Energy Storage Sizing Research Articles

DESIGN OF LVAC DISTRIBUTION SYSTEM WITH PV AND CENTRALIZED BATTERY ENERGY STORAGE INTEGRATION-A CASE STUDY OF CAMBODIA

This paper studies an optimal design of grid topology and integrated photovoltaic (PV) and centralized battery energy storage considering techno-economic aspect in low voltage distribution systems for urban area in Cambodia. This work aims at searching for an optimal topology including size of the battery energy storage by two different methods over the planning study of 15 years. Firstly, the shortest path algorithm (SPA) and first-fit bin-packing algorithm (FFBPA) are used to find out the topology which minimize the line and the load balancing. Secondly, mixed integer quadratically constrained programming (MIQCP) algorithms are developed to search for a topology which minimize conductor use and the load balancing improvement. Next, Genetic algorithm is developed to size the maximum PV peak power connected into LV network with respected to voltage and current constraints. Then, the size of battery energy storage procedure is established in order to eliminate the reverse power flow going on medium voltage (MV) grid and to improve the autonomous operation time of system. A discounted cost method is used to evaluate the solutions for different methods. Lastly, an urban area in Cambodia is chosen as a case study in this paper. Simulation results confirm the proposed method in this research.

Energy storage design considerations for an MVDC power system

ABSTRACTThe U.S. Navy is investing in the development of new technologies that broaden warship capabilities and maintain U.S. naval superiority. Specifically, Naval Sea Systems Command (NAVSEA) is supporting the development of power systems technologies that enable the Navy to realise an all-electric warship. A challenge to fielding an all-electric power system architecture includes minimising the size of energy storage systems (ESS) while maintaining the response times necessary to support potential pulsed loads. This work explores the trade-off between energy storage size requirements (i.e. mass) and performance (i.e. peak power, energy storage, and control bandwidth) in the context of a power system architecture that meets the needs of the U.S. Navy.In this work, the simulated time domain responses of a representative power system were evaluated under different loading conditions and control parameters, and the results were considered in conjunction with sizing constraints of and estimated specific power and energy densities of various storage technologies. The simulation scenarios were based on representative operational vignettes, and a Ragone plot was used to illustrate the intersection of potential energy storage sizing with the energy and power density requirements of the system.Furthermore, the energy storage control bandwidth requirements were evaluated by simulation for different loading scenarios. Two approaches were taken to design an ESS: one based only on time domain power and energy requirements from simulation and another based on bandwidth (specific frequency) limitations of various technologies.

Assessing the Techno-Economic Benefits of Flexible Demand Resources Scheduling for Renewable Energy–Based Smart Microgrid Planning

The need for innovative pathways for future zero-emission and sustainable power development has recently accelerated the uptake of variable renewable energy resources (VREs). However, integration of VREs such as photovoltaic and wind generators requires the right approaches to design and operational planning towards coping with the fluctuating outputs. This paper investigates the technical and economic prospects of scheduling flexible demand resources (FDRs) in optimal configuration planning of VRE-based microgrids. The proposed demand-side management (DSM) strategy considers short-term power generation forecast to efficiently schedule the FDRs ahead of time in order to minimize the gap between generation and load demand. The objective is to determine the optimal size of the battery energy storage, photovoltaic and wind systems at minimum total investment costs. Two simulation scenarios, without and with the consideration of DSM, were investigated. The random forest algorithm implemented on scikit-learn python environment is utilized for short-term power prediction, and mixed integer linear programming (MILP) on MATLAB® is used for optimum configuration optimization. From the simulation results obtained here, the application of FDR scheduling resulted in a significant cost saving of investment costs. Moreover, the proposed approach demonstrated the effectiveness of the FDR in minimizing the mismatch between the generation and load demand.

Optimal renewable generation and battery storage sizing and siting considering local transformer limits

In response to climate change and sustainability challenges, various incentive programs have increased solar photovoltaic (PV) generation interconnection in the low voltage electrical distribution system. However, electric utility support of PV generation is limited by reverse power flow into the electricity network at high penetration levels. This limits the ability to achieve Zero Net Energy (ZNE) behind individual meters and in whole communities. In parallel, district level energy systems, and Advanced Energy Communities (AEC) that include storage, offer a great prospect for integrating high levels of Distributed Energy Resources (DER) into the built environment. Optimally designing such systems to serve communities, commercial, and industrial loads, while maximizing the penetration of solar PV, has been a challenge to Distribution System Operators (DSO) and city planners. This paper proposes a Mixed Integer Linear Program (MILP) optimization to decide the best DER portfolio, allocation, and dispatch, for an AEC that achieves ZNE and islanding while respecting electrical grid operational constraints, with a focus on distribution transformer overloads. The main strategies to avoid transformer overloads were found to be judicious sizing and siting of battery energy storage and also optimally re-distributing PV throughout the community, which increased the ability of the electric infrastructure to support a PV deployment that is 1.7 times larger than the existing transformer capacity without the need for infrastructure upgrades. This work highlights the importance of including local infrastructure capacities, such as distribution transformer constraints when developing projects that result in high renewable penetration throughout the distribution network.

Analysis of an energy storage sizing for grid-connected photovoltaic system

This paper present on the analysis of an energy storage sizing for a small grid-connected PV system. This project is to study the proper sizing of energy storage (battery) in a grid-connected PV system for consumers whom purchase and sell electricity from and to the utility grid. The goal is to minimize the total cost of the operation for a consumer with a PV system with a battery storage system. This is to make sure that minimizing the total annual operating cost while maintaining an efficient system. This study uses typical consumer load consumption, and solar irradiance data throughout a year, while varying the type of battery storage (study lead acid and Lithium ion battery) as an energy storage for a similar system. Since lithium ion is not the main options to be integrated with PV system, this study will then reveal the data in terms of cost on why it is not a popular choice.

Sizing domestic air-source heat pump systems with thermal storage under varying electrical load shifting strategies

The demand for local heat storage to help manage energy demand in dwellings is likely to increase as the electrification of heat through heat pumps becomes more widespread. Sizing thermal energy storage systems has been an important topic in contemporary literature, but the effect of the electrical load shifting tariff and the service the householder receives in terms of space-heating and hot water delivered, however, has not and this is particularly important when households transition from conventional gas fired to low carbon technologies. This paper takes a whole system modelling approach to understand the impact of user demand patterns and load shifting scenarios on the volume of energy storage required for a heat-pump installation. The work uses monitoring data from several family homes to drive the simulation and finds that the level of service the householder receives is sensitive to their patterns of consumption, thermal energy storage volume and the electricity tariff, with some households being far more sensitive to tariff choice than others. The paper introduces a novel, quantifiable measure of service for space-heating and hot water systems that can be incorporated into thermal energy storage sizing procedures.

Battery energy storage sizing based on a model predictive control strategy with operational constraints to smooth the wind power

The variability of wind power and forecast error not only increase the difficulty in using wind power but also bring security and stability issues into the power system. One of the solutions is to integrate an Energy Storage System (ESS) with the wind farm to accommodate the fluctuating wind power. This paper presents an approach of sizing ESS from the perspective of facilitating the integration of the wind farm. The control structure is twofold: the outer loop which determines the expected set-point power in the next hour and the inner loop which makes the actual output power follow the set-point power by charging/discharging ESS. In particular, operational constraints on ESS, such as maximum output power constraints and SOC constraints, are considered in the model predictive controller in the inner loop. According to the proposed control strategy, the average fluctuation rate of the wind storage system with different configuration capacities of ESS is calculated, and the results are visually presented in a sizing decision map. Finally, the most economical size of ESS is determined by presetting the dispatch fluctuation rate. The proposed sizing approach is applied to an actual wind farm in Fujian, China.

Analysis of Optimal Charging Points Location and Storage Capacity for Hybrid and Electric Buses

In order to be attractive in a very competitive market, hybrid electric buses and full electric buses need to reduce the total cost of ownership (TCO) compared to conventional buses. In this regard, the sizing of the onboard energy storage and the charging infrastructure becomes a key design stage. An optimal onboard storage and charging facilities are necessary to offer an appropriate vehicle autonomy, but they involve high investment costs for the manufacturer and fleet operator. Furthermore, the complex interrelations between these parameters make the best-performed system design a challenging process. To face this issue, the paper proposes an optimization methodology for the onboard storage capacity sizing, charging points rate and charging points location, aiming a total cost of ownership improvement for hybrid and full electric bus routes. As case study, several routes have been selected in the city of Donostia (Spain) to techno-economically evaluate the proposed methodology regarding factors such as: onboard storage cost, charging infrastructure cost, fuel cost, and electricity-grid cost.

CAES by design: A user-centered approach to designing Compressed Air Energy Storage (CAES) systems for future electrical grid: A case study for Ontario

Compressed Air Energy Storage (CAES) systems, if designed right, can provide a range of high-value grid services that are required for stable operation of the electrical grid. In this paper, a new design approach for customized CAES based on user-centered design (UCD) methodology is developed. To this end, a study of the electrical grid infrastructure and challenges, with a focus on Ontario (Canada) is presented. Afterward, a system approach methodology called CAES-by-Design is developed that incorporates the impact of grid profiles. Hourly load profiles of the Ontario grid are analyzed. Using a thermodynamic model, it is shown how the performance requirements of an adiabatic CAES (A-CAES) system, in terms of capacity, charge and discharge rates and duration are affected by the grid demand and supply profile. For example, it is observed that a compressor capacity of 13% of maximum excess energy and a turbine capacity of 10% of maximum required energy would be sufficient to capture more than 50% of the charging and discharging opportunities. Furthermore, the impact of charging and discharging cycle dynamics on the sizing and operating characteristics of thermal energy storage (TES) system is discussed. Results of this analysis suggest that a comprehensive assessment tool can be developed based on the presented methodology.

Multiobjective optimal sizing of battery energy storage in grid‐connected microgrid

This work proposes a new multiobjective framework for the sizing of Battery Energy Storage (BES) for grid-connected microgrid. A combined Particle Swarm Optimization and Technique for Order of Preference by Similarity to Ideal Solution (PSO-TOPSIS) is explored to solve the formulated problem. A grid-connected microgrid consisting of various renewable sources and BES is considered as a test system. The results obtained are found to be promising. A trade-off between the formulated objectives is presented and analysed.

Joint Workload Scheduling and Energy Management for Green Data Centers Powered by Fuel Cells

The fuel cell is a promising power source for green data centers due to its high energy efficiency, low carbon emissions, and high reliability. However, because of the mechanical limitations related to fuel delivery, fuel cells are slow in adjusting power output when the energy demand quickly changes, which is called limited load following . Many recent work have studied to mitigate the limited load following by using energy storage to adjust energy supply, but achieves limited successes because of the constraint of energy storage size. In this paper, we address this challenge by changing both energy supply and demand, via joint workload scheduling and energy management. Specifically, we consider multiple geo-distributed data centers powered by both fuel cells and energy storage. An online algorithm has been proposed to minimize the gap between energy supply and demand by jointly managing the fuel cells output and migrating workloads among data centers. Simulations results based on real-world traces show that the proposed algorithms can achieve satisfactory performance.

Energy storage sizing by copula modelling joint distribution for wind farm to be black‐start source

To achieve fast power system restoration with high penetration of wind power, using wind farm (WF) as black-start (BS) source is a promising choice. An energy storage system (ESS) sizing method with the minimum investment cost is proposed to enable WF to be a reliable BS source. The proposed method covers three aspects: (i) providing WF self-starting power, (ii) dealing with frequency deviation when starting up ancillary machines and (iii) smoothing WF output power while starting up generator unit. During BS progress, ESS power sizing and capacity sizing are both considered as random variables and their marginal distributions are estimated by kernel density estimation to avoid the distribution model mismatch. The correlation between these two random variables is discussed, which shows that these two variables are not independent. To model the dependence structure between ESS power sizing and capacity sizing, copula theory is applied to build the joint probability distribution. A tail dependence analysis and goodness-of-fit test included copula selection procedure is conducted to choose the most suitable copula function. A case study with real WF data demonstrates that a better-fitted copula has lower ESS sizing cost.

Optimal sizing and placement of energy storage systems and on-load tap changer transformers in distribution networks

The large-scale deployment of distributed energy resources will produce reverse power flows, voltage, and congestion problems in the distribution networks. This paper proposes a novel optimization model to support distribution system operators planning future medium voltage distribution networks characterized by high penetration of behind-the-meter distributed energy resources. The optimization model defines the optimal mix, placement, and size of on-load tap charger transformers and energy storage devices with the objectives of mitigating network technical problems and minimizing both investment and operation costs. The proposed optimization model relaxes the non-convex formulation of the optimal power flow to a constrained second-order cone programming model and exactly linearizes the non-linear model of the on-load tap changer transformer via binary expansion scheme and big-M method. These two transformations reduce the computational burden of the optimization allowing it to be applicable to real-scale distribution grids, as demonstrated by the results. The numerical results also show that the joint optimization of energy storage devices and on-load tap changer transformers produces a more affordable and flexible planning strategy than the individual optimization of the technologies.

Energy Storage Sizing Strategy for Grid-Tied PV Plants under Power Clipping Limitations

This paper presents an analyses of an Energy Storage System (ESS) for grid-tied photovoltaic (PV) systems, in order to harness the energy usually lost due to PV array oversizing. A real case of annual PV power generation analysis is presented to illustrate the existing problem and future solutions. Three PV modeling techniques have been applied to estimate non-measured non-harnessed PV power to provide an ESS energy and power sizing strategy. Moreover, a control strategy to store or release power from the DC-link, without modifying the Maximum Power Point Tracking (MPPT) strategy, is presented. The results show an estimation of the annual power loss caused by oversizing the PV array. The ESS sizing strategy gives insight into not only the energy requirements, but also the power requirements of the system. Simulation results show that the proposed ESS control strategy is capable of harnessing the extra power without modifying the existing power converter of the PV plant nor its control strategy.

Hybrid Energy Storage Sizing and Power Splitting Optimization for Plug-In Electric Vehicles

In this paper, we develop formulation of a multi-objective optimization problem (MOOP) to optimally size a battery unit (BU) ultracapacitor (UC) hybrid energy storage system (HESS) for plug-in electric vehicle (EV). In this application, the objectives were to minimize cost, weight, volume of the HESS simultaneously maximizing the remaining cycle life of the BU at the end of the driving cycle. The MOOP is solved by the non-dominated sorting genetic algorithm type 2 algorithm. Detailed mathematical models for the BU and UC are given. The thermal effect on performance and sizing are also included in the formulation. The power demand by the EV powertrain is shared amongst the BU and HESS by two methods: First is by using wavelet transformation, while the second is by using power split ratio. The ratio of the power (i.e., power split) handled by each storage unit was determined by the optimizer. A sensitivity analysis was conducted for the power splitting ratio verification. The problem was solved for using the urban dynamometer driving schedule and the highway fuel economy test driving profiles. This has resulted in sizing of an HESS with lower cost, volume, and weight than those existing in literature. Finally, the effect of changing the motor type on the MOOP result was investigated.

Engineering energy storage sizing method considering the energy conversion loss on facilitating wind power integration

This study, based on a novel control strategy, proposes a sizing method for battery energy storage systems (ESSs), which makes the wind power system more dispatchable. The main objective of the proposed control-based sizing method is to facilitate robust unit commitment by smoothing the output power of wind according to a desired reference. Owing to the energy conversion loss, the controller closely monitors the battery state of charge (SOC) to prevent the battery from being fully discharged during the smoothing procedure. The proposed controller can automatically revise the battery dispatching reference maintaining the SOC constant. In each dispatch interval, model predictive control is used in real time to ensure that the output power follows the generated reference. The ESS is sized according to the simulation results solved by the proposed control strategy. Meanwhile, the performance of the proposed control strategy is validated over actual wind power data.

Including power management strategies and load profiles in the mathematical optimization of energy storage sizing for fuel consumption reduction in maritime vessels

Abstract Choosing the optimal type and size of energy storage for a given hybrid maritime vessels is challenging. Investment cost, fuel saving and energy storage expected life time will be affected by the choices. Furthermore, the optimum choices depend on the operation profile of the vessel as well as safety related constraints in different vessel mode of operations. In addition, the optimum power management strategy will be mode dependent as well as dependent on the type and size of onboard energy storage. Finally, the total system has to fulfill certain safety related rules and regulations that typically both favour the use of storage and set some constraints to the size and the utilization of the storage. In this paper we propose a mathematical optimisation model called OBLIVION that stands for “Optimised Battery Lifetime In Vessels Internal Operations and Networks”. OBLIVION is created to support battery investment decisions. Beyond including battery degradation and desired battery lifetime in the choice, the model facilitates analysis of how the investment decisions change for different combinations of vessel operation modes. The key contribution of this paper is the proposed methodology to formulate technical and safety constraints, represent different vessel modes of operation and battery storage degradation in a way suitable for inclusion within mathematical optimisation models. Moreover, analyses that demonstrate how these features affect the storage investment decisions are presented. Mathematical formulations of constraints such as closed and open bus-tie breaker operation, true spinning reserve requirements as well as spinning reserve provided by batteries are included as well.

An Energy Management System for Residential Autonomous DC Microgrid Using Optimized Fuzzy Logic Controller Considering Economic Dispatch

This work presents the operation of an autonomous direct current (DC) DC microgrid for residential house controlled by an energy management system based on low complexity fuzzy logic controller of only 25-rules to manage the power flow that supply house load demand. The microgrid consists of photovoltaic (PV), wind turbine, fuel cell, battery energy storage and diesel generator. The size of the battery energy storage is determined based on the battery sizing algorithm depending on the generation of renewables during all seasons of the year in the eastern region of Saudi Arabia. Two scenarios are considered in this work. In the first scenario: the microgrid consists of solar PV, wind turbine, battery energy storage and fuel cell. The fuzzy logic controller is optimized using an artificial bee colony technique in order to increase the system energy saving efficiency and to reduce the cost. In the second scenario: wind turbine is replaced by a diesel generator, also the rated power of the fuel cell is reduced. In this scenario, a new method is proposed to reduce the generation cost of the dispatchable sources in the microgrid by considering economic dispatch within the optimized fuzzy logic energy management system. To obtain the most suitable technique for solving the economic dispatch problem, three optimization techniques were used which are particle swarm optimization, genetic algorithm and artificial bee colony based on real environmental data and real house load demand. A comparison in terms of energy saving between the two scenarios and a comparison in terms of cost reduction between conventional economic dispatch method and the proposed method are presented.

Effects of different sizes and dispatch strategies of thermal energy storage on solar energy usage ability of solar thermal power plant

The solar energy usage ability is mainly considered in the construction of solar thermal power plant (STPP) which is affected by factors of design direct normal irradiance (DNI), solar multiple (SM), thermal energy storage (TES) size and dispatch fraction (DF). In this paper, a general model is developed in order to obtain the relationship among these factors, the electricity output and the solar energy usage efficiency of STPP. Based on the modeling method, a critical design of DNI is derived and some suggestions for improvement in the design of DNI are presented in order to improve the efficiency, which can result in the reduction of waste solar energy. The effects of SM, TES size and DF on the critical design of DNI are also investigated. It is found that the solar energy usage ability will be only dependent on the TES size when TES size is below 6 h and the solar energy usage ability will be dependent on both TES size and DF when TES size is above 6 h. The increase of TES size and the decrease of DF beyond their corresponding critical design DNI will reduce the solar energy usage ability of STPP. The results obtained can provide an important basis for studying the solar energy utilization capacity of STPP and determine the design parameters of STPP.

An eletricity inventory model for power plant and transmission stasion system with in-house demand and carbon emission cost

This paper develops a mathematical model for optimizing energy storage size for a single power plant and a single transmission station. The electricity generated by the power plant is transmitted to the transmission and distribution station and then consumed by the end-customers. Besides providing electricity to end customers, a power plant also supplies electricity to their utility system to satisfy the in-house demand. The proposed model considers in-house demand, end-costumer’s demand and carbon emissions. The carbon emission is calculated from production activities. The objective of the model is to determine the supply size of in-house demand, shipment size of end-costumer’s demand, production lot size factor and emergency backup factor to minimize a total cost incurred by a power plant and transmission station. We utilize an inventory theory to develop our model and propose an algorithm to obtain the optimal solution for this problem. A numerical example is provided to illustrate the application of model.