Capacity Of Energy Storage System Research Articles

Aggregate demand response strategies for smart communities with battery-charging/switching electric vehicles

Demand response (DR) plays an important role in the peak shaving and load-shifting of a community, but the avalanche effect creates a huge obstacle in DR with uncoordinated usage of appliances and charging of electric vehicles (EVs). Recognizing the remarkably rising deployment of EVs, there is great potential to coordinate EVs' charging in a charging station or a battery switching station for providing DR. However, the potential benefits of the battery-switching EVs for providing DR and their impacts on the aggregate load of the community have not been fully studied. In this regard, the present study proposes two novel aggregate community-level DR strategies, i.e., a strategy employing battery-charging EVs and a strategy employing battery-switching EVs. The proposed strategies are compared with the conventional strategy that only incorporates community battery energy storage for enabling peak shaving. The cost optimization is conducted with optimization parameters including demand limits during different periods and the power and energy capacity of the battery energy storage system. A stochastic bottom-up demand model for households, a probabilistic model for predicting loads of EVs, and a redistribution time-of-use tariff are implemented in the optimization. A group with 20 households and 20 EVs in New York, USA is used in the case study. The results in the case study show that the total annual costs (electricity tariff cost and battery energy storage cost if any) are reduced by 63.3 %, 54.0 %, and 14.7 % under the battery-switching EV strategy, the battery-charging EV strategy, and the battery energy storage strategy compared to the base case (i.e., DR-free strategy), respectively. Additionally, the battery-switching EV strategy enables DR throughout the day with the maximum temporal flexibility. Last, the developed two EV strategies can provide a good stability of the household loads on the grid and avoid the avalanche effect.

Optimal clustering-based operation of smart railway stations considering uncertainties of renewable energy sources and regenerative braking energies

The smart railway stations (SRSs), as prosumer microgrids, are considered active users in smart grids. By utilizing regenerative braking energy (RBE) and renewable energy resources (RERs) along with energy storage systems (ESSs), these SRSs can participate in the prosumer market. The uncertainties of RERs in SRSs due to meteorological factors have been studied in the literature. However, there is a research gap in developing a stochastic method for optimized operating of SRSs considering the RBE uncertainties besides the RER, load, and number of passengers’ uncertainties. In this paper, a new probabilistic clustering-based framework for the optimal operation of SRSs is presented. By applying Monte Carlo Simulations (MCS), several scenarios are generated and then clustered by the k-means algorithm. The introduced method is applied to an actual SRS in Tehran Urban and Suburban Railway Operation Company. The test results of the MCS, deterministic, and proposed scenario-based approaches are compared to illustrate the proposed method. Test results imply that the related error of the scenario-based method under the real-time pricing can be less than 4.4%, while the computation time significantly decreases. Furthermore, sensitivity analysis is done to determine how the exchanging power constraints and ESS capacity might influence the SRS operation.

The power balancing benefits of wave energy converters in offshore wind-wave farms with energy storage

With many countries planning to significantly increase grid renewable energy penetration levels, we consider the role of wave energy in supply–demand matching. We investigate how incorporating wave power into an offshore wind farm affects farm power predictability, smoothness, required energy storage capacity, and cost. In this paper, we do a first-order cost analysis of an offshore farm comprised of floating wind turbines and wave energy converters that are both standalone and combined and onshore compressed air energy storage. Then, we do a parameter sweep investigation of an isolated power network supplied by varied grid renewable energy penetration levels supplemented by natural gas, varied distribution of renewable energy between wind and wave power, and varied power capacity of a compressed air energy storage system supplying power to a shoreline community. For each parameter set, we consider the historical hourly electricity demand and wind-sea data of a coastal California community over a year, and optimize the energy storage schedule to reduce curtailed power, stored energy, and base gas plant operational cost. We show that a co-located wind-wave farm has smoother power supply, less energy curtailment, and higher farm-to-grid efficiency than a solely wind farm. That is, a 50%–50% wind-wave farm has a 15% smaller coefficient of variation in the power supply, 6% less curtailed power, and 2% higher farm-grid efficiency than a 100% wind farm when the grid is 100% renewable energy. These benefits of wave power potentially decrease the need for interconnecting regional transmission lines to match power supply with demand. The intent of this paper is to provide baseline system technical results to help future researchers and policy makers make decisions about offshore hybrid wind-wave-storage farms.

Optimal Allocation of Energy Storage Capacity of High-Permeability Photovoltaic Power Generation System Based on Elastic Neural Network

The energy storage capacity configuration of high permeability photovoltaic power generation system is unreasonable and the cost is high. Taking the constant capacity of hybrid energy storage system (Hess) composed of high permeability wind frame and super capacitor as the standard, in order to ensure smooth and stable output of photovoltaic power equipment, this paper adopts the power suppression method based on empirical mode decomposition to decompose the original power into high and low frequency components, and uses super capacitor and energy storage battery to stabilize the high and low frequency components, Reduce output power fluctuation; Taking the minimum energy storage cost as the objective function, the optimal allocation of system energy storage capacity is realized by the recursive algorithm of elastic neural network. The experimental results show that when the supercapacitor capacity and photovoltaic electric field energy storage cell capacity are 0.262 mw· h and 2.889 mw· h respectively, the minimum cost is 16987 yuan, and the smoothness index is less than 5%; Compared with before the optimal configuration, the output power of the photovoltaic power generation system after the optimal configuration is smoother, the power peak valley difference is lower, and the loss can be reduced.

Optimal integration of Vehicle to Building (V2B) and Building to Vehicle (B2V) technologies for commercial buildings

The advent of vehicle-to-building (V2B) technology provides an option to export electricity from the battery in electric vehicles (EVs) into buildings to assist in meeting building electric demands. However, there are some constraints on operating the EV fleets as flexible energy storage for buildings, including (i) stochastic and limited availability of EVs for building energy management; and (ii) necessity of bi-directional flow of EVs to operate in both V2B and building-to-vehicle (B2V) modes. Hence, these constraints and the changing degree of electric energy exchange provided by the different numbers of EVs could affect the building energy management system and the dynamic energy usage. The main objectives of this paper are to study the optimal configurations of the number of EVs interacting with a building by examining (1) the impact of different numbers of EVs on the daily electricity bills in a grid-connected commercial building microgrid, (2) the range of allowable EV numbers that enable optimal V2B capacity and reasonable charging loads for enhancing the cost-effectiveness of building energy management; (3) the role of different EV numbers in affecting the required stationary battery energy storage system (BESS) capacity in an isolated commercial building microgrid. It is observed that optimal building connected EV numbers are determined by EV driving patterns, seasonal solar output, building demand profiles, and the price of grid electricity. Thus, a scenario analysis is conducted to determine a range of suitable EV numbers in each scenario. Simulation results indicate that the optimal number of EVs varies greatly between summer and winter, as well as across different solar irradiance scenarios. In a case study where a building’s peak electric consumption is 30 kW and the PV system’s rated capacity is 50 kW, the allowable EV number, depending on the amount of available solar energy, varies from 8 to 29 in summer and changes from 2 to 15 in winter. These results could be used to suggest the optimal EV numbers based on the day-ahead solar energy forecasting, enabling day-ahead planning to either attract more vehicle owners to boost V2B capacity or to limit the number of integrated EVs in order to avoid a spike in total EV charging demand.

배전계통에 있어서 태양광전원의 수용성 향상을 위한 SVR 및 ESS의 운용방안

According to the 3020 RE policy of korean government, renewable sources including PV and WP systems have been actively installed and operated in distribution systems. However, if large-scale PV systems are interconnected in distribution system, hosting capacity in PV systems may be reduced because they may violate the allowable voltage limits(220±13V) due to reverse power flow of PV systems. Under these circumstances, this paper proposes the operation algorithms of SVR(step voltage regulator) and ESS(energy storage system) to improve the hosting capacity of PV systems. Specifically, optimal LDC setting values are obtained by calculation algorithm of LDC setting values for SVR using the Least-squares method that minimizes the error for the entire time interval based on the distribution characteristics of upper and lower limits for the sending voltage of SVR considering the reverse power flow by PV systems. In addition, the minimum introduction capacity of ESS is obtained by evaluation algorithm of the required capacity of ESS to maintain the customer voltage within the allowable voltage limits for each time interval. From the simulation results, it is confirmed that the proposed algorithms are useful and practical tool to improve the hosting capacity of PV system.

Analysis of the Hybridization of PV Plants with a BESS for Annual Constant Power Operation

The electrical generation of photovoltaic systems is variable and non-dispatchable. Energy storage systems can provide the system with energy management capabilities. In particular, with a hybrid system that combines a photovoltaic system and an energy storage system, it is possible to deliver firm power to the grid, if it is correctly dimensioned and operated. The objective of this work is to study the most appropriate relationship between the capacity of the battery energy storage system (BESS) and the peak power of the photovoltaic generator that enables the delivery of constant power throughout the year. Analysis parameters are presented that help to decide the most convenient energy injection constant value (PV-CPG set point) and the size of the storage system. As a case study, the most suitable battery capacity for a 1 MWp photovoltaic system with a battery located in Zaragoza (Spain), and the most convenient annual setpoint values for its operation are analyzed.

Analysis of the problem of optimal placement and capacity of the hydrogen energy storage system in the power system

Nowadays the trend of increasing the generation units based on renewable energy sources in the electric power system can be observed. Obviously, this is due to the intensifying level of consumer load and demand for electricity. However, renewable generation is characterized by intermittent energy production, which can cause and potential imbalance between generation and demand, especially during off-peak periods. Therefore, in order to ensure a reliable power supply to consumers, it is necessary to use a maneuverable reserve of capacity, such as energy storage systems, in conjunction with the renewable energy source unit. Over the past 10 years, the energy storage market has grown by almost 50%: the installed capacity of energy storage system in the world is about 5 GW. Analysis of the literature on the subject determines the need to study the impact of these devices on the parameters of electric power systems and one of the primary tasks is to determine the optimal location and capacity of energy storage system in the power system. This paper presents the result of solving the task of determining the optimal parameters of a hydrogen energy storage system using the particle swarm optimization method for example a test scheme radial distribution system – 33 bus IEEE. The choice of the type of energy storage is based on such advantages of a hydrogen energy storage system as environmental friendliness, high energy capacity and the ability to store electricity for a long period of time. In addition, compared to lithium-ion batteries, hydrogen energy storage systems have a long life time of about 25 years, during this period of time there is no degradation and significant deterioration of its properties. All these advantages of hydrogen as an energy carrier allow to take into account not only the criterion of total value of active power losses and its maximum reduction respectively, but the possibility and economic efficiency of partial use of the stored hydrogen for other needs when determining the optimal scenario of their operation in the process of discharge.

Two-Stage stochastic optimization for operating a Renewable-Based Microgrid

• A conventional MILP algorithm does not consider the uncertainty in operating a renewable-based Microgrid. • The two-Stage stochastic optimization algorithm takes into consideration the potential future PV profile. • The proposed method improves the Microgrid reliability by a 19.4% decrease in the Energy not supplied. • The proposed method decreases the operation cost by 18.8%. Carbon emissions are increasing as a result of urbanization and population growth all over the world. Scientists agree that these emissions are one of the main causes of climate change and global warming. The power industry is shifting to renewable energy sources (RES) such as solar power (PV) and employing different Energy Storage Systems (ESS) to enable a clean energy future and compensate for the scarcity of fossil fuel. However, the intermittent behaviour of renewable resources causes some obstacles such as power fluctuation, committing extra reserve units, and load shedding. Microgrid (MG) technology is introduced as a promising solution for integrating different RES and loads into the grid. Operating the MG in islanded mode with a limited ESS capacity requires a sophisticated scheduling method. Previous studies on MG addressed the operation issue, neglecting the supply–demand uncertainty or adopting the worst-case scenario. Uncertainty is an inherent characteristic in power systems; on the other hand, considering the worst-case scenario may unnecessarily increase the operation and planning costs. To address the uncertainty and fluctuant characteristics of RES-based MG, this paper proposes a two-stage stochastic optimization integrated with a novel ANN-based prediction model. A new model for PV power prediction is proposed by which the predicted data is in a probability density function (PDF) form. A stochastic optimization (SO) method is proposed to minimize the operation cost and load shedding during the islanding mode. In the proposed SO, the optimal scheduling decision is made in the current moment taking into account the probability of potential supply, load, and ESS capacities in the near future. For this purpose, an ANN-based prediction model is developed to represent the PV output uncertainty in the SO problem. The proposed prediction model proves efficient in the prediction with nRMSE of 9.7% and nMAE of 9.1%. The proposed method is applied to a real Microgrid designed by the Natural Energy Laboratory of Hawaii Authority (NELHA) and compared to a conventional optimization method. The proposed scheduling method reduces both the average Energy Not Supplied (ENS) and operation costs by 19.4% and 18.8%, respectively, with no additional investment cost. A sensitivity study is also conducted to assess the performance of the proposed method in terms of ENS, cost, and simulation time.

Study on the Structural Degradation of the Polyaniline(PANi) Coated LiNi0.90Co0.85Mn0.15o Particles

The nickel content of the layered lithium transition metal oxide cathodes is proportional to the discharge capacity of energy storage systems. Ni2+ ion, the oxidation state of Ni, moves to the Li sites when Li+ ion is deintercalated during discharge process due to the similar size of ion (Ni2+: 0.069 nm, Li+: 0.076 nm). The spatial transition of Ni2+ ion causes gradual cation mixing and structure degradation of cathode, resulting in the failure of the battery. Also, thick cathode-electrolyte interphase (CEI) is formed on the cathode surface because of the transition metal dissolution, the degradation of electrolyte, and the residual gas such as H2O and CO2.Coating technology is one of the strategies for the physical protection of the cathode surface and the control of CEI layer thickness. Polyaniline(PANi) is a conductive polymer with stable oxidation state. PANi can enhance the electrochemical property and structural stability as a protective layer. In this study, we synthesized PANi material by the oxidative polymereization and coated NCM with the synthesized PANi by sonication method.The PANi-coated NCM(PANi@NCM) has uniform coating layer with 5 nm thickness. PANi@NCM exhibits an initial discharge capacity of 208 mAhg-1 and a capacity retention of 81% after 50 cycles at 45 oC in Figure 1(a) and (b). After the cycling test, pristine and coated cathodes were disassembled for the comparison of the microstructure. Pristine NCM only shows the structural transition from layered structure to structure in the XRD pattern of Figure 1(c). Cross-sectional SEM image of the pristine NCM after cycling test shows micro and macro cracks are distributed from the bulk to the surface of the cathode as shown in Figure 1(d). On the other side, cracks are barely observed on the PANi@NCM particles in Figure 1(e). The layered structure of PANi@NCM is maintained even after cycling test, which are confirmed by TEM analysis with FFT patterns and HAADF images. Figure (f) and (g) show the FFT pattern of the two cathode surfaces after cycling test. Mixed phase is observed on the pristine cathode surface, while the layered structure is maintained on the PANi@NCM surface. Also, the PANi@NCM maintains a thinner passivation layer compared with that of the pristine NCM (6 nm vs 35 nm). In conclusion, the PANi coating layer prevents the degradation of structure and the formation of thick CEI layer. The structural stability enhances the electrochemical performance. Figure 1

Planning, Operation, and Design of Market-Based Virtual Power Plant Considering Uncertainty

The power systems of today seem inseparable from clean energy sources such as wind turbines (WTs) and photovoltaics (PVs). However, due to their uncertain nature, operational challenges are expected when WT and PV energy is added to the electricity network. It is necessary to introduce new technologies to compensate for the intermittent nature of renewable energy sources (RESs). Therefore, rationally implementing a demand response (DR) program with energy storage systems (ESSs) in a virtual power plant (VPP) environment is recommended as a way forward to minimize the volatile nature of RESs and improve power system reliability. Our proposed approach aims to maximize social welfare (SW) (i.e., maximization of consumer benefits while minimizing energy costs). Our method assesses the impact of the DR program on SW maximization. Two scenarios are examined, one with and one without a DR program. Stochastic programming theory is used to address the optimization problem. The uncertain behavior of WTs, PVs, and load demand is modeled using a scenario-based approach. The correctness of the proposed approach is demonstrated on a 16-bus UK generic distribution system. Our results show that SW and active power dispatch capacity of WT, PV, and ESS are fairly increased using the proposed approach.

Characterization and optimization of energy sharing performances in energy-sharing communities in Sweden, Canada and Germany

Peer-to-peer (P2P) renewable power sharing within a building community is a promising solution to enhance the community’s self-sufficiency and relieve the grid stress posed by the increased deployment of distributed renewable power. Existing studies have pointed out that the energy sharing potentials of a building community are affected by various factors including location, community scale, renewable energy system (RES) capacity, energy system type, storage integration, etc. However, the impacts of these factors on the energy sharing potentials in a building community are not fully studied. Being unaware of those factors’ impacts could lead to reduced energy sharing potentials and thus limit the associated improvement in energy and economic performances. Thus, this study conducts a comprehensive analysis of various factors’ impacts on the energy sharing performances in building communities. Two performance indicators are first proposed to quantify the energy sharing performances: total amount of energy sharing and energy sharing ratio (ESR). Then, parametric studies are conducted based on real electricity demand data in three countries to reveal how these factors affect the proposed indictors and improvements in self-sufficiency, electricity costs, and energy exchanges with the power grid. Next, a genetic algorithm based design method is developed to optimize the influential parameters to maximize the energy sharing potentials in a community. The study results show that the main influential factors are RES capacity ratio, PV capacity ratio, and energy storage system capacity. A large energy storage capacity can enhance the ESR. To achieve the maximized ESR, the optimal RES capacity ratio should be around 0.4 ∼ 1.1. The maximum energy sharing ratio is usually smaller in high latitude districts such as Sweden. This study characterizes the energy sharing performances and provides a novel perspective to optimize the design of energy systems in energy sharing communities. It can pave the way for the large integration of distributed renewable power in the future.

Outlook of industrial-scale green hydrogen production via a hybrid system of alkaline water electrolysis and energy storage system based on seasonal solar radiation

Hydrogen has been considered as a clean energy carrier by generating electricity via fuel cells without carbon dioxide emissions; however, in the current stage, most hydrogen is produced by a steam methane reforming, emitting carbon dioxide as a byproduct, together. In this context, a green hydrogen production system, which is consisted of water electrolysis and a renewable energy plant, should be expanded to prepare for the upcoming hydrogen society in the future. A techno-economic analysis is carried out for green hydrogen production based on seasonal solar radiation data in the case of the single and the hybrid system, which is designed as only alkaline water electrolyzer and a combination of alkaline water electrolyzer and energy storage system. In addition, a carbon footprint analysis is performed to quantify the carbon dioxide emissions for the proposed systems. And the optimal scale of alkaline water electrolyzer and energy storage system is figured out via a genetic algorithm considering a carbon tax on emitted carbon dioxide. Based on itemized cost estimation results, 6.55 and 6.88 USD kgH2−1 of unit hydrogen production costs were obtained for the case of a hybrid and a single system, respectively. Further, the results present that the hybrid system is preferred when Li-ion battery costs decrease to under 79.67 USD kWh−1. In addition, the capital cost is a crucial factor to figure out the optimized alkaline water electrolyzer scale and energy storage system capacity that set the optimized size is important to minimize the unit hydrogen production cost. Finally, the effort to reduce the capital cost to produce the green hydrogen is necessary when increasing trend of carbon dioxide tax is considered.

Optimal sizing of country-scale renewable energy systems towards green transportation sector in developing countries

In the context of refining countries’ transition plans toward 100% shares of renewable systems in the electric power grids by 2050, this study aims to investigate, for the first time, the potential of covering the increasing energy demand for electric vehicles (EVs) using the previously planned hybrid renewable energy system (RES) for Jordan, which comprised wind, solar photovoltaic (PV), and battery energy storage systems (ESS). The optimal RES sizing is revisited to examine the influence of EV penetration percentages and charging rates (normal, medium, and fast) and optimize the charging periods to reduce system capacity. The results showed that for typical nighttime charging and relatively smaller EV penetrations, the RES system capacity remains the same, but the size of ESS must be increased by up to 16.67% for 25% EV penetration. As EV penetration increases, the required ESS capacity drops slightly, but the RES capacity increases by up to 24.53% for 100% EV penetration. Optimal charging periods were found to be during morning hours (6:00 to 9:00 a.m.). Adopting these early charging hours and compared to 0% EV, the required ESS capacity remained nearly the same, except for smaller EV penetration percentages. However, for 100% EV penetration, the increase in required capacity ranged from 24.5% for fast charging to 50.01% for normal charging. For lower penetration percentages, the three modes of charging resulted in comparable capacities of ESS and RES. As the penetration percentage increased to 100%, the fast charging mode required considerably lower RES capacity (by 17.0%) and the same ESS capacity of the 0% EV penetration.

Sizing and Sitting of Battery Energy Storage Systems in Distribution Networks with Transient Stability Consideration

In this study, the capacity and location of battery energy storage systems (BESSs) in a distribution network were evaluated to increase the stability and reliability of power systems by applying the proposed transient stability indicators. The search capability of particle swarm optimization (PSO) combined with Pareto optimality in MATLAB R2019b was used to solve multi-objective problems in steady-state conditions, including active power loss, voltage deviation, and total operating cost. The BESS capacity and location were then derived using the Manhattan distance method. Subsequently, the BESS control model was constructed using the commercial software DIgSILENT PowerFactory 2021 to evaluate generator power smoothing and short-term voltage stability index during the transient response and determine the final capacity and location of BESS in distribution networks. Finally, the accuracy of the proposed method was verified by considering the daily load and solar power generation curves using the IEEE 33-bus radial power distribution network.

Investigative Study of Solidification and Melting of Stearic Acid in Triplex Pipe with Perforated Fin Surface

The energy storage capacity of latent heat thermal energy storage systems is high therefore these systems are efficient among all other thermal heat storage. This study experimentally investigates the charging and solidification of stearic acid (C18H36O2) in the finned triplex pipe heat exchanger with holes on the fin surface. The melting and discharging of stearic acid are carried out at steady inlet heat transfer fluid (HTF) i.e., water temperature. This study also investigates the result of the change in HTF flow rate on solidification and melting of phase change material. Finally, the study focuses on the comparison of average effectiveness for finned triplex tube heat exchanger and finned triplex tube with cylindrical holes on fin surface with rates of 0.33 and 0.43 Kg/s. The results showed that the average effectiveness with a finned triplex tube with a perforated fin surface is 4% more than that of a finned triplex tube at 0.33 Kg/s whereas it is 5 % more at 0.43 Kg/s during the charging process. On the other side during discharging process average effectiveness is 14 % more at 0.33 Kg/s and 11 % at 0.43 Kg/s.

High performance rechargeable aqueous zinc-iodine batteries via a double iodine species fixation strategy with mesoporous carbon and modified separator

With the increasing requirement for high capacity energy storage systems, a large amount of recent work has focused on the development of zinc-iodine batteries (ZIBs) on account of high energy density, fast redox kinetics, and excellent reversibility. Nevertheless, low electron conductivity, the shuttle effect, and highly soluble iodine species (I2, I−, and I3−) have impeded their widespread application. In this study, metal organic framework-5 (MOF-5)-derived mesoporous carbon (MPC) loaded iodine (MPC/I2) cathode and the single-sided ketjen black modified cotton fiber (KB@CF) separator are designed to solve the problems mentioned above. That is, the double fixation strategy using MPC and KB@CF separators for iodine species suppresses the shuttle effect. Therefore, the ZIBs constructed with the MPC/I2 cathode and the KB@CF separator can exhibit excellent electrochemical performance. At the current density of 0.1 A g−1, a high discharge specific capacity of 137 mAh g−1 is still available after 300 cycles. Meanwhile, it exhibits a low capacity decay rate at long cycling (0.030% per cycle over 2000 cycles).

Reliability Evaluation of Standalone Microgrid Based on Sequential Monte Carlo Simulation Method

In order to analyze the influence of uncertainty and an operation strategy on the reliability of a standalone microgrid, a reliability evaluation method based on a sequential Monte Carlo (SMC) simulation was developed. Here, the duty cycles of a microturbine (MT), the stochastic performance of photovoltaics (PV), and wind turbine generators (WTG) were considered. Moreover, the time-varying load with random fluctuation was modeled. In this method, the available capacity of an energy storage system (ESS) was also comprehensively considered by the SMC simulation. Then, the reliability evaluation framework was established from the perspectives of probability, frequency, and duration, and reliability evaluation algorithms under different operation strategies were formulated. Lastly, the influence of WTG and PV penetration and equipment capacity on the reliability was evaluated in the test system. The results showed that the complementary characteristics of wind and solar and the enhancement of the equipment capacity can both improve the reliability; but, with the increase in the penetration rate of WTG and PV, more ESS capacity is needed to cope with the randomness of WTG and PV. In addition, load shedding minimization strategies can minimize the probability and the number of reductions and achieve optimal reliability, which can provide a reference for the formulation of microgrid operation strategies.

Distributed ESS Capacity Decision for Home Appliances and Economic Analysis

The imbalance between the supply and demand of electricity is becoming expansive due to the increase in renewable energy penetration. Consequently, the time-varying electricity price is proposed as a tool to manage the mismatch. To cope with the electricity price difference across time and to extract the benefit to the limit, an energy storage system (ESS) can be a good tool to smooth out electricity usage. Nevertheless, ESS can be expensive due to the high installation cost and centrally managed system. In this paper, we propose a distributed ESS installed in each home appliance. Separately installed batteries can avoid sizeable initial investment costs. We calculated the benefit of the system that mainly stemmed from the electricity price difference and compared the benefits across various sizes of the battery system. In addition, the charging/discharging schedule depends on the power consumption profile and electricity price distribution. Decreasing battery prices and rising electricity price volatility can increase the system’s potential benefit.

Energy Loss Reduction for Distribution Networks with Energy Storage Systems via Loss Sensitive Factor Method

The loss of distribution networks caused by various electrical motors including transformers and generators can significantly affect the efficiency and economical operation of the power grid, especially for new power systems with high penetration of renewable energies. In this paper, the potential of using an energy storage system (ESS) for loss reduction is investigated, where a novel two-stage method for key-bus selection and ESS scheduling is proposed. At the first stage, the most sensitive key buses to the variation of load are selected by using the loss sensitive factors (LSF) method. At the second stage, ESS scheduling is conducted by solving an optimization problem with uncertainties caused by high penetration of renewable energies, where the uncertainties are characterized by confidence levels. The optimal scheduling of ESS including locations, capacities, and working modes are obtained at the second stage. The effectiveness of the proposed method is demonstrated via numerical simulations. The influences of capacities of ESS and confidence levels with respect to uncertainties are also analyzed. It is demonstrated that the loss-reduction performances can be improved if the ESSs are deployed on the buses selected by the LSF method and operated under the developed optimal scheduling method.