatment for the errors in solar and demand forecasts to then optimize the battery scheduler. To test the effectiveness of the proposed methodology, it was applied in the data science challenge Presumed Open Data 2021. With the historical Numerical Weather Prediction (NWP) data, solar power plant generation and distribution-level demand data provided, the proposed methodology was tested for four different seasons. The evaluation metric used is the peak reduction score (defined in the paper), and our approach has improved this KPI from 82.84 to 89.83. The solution developed achieved a final place of 5th (out of 55 teams) in the challenge.

Highlights • Active users with PV systems and batteries are considered in a LV distribution grid. • A strategy for voltage regulation ancillary service provision is proposed. • The proposed scheduling algorithm exploits the grid nodal sensitivity coefficients. • A way for determining the most convenient tariffs is proposed. • Numerical results are presented and discusses for a LV radial distribution grid. Abstract In recent years, renewable energies and storage systems are bringing a significant contribution to power system. Due to the high penetration of non-predictable power sources, especially in the distribution networks, new challenges related to the integration and management of these new resources also combined with storage systems arose in power system research. Accordingly, the aim of this paper is to propose an innovative scheduling method for voltage regulation in low voltage networks. A simple type of contract for ancillary services that foresees the participation of active users endowed with a storage system coupled with a PV system is considered. The method allows to regulate the voltage in a distribution system by using a combined PV and storage system. The voltage regulation is achieved by setting the value of the inverter reactive power on the basis of sensitivity coefficients of the considered node, taking into account the capability curve constrains, as required by new grid codes. A possible tariff policy is also proposed for the ancillary service and for the operation of the realization of the scheduling algorithm. Simulation results demonstrate the effectiveness of the proposed method for the scheduling of the power system. [ABSTRACT FROM AUTHOR]

This paper presents a reinforcement learning framework for solving battery scheduling problems in order to extend the lifetime of batteries used in electrical vehicles (EVs), cellular phones, and embedded systems. Battery pack lifetime has often been the limiting factor in many of today’s smart systems, from mobile devices and wireless sensor networks to EVs. Smart charge-discharge scheduling of battery packs is essential to obtain super linear gain of overall system lifetime, due to the recovery effect and nonlinearity in the battery characteristics. Additionally, smart scheduling has also been shown to be beneficial for optimizing the system’s thermal profile and minimizing chances of irreversible battery damage. The recent rapidly-growing community and development infrastructure have added deep reinforcement learning (DRL) to the available tools for designing battery management systems. Through leveraging the representation powers of deep neural networks and the flexibility and versatility of reinforcement learning, DRL offers a powerful solution to both roofline analysis and real-world deployment on complicated use cases. This work presents a DRL-based battery scheduling framework to solve battery scheduling problems, with high flexibility to fit various battery models and application scenarios. Through the discussion of this framework, comparisons have also been made between conventional heuristics-based methods and DRL. The experiments demonstrate that DRL-based scheduling framework achieves battery lifetime comparable to the best weighted-k round-robin (kRR) heuristic scheduling algorithm. In the meantime, the framework offers much greater flexibility in accommodating a wide range of battery models and use cases, including thermal control and imbalanced battery.

The smart grid with its two-way communication and bi-directional power layers is a cornerstone in the combat against global warming. It allows for the large-scale adoption of distributed (individually-owned) renewable energy resources such as solar photovoltaic systems. Their intermittency poses a threat to the stability of the grid, which can be addressed by the introduction of energy storage systems. Determining the optimal capacity of a battery has been an active area of research in recent years. In this research, an in-depth analysis of the relation between optimal capacity and demand and generation patterns is performed for households taking part in a community-wide demand-side management scheme. The scheme is based on a non-cooperative dynamic game approach in which participants compete for the lowest electricity bill by scheduling their energy storage systems. The results are evaluated based on self-consumption, the peak-to-average ratio of the aggregated load and potential cost reductions. Furthermore, the difference between individually-owned batteries and a centralised community energy storage system serving the whole community is investigated.

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]

In this paper we propose two optimization-based algorithms for coordinating residential battery storage when solar photovoltaic (PV) generation in excess of load is compensated via net metering. Our objective is to balance increases in daily operational savings that accrue to customers with the management of reverse power flows and/or peak loads approaching a network capacity. To achieve this objective we present a central quadratic program (QP)-based algorithm, where residential customers implement a distributor-specified day-ahead battery schedule. We also present a local QP-based algorithm, where each residential customer implements a day-ahead battery schedule subject to three distributor-specified weights. To complete our assessment of the distributor benefit, both QP-based scheduling algorithms are applied to measured load and generation data from 145 residential customers located in an Australian distribution network. The results of this case study confirm both QP-based scheduling algorithms manage reverse power flow and peak loads within a distribution network. In the context of net metering, all customers exhibit the same operational savings when implementing the central QP-based algorithm, while the local QP-based algorithm disproportionately penalizes some customers. [ABSTRACT FROM AUTHOR]

The rising climatic concerns force the conventional power system to minimise the gas emissions. Microgrid integrating with renewable energy plays a key role in utilising power systems in a more environment friendly way. Compared with single renewable energy system, the microgrid with multi‐energy systems is more attractive in practice. The schedule problem is considered to schedule wind power, combined heating and power generation (CHP), battery, and power grid in order to satisfy the electricity load and heat loads in the microgrid with the minimal cost in real time. Owing to the stochastic nature of wind power generation, it is a challenge to solve the multi‐energy scheduling problem when using the obtained real‐time information. In this study, the joint scheduling of the microgrid with multi‐energy systems is formulated as a rolling horizon Markov decision process (MDP). Moreover, then the rollout algorithm is applied to solve the problem in order to deal with the large state and decision space of MDP, which is caused by the increase of the number of CHPs. The feasible base policy needed in the rollout algorithm is constructed using a greedy algorithm. Numerical results demonstrate that the algorithm can achieve the requirement of real‐time scheduling effectively. [ABSTRACT FROM AUTHOR]

In this paper we propose a linear program (LP)-based algorithm to schedule battery storage co-located with residential solar photovoltaics (PV), when excess generation is compensated via net metering. While the objective of this LP-based approach is to maximize the operational savings that accrue to customers, an undesirable consequence to the utility is reverse power flow during the peak pricing period. We show in this paper that it is possible to balance the objective of the utility in limiting reverse power flow, with the customer objective of increasing operational savings, in the context of net metering. To balance the specified utility and customer objectives we employ a quadratic program (QP)-based algorithm, which explicitly penalizes reverse power flow. To complete our assessment of net metering, both the LP-based and QP-based scheduling algorithms are applied to measured load and generation data from 145 residential customers located in an Australian distribution network. The results of this case study confirm the absence of reverse power flow when all customers employ a QP-based battery schedule, with the majority of customers exhibiting operational savings. [ABSTRACT FROM AUTHOR]

In the distributed optimization of micro-grid, we consider grid connected solar micro-grid system which contains a local consumer, a solar photovoltaic system and a battery. The consumer as an agent continuously interacts with the environment and learns to take optimal actions. Each agent uses a model-free reinforcement learning algorithm, namely Q Learning, to optimize the battery scheduling in dynamic environment of load and available solar power. Multiple agents sense the states of the environment components and make collective decisions about how to respond to randomness in load, intermittent solar power using a Multi-Agent Reinforcement Learning algorithm, called Coordinated Q Learning (CQL). The goals of each agent are to increase the utility of the battery and solar power in order to achieve the long term objective of reducing the power consumption from grid. [ABSTRACT FROM AUTHOR]

Several studies have suggested that battery storage co-located with solar photovoltaics (PV) benefits electricity distributors in maintaining system voltages within acceptable limits. However, without careful coordination, these potential benefits might not be realized. In this paper we propose an optimization-based algorithm for the scheduling of residential battery storage co-located with solar PV, in the context of PV incentives such as feed-in tariffs. Our objective is to maximize the daily operational savings that accrue to customers, while penalizing large voltage swings stemming from reverse power flow and peak load. To achieve this objective we present a quadratic program (QP)-based algorithm. To complete our assessment of the customer benefit, the QP-based scheduling algorithm is applied to measured load and generation data from 145 residential customers located in an Australian distribution network. The results of this case study confirm the QP-based scheduling algorithm significantly penalizes reverse power flow and peak loads corresponding to peak time-of-use billing. In the context of feed-in tariffs, the majority of customers exhibited operational savings when QP energy-shifting. [ABSTRACT FROM AUTHOR]

Distributed, controllable energy storage devices offer several benefits to electric power system operation. Three such benefits include reducing peak load, providing standby power, and enhancing power quality. These benefits, however, are only realized during peak load or during an outage, events that are infrequent. This paper presents a means of realizing additional benefits by taking advantage of the fluctuating costs of energy in competitive energy markets. An algorithm for optimal charge/discharge scheduling of Community Energy Storage (CES) devices as well as an analysis of several of the key drivers of the optimization are discussed. [ABSTRACT FROM AUTHOR]

This paper addresses the scheduling and operation problem of a combined battery and wind generation system (BWGS) in real-time market. The proposed operation scheme consists of two control mechanisms: (i) offline scheduling according to the estimation of the market prices and the stochastic information of wind generation, and (ii) online dispatching based on the updated system state in real time. The analysis in this paper is different from the previous studies by taking into account the expense of battery energy storage under a newly defined term: cost of lifetime losses (CLLs). Thus the optimal scheduling and operation of BWGS not only gives maximum profit to the owner but also avoids the abusive operation of the battery. The case study provides a comparison of the proposed operation scheme with other approaches. It is shown that the proposed scheme considering the CLLs can significantly change the optimal charging/discharging of battery energy storage, and the increase in profit obtained in this case is about 9%. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

Abstract: A battery switch array system is developed for solid-state rechargeable micro-scale lithium batteries, designed and developed by Jet Propulsion Laboratory (JPL), California Institute of Technology, for aerospace applications. The heart of the system is a topology unique in its dynamic reconfiguration ability by providing selective connection and isolation and versatile in its core design by incorporating passive, active or energy storage elements. It allows up to N-batteries to be connected in any desired configuration; providing maximum voltage, maximum capacity and a voltage/capacity ratio. This battery switch array system is part of a power management system that is developed to utilize these features and provide for multiple charging algorithms, real time status information and fault tolerant capabilities. A two microbattery prototype circuit is built using a microprocessor-based controller for verification of the system design and its features. The topology and its features will be explored in this paper to provide methods for modular and scalable energy storage system design. [Copyright &y& Elsevier]

This paper presents a reinforcement learning framework for solving battery scheduling problems in order to extend the lifetime of batteries used in electrical vehicles (EVs), cellular phones, and embedded systems. Battery pack lifetime has often been the limiting factor in many of today's smart systems, from mobile devices and wireless sensor networks to EVs. Smart charge-discharge scheduling of battery packs is essential to obtain super linear gain of overall system lifetime, due to the recovery effect and nonlinearity in the battery characteristics. Additionally, smart scheduling has also been shown to be beneficial for optimizing the system's thermal profile and minimizing chances of irreversible battery damage. The recent rapidly-growing community and development infrastructure have added deep reinforcement learning (DRL) to the available tools for designing battery management systems. Through leveraging the representation powers of deep neural networks and the flexibility and versatility of reinforcement learning, DRL offers a powerful solution to both roofline analysis and real-world deployment on complicated use cases. This work presents a DRL-based battery scheduling framework to solve battery scheduling problems, with high flexibility to fit various battery models and application scenarios. Through the discussion of this framework, comparisons have also been made between conventional heuristics-based methods and DRL. The experiments demonstrate that DRL-based scheduling framework achieves battery lifetime comparable to the best weighted-k round-robin (kRR) heuristic scheduling algorithm. In the meantime, the framework offers much greater flexibility in accommodating a wide range of battery models and use cases, including thermal control and imbalanced battery. [ABSTRACT FROM AUTHOR]

The smart grid with its two-way communication and bi-directional power layers is a cornerstone in the combat against global warming. It allows for the large-scale adoption of distributed (individually-owned) renewable energy resources such as solar photovoltaic systems. Their intermittency poses a threat to the stability of the grid, which can be addressed by the introduction of energy storage systems. Determining the optimal capacity of a battery has been an active area of research in recent years. In this research, an in-depth analysis of the relation between optimal capacity and demand and generation patterns is performed for households taking part in a community-wide demand-side management scheme. The scheme is based on a non-cooperative dynamic game approach in which participants compete for the lowest electricity bill by scheduling their energy storage systems. The results are evaluated based on self-consumption, the peak-to-average ratio of the aggregated load and potential cost reductions. Furthermore, the difference between individually-owned batteries and a centralised community energy storage system serving the whole community is investigated. [ABSTRACT FROM AUTHOR]