Usage Of Energy Storage Systems Research Articles

Performance Assessment User Interface to Enhance the Utilization of Grid-Connected Residential PV Systems

The share of renewable energy resources in modern electrical power networks is increasing in order to meet environmental and technical targets. Consequently, energy researchers and power providers have been focusing on optimizing the integration of renewable energy into existing power grids. One of the most significant growing applications of renewable energy resources is residential photovoltaic (PV) systems; therefore, this paper discusses a new methodology to enhance the utilization of small-scale and medium-scale PV systems. For this purpose, this study proposes a user-friendly interface to help novice users optimally design their own PV projects with the highest possible utilization of the installed panels. Unlike the commercially available design tools, the proposed interface in this paper provides a higher degree-of-freedom computational process, as well as the option of improving the generated power quality, while maintaining the simplicity of the required tools and inputs. The proposed methodology mainly relies on a deep mathematical analysis considering different generation and consumption aspects, such as the load profile, time of usage, ambient temperature, PV system specifications and location. Furthermore, the mechanism of integrating a small portion of Energy Storage Systems (ESSs), to improve the quality of the extracted power, is also discussed in this study. The user interface provides the ability to estimate optimal ESS usage versus the estimated price when energy is urgently required. The case study was conducted in Riyadh, Saudi Arabia, and the results showed an essential improvement in the efficiency, solar fraction and power quality of the studied PV project, which can be extended to other home and distributed generation (DG) scales.

Rolling horizon optimization based real-time energy management of a residential neighborhood considering PV and ESS usage fairness

The incorporation of decentralized energy solutions, including solar photovoltaic (PV) installations and energy storage systems (ESSs), into residential communities has received substantial interest in recent times. This is driven by the objective of enhancing the self-reliance of residential zones while decreasing their reliance on centralized power grids. Effective management of these systems can yield advantages such as load equalization, cost reduction, and diminished emissions. The process of optimizing energy management in residential communities entails addressing multiple factors, including uncertainty, equity, and efficiency.This study introduces an innovative methodology for real-time energy management in a residential community, taking into account both the equitable utilization of solar PV and ESS and the minimization of costs. The suggested technique employs rolling horizon optimization and mixed-integer linear programming (MILP) to tackle uncertainties in PV production and guarantee a fair allocation of shared resources. The outcomes of this novel approach showcase its capability to sustain cost-effectiveness while fostering equitable energy consumption.

Joint Voyage Planning and Onboard Energy Management of Hybrid Propulsion Ships

Maritime transportation decarbonization has become a crucial factor in reducing carbon emissions and mitigating climate change. As an industry that historically relies on fossil fuels, in particular, heavy fuel oil, the reinvention of the maritime transportation system is occurring at an unprecedented speed to integrate renewable and green energy, low-/zero- carbon fuels, and green infrastructure to support emission-free shipping. In this paper, a two-stage joint optimization model is proposed to optimize the voyage planning of a ship among multiple ports as well as its onboard energy management during each section of the voyage. More specifically, in the first stage, the arrival time of ships is optimized according to the mission of the ship and the electricity prices offered at each port. In the second stage, the speed of the ship, the dispatch of the onboard diesel engine, and the usage of energy storage systems (ESSs) are optimized based on emission control areas and maritime meteorological conditions. Simulation results have shown that the proposed approach would help ship operators minimize the operating cost over the whole voyage while significantly contributing to carbon emissions reduction.

Optimization of a Renewable Energy Source-Based Virtual Power Plant for Electrical Energy Management in an Unbalanced Distribution Network

The virtual power plant (VPP) is a developing concept in the modern engineering field. This paper presents a local search optimization (LSO) algorithm-based virtual power plant for energy management in a distribution network. The proposed LSO algorithm is used for the optimal selection and location of the distributed energy resources (DER), the optimal regulation of load, and the optimal usage of energy storage systems in a VPP. DERs are a renewable energy sources (RES) that consist of solar PV and a wind energy source. DERs face the challenge of energy losses, voltage variations, and revenue losses in the utilization network. These problems are solved by the proposed VPP concept by reducing the acquiring of energy from the power sector. An LSO-based virtual power plant is modeled in MATLB PSCAD and verified using the IEEE-9 bus system. The results show that 81% of the purchased energy from the utility grid was reduced by the optimal placement of the DER and 86% of acquired energy from utility grid was reduced by the optimal location of the DER and optimal load control in the VPP.

Optimal Scheduling of Energy Storage System Considering Life-Cycle Degradation Cost Using Reinforcement Learning

Recently, due to the ever-increasing global warming effect, the proportion of renewable energy sources in the electric power industry has increased significantly. With the increase in distributed power sources with adjustable outputs, such as energy storage systems (ESSs), it is necessary to define ESS usage standards for an adaptive power transaction plan. However, the life-cycle cost is generally defined in a quadratic formula without considering various factors. In this study, the life-cycle cost for an ESS is defined in detail based on a life assessment model and used for scheduling. The life-cycle cost is affected by four factors: temperature, average state-of-charge (SOC), depth-of-discharge (DOD), and time. In the case of the DOD stress model, the life-cycle cost is expressed as a function of the cycle depth, whose exact value can be determined based on fatigue analysis techniques such as the Rainflow counting algorithm. The optimal scheduling of the ESS is constructed considering the life-cycle cost using a tool based on reinforcement learning. Since the life assessment cannot apply the analytical technique due to the temperature characteristics and time-dependent characteristics of the ESS SOC, the reinforcement learning that derives optimal scheduling is used. The results show that the SOC curve changes with respect to weight. As the weight of life-cycle cost increases, the ESS output and charge/discharge frequency decrease.

Artificial Deep Neural Network in Hybrid PV System for Controlling the Power Management

The analysis of different components of a grid-linked hybrid energy system (HES) comprising a photovoltaic (PV) system is presented in this work. Due to the increase of the population and industries, power consumption is increasing every day. Due to environmental issues, traditional power plants alone are insufficient to supply customer demand. In this case, the most important thing is to discover another approach to meet customer demands. Most wealthy countries are now concentrating their efforts on developing sustainable materials and investing considerable amounts of money in product development. Wind, solar, fuel cells, and hydro/water resources are among the most environmentally benign renewable sources. To control the variability of PV generation, this sort of application necessitates the usage of energy storage systems (ESSs). Lithium-ion (Li-ion) batteries are the most often used ESSs; however, they have a short lifespan due to the applied stress. Hybrid energy storage systems (HESSs) started to evolve as a way to decrease the pressure on Li-ion batteries and increase their lifetime. This study represents a great power management technique for a PV system with Li-ion batteries and supercapacitor (SC) HESS based on an artificial neural network. The effectiveness of the suggested power management technique is demonstrated and validated using a conventional PV system. Computational models with short-term and long-term durations were used to illustrate their effectiveness. The findings reveal that Li-ion battery dynamical stress and peak value are reduced, resulting in longer battery life.

A novel deep learning‐based integrated photovoltaic, energy storage system and electric heat pump system: Optimising energy usage and costs

The use of photovoltaic (PV) systems has drawn attention as a solution to reduce the dependence on fossil fuel for building energy needs. Moreover, incorporating energy storage systems (ESSs) in PV systems can optimise electric energy costs by increasing dependency on PV-generated energy during electric peak load times. However, current ESSs have limited capacities making it difficult to fully maximise PV-generated energy. We propose a novel integrated energy-efficient system for PV, ESS and electric heat pump (EHP) that maximises the usage of PV energy, optimises ESS usage and reduces EHP energy consumption costs. The components of the proposed integrated system are linked with a deep learning (DL)-based algorithm that forecasts PV energy generation and energy demand of the EHP. The proposed system schedules the charging/discharging time of ESSs depending on peak load times, the forecasted EHP electric demand, and PV-generated energy. The data used were collected for 10 months from a retail shop equipped with an EHP and ESS. We found that the developed DL-based forecasting models for PV and EHP are accurate and reliable (ie, R2 above 0.95). Also, the results show that the proposed integrated energy-efficient PV-ESS-EHP system saves 12% of the total annual electric costs, which corresponds to 1 285 291 Won. The proposed system ensures an efficient method to maximise PV-generated energy resulting in reduced dependency on fossil fuels for building energy needs.

A non-linear electricity planning model for low-cost housing estates during a lockdown in developing countries

Electricity plan for a low-cost housing estate in developing countries requires a comprehensive understanding of relationships between energy resources and household’s electricity budgets. In this article, an optimization model is proposed for an electricity plan in low-cost housing estate during a total lockdown. The four decision variables contained in proposed optimization are generators operation time, energy storage systems (ESS) operation time, generator noise and electricity supply from national a grid. Particle swarm optimization algorithm was selected as a solution method for the model. The proposed model was used to optimize electricity consumption for 30days lockdown in three low-case housing estates in Lagos, Nigeria. Based on the optimal results, the cost for generator usage is N 64,399,914.12, while the cost for the ESS usage is N310, 072.77. This article’s concluding remarks highlights its contributions to energy literature. The average daily noise dosage for estates 1, 2 and 3 are 69.17, 69.30 and 69.21db, respectively. The average daily CO2 emitted for estates 1, 2 and 3 are 41.52, 43.60 and 41.43kg, respectively.

Theoretical energy storage system sizing method and performance analysis for wind power forecast uncertainty management

Forecasting uncertainties limit the widespread adoption of wind power generation. Energy storage systems (ESSs) are essential for managing uncertainty, and ESS sizing determines the availability of uncertainty management. However, most ESS sizing studies utilize heuristic approaches. Therefore, research on the determination of ESS sizing related to uncertainty management performance is needed. This paper proposes a theoretical ESS sizing method that considers the stochastic properties of the uncertainty. In the proposed method, the power subsystem (PS) and energy subsystem (ES) capacities, which are related to the instantaneous and accumulated uncertainty characteristics of the ESS, respectively, are determined in terms of the confidence interval of the uncertainty statistic. They are presented as simple formulas by applying the extreme value theory. Furthermore, to demonstrate the uncertainty management performance of ESS sizing, the mean absolute error (MAE) is analyzed, as the variance and absolute errors of the uncertainty determine the MAE of the PS and ES, respectively. A numerical study using real wind power generation and its forecasting data verifies that the proposed method suitably reflects the characteristics of the uncertainty, with an analysis gap between the analyzed MAE and the actual measured value of less than 1%. This study can act as a reference for the expected performance when using ESS and can be extended to the theoretical economic evaluation of ESS usage.

Zero-emission casting-off and docking maneuvers for series hybrid excursion ships

Ecological areas visits require appropriate management of key ecological features. In protected reserves innovative systems should be developed to facing problems of pollution, exhaust fumes and noise. These underlying conditions lead to special challenges for excursions ships. The switching to a cleaner, safer and quieter transport system seems to be a sustainable long-term solution for the problems of ecological areas, where more electric ship can be an important part of the solution and principally for the last mile. This paper presents a design approach of a hybrid Energy Storage Systems (ESS) for new generation of series hybrid excursion ship devoted to ecological areas to avoid any emission during the casting-off and the docking maneuvers. The main purpose of the paper is to show that, even if the hybrid ESS plus series-hybrid propulsion were already used to other vehicles, the gain for a ship with no regenerative capability is not evident. The proposed hybridization is built upon a fully-active parallel topology with battery and supercapacitors. The sizing methodology and the configuration of the ESS are detailed. For a real mission profile in Alster Lake (Hamburg, Germany) it is shown that a small battery pack of 2 kWh with a supercapacitor bank of 0.54 kWh are necessary to have zero-emission maneuvers. The graphical formalism Energetic Macroscopic Representation (EMR) is used to describe the model and to establish an energy analysis tool. An inversion-based control scheme is then deduced from the EMR and simulation results are provided to validate the proposed methodology. The usage of hybrid ESS avoids 1.39 kg of CO2 during the casting-off and docking maneuvers despite increasing consumption of 0.2 l (+8%). However, the plug-in capability can be introduced and a recharge of the battery when the ship is docked could reduce the global consumption and emissions by 20% in comparison to the original ship.

A Heuristics-Based Policy to Reduce the Curtailment of Solar-Power Generation Empowered by Energy-Storage Systems

Renewable energy sources, on one hand, are environmentally friendly, but on the other, they suffer from volatility in power generation, which endangers power-grid stability. A viable solution to circumvent the intermittent behavior of renewables is the usage of energy-storage systems. In this paper, we study the energy management of a proof-of-concept system consisting of solar panels, energy-storage systems, a power grid, and household loads. Using neural networks, we identify the most relevant parameters impacting the power generation of solar panels, and then train the corresponding network to derive forecasts. We also go one step further, and propose a heuristics-based energy-management policy for the purpose of reducing curtailments. We show that our proposed policy outperforms the naive policy by 8%, which does not consider any power-generation forecasts.

Coordinated charging and discharging strategies for plug‐in electric bus fast charging station with energy storage system

Plug-in electric bus (PEB) is an environmentally friendly mode of public transportation and PEB fast charging stations (PEBFCSs) play an essential role in the operation of PEBs. Under effective control, deploying an energy storage system (ESS) within a PEBFCS can reduce the peak charging loads and the electricity purchase costs. To deal with the (integrated) scheduling problem of (PEBs charging and) ESS charging and discharging, in this study, the authors propose an optimal real-time coordinated charging and discharging strategy for a PEBFCS with ESS to achieve maximum economic benefits. According to whether the PEB charging loads are controllable, the corresponding mathematical models are, respectively, established under two scenarios, i.e. coordinated PEB charging scenario and uncoordinated PEB charging scenario. The price and lifespan of ESS, the capacity charge of PEBFCS and the electricity price arbitrage are considered in the models. Further, under the coordinated PEB charging scenario, a heuristics-based method is developed to get the approximately optimal strategy with computation efficiency dramatically enhanced. Finally, the authors validate the effectiveness of the proposed strategies, interpret the effect of ESS prices on the usage of ESS and provide the sensitivity analysis of ESS capacity through the case studies.

Energy storage systems to exploit regenerative braking in DC railway systems: Different approaches to improve efficiency of modern high-speed trains

The growing attention to environmental sustainability of transport systems made necessary to investigate the possibility of energy optimization even in sectors typically characterised by an already high level of sustainability, as in particular the railway system. One of the most promising opportunity is the optimization of the braking energy recovery, which has been already considered in tramway systems, while it is traditionally overlooked for high-speed railway systems. In this research work, the authors have developed two simulation models able to reproduce the behavior of high-speed trains when entering in a railway node, and to analyze the impact of regenerative braking in DC railway systems, including usage of energy storage systems. These models, developed respectively in the Matlab-Simscape environment and in the open source Modelica language, have been experimentally validated considering an Italian high-speed train. After validation, the authors have performed a feasibility analysis considering the use of stationary and on-board storage systems, also by taking into account capital costs of the investment and annual energy saving, to evaluate cost-effectiveness of the different solutions. The analysis has shown the possibility to improve the efficiency of high-speed railway systems, by improving braking energy recovery through the installation of such storage systems.

Aging Cost Optimization for Planning and Management of Energy Storage Systems

In recent years, many studies have proposed the use of energy storage systems (ESSs) for the mitigation of renewable energy source (RES) intermittent power output. However, the correct estimation of the ESS degradation costs is still an open issue, due to the difficult estimation of their aging in the presence of intermittent power inputs. This is particularly true for battery ESSs (BESSs), which have been proven to exhibit complex aging functions. Unfortunately, this collides with considering aging costs when performing ESS planning and management procedures, which are crucial for the exploitation of this technology. In order to overcome this issue, this paper presents the genetic algorithm-based multi-period optimal power flow (GA-MPOPF) procedure, which aims to economically optimize the management of ESSs by taking into account their degradation costs. The proposed methodology has been tested in two different applications: the planning of the correct positioning of a Li-ion BESS in the PG& E 69 bus network in the presence of high RES penetration, and the definition of its management strategy. Simulation results show that GA-MPOPF is able to optimize the ESS usage for time scales of up to one month, even for complex operative costs functions, showing at the same time excellent convergence properties.

Energy storage system location selection for smart grid applications on distribution networks

Every day, the evaluation of classical distribution networks (DNs) to smart grids (SGs) has become a necessity, and renewable energy sources (RESs) are an important part for smart grids. One of the most significant problems for RESs is the sustainability of energy, because the raw material storage is not possible for renewable energy sources as photovoltaic (PV), wind, etc. Therefore, energy storage systems (ESSs) have an important place for RESs and SGs. The aim of this paper is ESS location selection for selected distribution networks. In this paper, a PV array is modelled and connected to DN randomly as unlicensed manufacturers, and a location planning algorithm (LPA) for location selection of ESSs is studied for higher energy quality at DNs. The location is determined for optimum ESS usage connecting the ESS to the correct bus decreases the power losses and increases the voltage levels. Besides, energy can be stored at the peak-off time, and stored energy can be used at the peak times for the lower cost with the usage of ESSs. Thus, the optimum usage of ESSs will be provided for RESs and SGs.

Optimal Fast Control and Scheduling of Power Distribution System Using Integrated Receding Horizon Control and Convex Conic Programming

In this paper, a convex optimal power flow (OPF) formulation integrated within receding horizon control (RHC) architecture using second-order conic programming (SOCP) is proposed. The main advantages of the proposed method are 1) global optimal scheduling with faster computation time; 2) dynamic models with online control within optimization routine; and 3) integration of uncertain resources and measurements. The effectiveness of this method is evaluated using modified IEEE 32-bus and IEEE 119-bus distribution test systems considering network constraints such as energy market interactions, storage dynamics, and uncertain model of wind generation. The efficiency of the proposed method compared to RHC ac optimal power flow (RHC-ACOPF) is also evaluated using real-time simulator. The results show that the proposed method outperforms the RHC-ACOPF and guarantees global optimal solution. The proposed method also provides effective usage of energy storage system since dynamic modeling of energy storage within the optimization algorithm is possible using RHC integration.