Sizing Of Energy Storage System Research Articles

Optimal Sizing of Energy Storage System in Active Distribution Networks Using Fourier–Legendre Series Based State of Energy Function

A novel method for optimal sizing of energy storage system (ESS) in active distribution networks using state of energy (SOE) function is proposed in this letter. The SOE of ESS in an operational cycle is characterized by an approximately continuous function based on Fourier–Legendre series expansion. Then, the ESS capacity can be determined by the optimized SOE function extreme. The effectiveness and advantages of the proposed method are verified by test cases.

Optimal Planning of Distributed Energy Storage Systems in Active Distribution Networks Embedding Grid Reconfiguration

In this paper, we present a procedure for the optimal siting and sizing of energy storage systems (ESSs) owned, and directly controlled by network operators of active distribution networks. The peculiarity of the proposed planning procedure consists in embedding the grid reconfiguration. We use a recently proposed conditionally exact convex optimal power flow (OPF) as the core of the optimization model. We appropriately model the objective function to include both technical and economic aspects, while keeping the exactness of the relaxed convex OPF. In particular, the proposed procedure accounts for the minimization of: voltage-magnitude deviations, feeders’/lines’ congestion, cost of supplying loads, and investment costs related to the ESSs. In addition, the seasonal configurations of the grid are determined based on 1) network security constraints, and 2) the minimum resistive losses. The stochasticity of loads and renewable productions are also taken into account. We suitably modeled the ESSs to consider their ability to support the network by both active and reactive powers. Two test cases are used to demonstrate, and quantify, the capabilities of the proposed procedure for providing optimal and feasible solutions.

Optimal sizing of energy storage system and its cost-benefit analysis for power grid planning with intermittent wind generation

Abstract Energy storage system (ESS) is a key technology to accommodate the uncertainties of renewables. However, ESS at an improper size would result in no-reasonable installation, operation and maintenance costs. With concerns on these costs outweighing ESS operating profit, this paper establishes a stochastic model to size ESS for power grid planning with intermittent wind generation. In the model, the hourly-based marginal distributions with covariance is first derived from historical data of wind generation, and a stochastic cost-benefit analysis model with consideration of the generation fuel cost expectation and ESS amortized daily capital cost is formed. Then a hybrid solution approach combining the Point Estimated method and the parallel Branch and Bound algorithm (PE-BB) is designed to solve the model. Finally, the stochastic model and PE-BB approach are thoroughly tested on the 10-unit and 26-unit systems with uncertain wind generation. Simulation results confirmed the proposed model and PE-BB approach are effective to optimize ESS size for power grid planning with intermittent wind generation. The cost-benefit investigations on four typical ESSs also indicated that the ESS capital cost, charging/discharging efficiency and lifetime are important properties for optimizing ESS size, and it is not always economically justifiable to install ESS in power system.

Methodology for ESS‐type selection and optimal energy management in distribution system with DG considering reverse flow limitations and cost penalties

As there are different types of energy storage systems (ESS), it is important to have a tool that determines the best alternative to be installed in the distribution systems. This study presents a methodology for optimal determination of size, type and site of ESS in distribution systems with distributed generation (DG). The methodology uses a genetic algorithm and a power flow calculated by the Electric Power Research Institute software OPENDSS. For the optimal ESS charge and discharge management uses a non-linear optimisation model that has as objective an energy cost and loss reduction. In addition, the methodology makes an energy management of the system by limiting the reverse flow with a load shifting process and a cost penalty. Solar and wind power are considered as DG modelled in Homer. Then, a genetic algorithm is used to select the size, type and site of the ESS, considering different lifecycles and costs. This methodology is tested in the IEEE 123 nodes system simulated and adapted in the OPENDSS and the formulation is implemented in MATLAB via COM interface. Finally, results of the methodology are presented and discussed.

Optimal Sizing of Energy Storage Systems for the Energy Procurement Problem in Multi-Period Markets under Uncertainties

In deregulated electricity markets, minimizing the procurement costs of electricity is a critical problem for procurement agencies (PAs). However, uncertainty is inevitable for PAs and includes multiple factors such as market prices, photovoltaic system (PV) output and demand. This study focuses on settlements in multi-period markets (a day-ahead market and a real-time market) and the installation of energy storage systems (ESSs). ESSs can be utilized for time arbitrage in the day-ahead market and to reduce the purchasing/selling of electricity in the real-time market. However, the high costs of an ESS mean the size of the system needs to be minimized. In addition, when determining the size of an ESS, it is important to identify the size appropriate for each role. Therefore, we employ the concept of a “slow” and a “fast” ESS to quantify the size of a system’s role, based on the values associated with the various uncertainties. Because the problem includes nonlinearity and non-convexity, we solve it within a realistic computational burden by reformulating the problem using reasonable assumptions. Therefore, this study identifies the optimal sizes of ESSs and procurement, taking into account the uncertainties of prices in multi-period markets, PV output and demand.

Modeling the temporal correlation of hourly day-ahead short-term wind power forecast error for optimal sizing energy storage system

Due to the inherent stochastic and intermittent nature of wind generation, it is very difficult to sharply improve the wind power forecasting accuracy. The sizing of energy storage system (ESS) for wind farm can effectively reduce the uncertainty caused by the inevitable forecast error of wind power. However, optimal sizing of ESS is a multi-period decision-making problem and it is the key point that exactly captures the variation magnitude and speed of forecast error. This paper proposes a method to establish the multivariate joint cumulative distribution function (JCDF) of hourly day-ahead short-term multi-period forecast errors using Normal/t copula. Based on the proposed JCDF and multiple scenarios technique, a model of optimal sizing of ESS is proposed in which the temporal correlation relationship between different time period forecast errors and probability distribution of each time period forecast error are both considered. The simulation results verify the effectiveness of the proposed methods and show that if the temporal correlation of wind power short-term forecast error is ignored, the rated energy and power capacity of sizing of ESS will be significantly misestimated.

Coordinated PHEV, PV, and ESS for Microgrid Frequency Regulation Using Centralized Model Predictive Control Considering Variation of PHEV Number

The integration of plug-in hybrid electric vehicles (PHEVs), photovoltaic (PV) generators, and energy storage systems (ESSs) into microgrids is highly anticipated. A coordinated control of PHEVs, PVs, and ESS will support frequency control in a microgrid. However, the size of the ESS depends on the surplus power of PV. The lower the surplus power is, the smaller the size of ESS. Furthermore, the number of available PHEVs vary with the cumulative number of the participating PHEVs. This variation of the number of PHEVs may reduce the PHEVs’ control effect in the microgrid. This paper proposes a coordinated control of PHEVs, PVs, and ESSs for frequency control in the microgrid using a centralized model predictive control (CMPC) considering the variation of PHEV numbers. The objectives of the coordinated control are: 1) to suppress the system frequency fluctuation and 2) to minimize the surplus power of PV and, therefore, reduce the size of ESS. Simulation studies indicate that the coordinated control of PHEVs, PVs, and ESSs by the proposed CMPC is superior to that of the proportional integral derivative control and the distributed MPC in terms of minimizing the frequency fluctuation, the PV surplus power, and the ESS size.

Optimal Sizing of PV and BESS for a Smart Household Considering Different Price Mechanisms

The study on the sizing of renewable energy generation systems and energy storage systems together in a household considering different price mechanisms can further promote the development of the home energy management system (HEMS). In this paper, a HEMS expressed as a bi-level model is provided to investigated capacity allocation strategy of the photovoltaic (PV) and battery energy storage system (BESS) in a smart household considering: 1) the impact of electricity price mechanisms which include the time-of-use pricing (TOU), the real-time pricing (RTP), and the stepwise power tariff (SPT); 2) the effect of subsidies of PV; and 3) the uncertainty in the PV output and seasonal load profiles. Then, the hybrid approach which combines the cataclysmic genetic algorithm and the DICOPT solver in GAMS is employed to find an optimal solution. Finally, six cases with different price mechanisms and approaches, as well as the sensitivity analysis of optimal solution to subsidies are presented. Results indicate that, with the subsidies, only the PV system needs to be equipped in a household under the SPT, while the PV system and BESS need to be equipped together under the RTP and TOU. Only when the subsidies of PV reach a certain level will the installation of PV be considered.

Technoeconomic analysis of a grid-connected PV and battery energy storage system considering time of use pricing

This paper examines the technoeconomic optimization of the size of valve-regulated lead acid-type battery energy storage systems (BESSs) in order to minimize the total annual operating cost of a grid-connected photovoltaic BESS within the framework of system operational constraints, using the improved harmony search algorithm. The electricity cost is calculated using time of use pricing, where the price of energy varies according to the load demand. The concept of feed-in-tariff is discussed, where power can be bought from and sold back to the grid. A sensitivity analysis is performed for three cases: without storage, with storage and without peak load shaving, and with both storage and peak load shaving. The total annual operating cost of the system is calculated for all three cases and cost savings are compared. The simulations, performed with MATLAB, show a good optimization performance.

Optimal sizing and operation of energy storage systems considering long term assessment

This paper proposes a procedure for estimating the optimal sizing of Photovoltaic Generators and Energy Storage units when they are operated from the utility’s perspective. The goal is to explore the potential improvement on the overall operating conditions of the distribution system to which the Generators and Storage units will be connected. Optimization is conducted by means of a General Parallel Genetic Algorithm that seeks to maximize the technical benefits for the distribution system. The paper proposes an operation strategy for Energy Storage units based on the daily variation of load and generation; the operation strategy is optimized for an evaluation period of one year using hourly power curves. The construction of the yearly Storage operation curve results in a high-dimension optimization problem; as a result, different day-classification methods are applied in order to reduce the dimension of the optimization. Results show that the proposed approach is capable of producing significant improvements in system operating conditions and that the best performance is obtained when the day-classification is based on the similarity among daily power curves.

Novel Adaptive Multi-Clustering Algorithm-Based Optimal ESS Sizing in Ship Power System Considering Uncertainty

The optimal sizing of an energy storage system (ESS) in a power generation system that incorporates photovoltaic (PV) generation is crucial in a power grid for which the reduction of CO2 emissions is important. This problem is particularly challenging when it relates to the power system of a ship because it involves uncertain meteorological and load data along a navigation route. This paper proposes a novel method for multi-objective minimization of investment/replacement cost, fuel cost, and CO2 emissions, to find the optimal size of the ESS considering life-span of the ESS. The generation of power by PV modules on a ship is affected by temporal and geographical variations of irradiation along the navigation route. In particular, operating load conditions and irradiation are uncertain. This paper proposes a novel algorithm for partitioning high-dimensional uncertain data into tractable clusters solved by deterministic optimization method. Case studies of an all-electric ship along a route from Dalian in China to Aden in Yemen are shown to demonstrate the applicability of the proposed clustering-based stochastic optimization method.

Analysis of climatic factors affecting the size and efficiency of the thermal energy storage system

Analysis of climatic factors affecting the size and efficiency of the thermal energy storage system

Sizing of hybrid energy storage system for a PV based microgrid through design space approach

Energy storage plays a crucial role in ensuring reliable power supply in a renewable microgrid. The supply and demand variability is found in different time scales (i.e., instantaneous, diurnal, and seasonal). The nominal discharge duration of multiple storage options can be matched effectively for variability in all relevant time scales. An optimum mix of storage options is important to design a cost-effective system.This paper proposes a generic sizing methodology using pinch analysis and design space for hybrid energy storage in a PV-based isolated power system. Pinch analysis utilises a time series simulation of the system where generation should always exceed the load. The methodology defines the design space as feasible combinations of short, medium, and long-term storage size and PV array rating for the given loads. These design space curves are approximated by quadratic equations and the correlations are used as constraints to determine the optimal mix of supply and storage that minimise the life cycle cost.Four different practical cases in Indian context —a remote village, telecom tower, welding shop, and a standby system for a lift load – are analysed to illustrate the sizing method. As an example, the optimal size for a PV based microgrid supplying a remote telecom tower with an average load of 72 kWh/day is 40 kWp of PV, 5 m3 of hydrogen storage and 58 kWh of battery. The proposed methodology extends the design space approach to obtain an optimal minimum cost solution.

Probabilistic siting and sizing of energy storage systems in distribution power systems based on the islanding feature

Distributed storage systems embedded in distribution power systems could complement renewable generation and improve their operation, reducing peak power levels and providing supply support to island zones in the cases of outages. This paper proposes a multi-objective optimisation, which takes advantage of the possibility of operating in an island mode. The optimisation considers the siting and sizing of storage systems placed on power distribution systems with radial topology. The objectives to be minimised are: the amount of energy storage, power losses and expected energy not supplied (EENS). Loads and generators are uncertainty variables, and a probabilistic power flow based on the point estimate method helps with assessing the electrical parameters.The optimisation uses the IEEE-34 and IEEE-123 test feeders. The Monte Carlo simulation benchmarks some results. The final results show that storage systems could reduce the peaks of power required from the central network and improve other electrical parameters. In addition, those systems with higher interruption probabilities or those working near their operation limits could benefit from storage systems.This point of view is different from other authors’. Here, storage energy systems and the island mode of operation are defined by a probabilistic point of view. The siting and sizing of storage energy systems is decided from data obtained on the probabilistic power flow and the universal generating function.

Scalable Planning for Energy Storage in Energy and Reserve Markets

Energy storage can facilitate the integration of renewable energy resources by providing arbitrage and ancillary services. Jointly optimizing energy and ancillary services in a centralized electricity market reduces the system's operating cost and enhances the profitability of energy storage systems. However, achieving these objectives requires that storage be located and sized properly. We use a bilevel formulation to optimize the location and size of energy storage systems, which perform energy arbitrage and provide regulation services. Our model also ensures the profitability of investments in energy storage by enforcing a rate of return constraint. Computational tractability is achieved through the implementation of a primal decomposition and a subgradient-based cutting-plane method. We test the proposed approach on a 240-bus model of the Western Electricity Coordinating Council system and analyze the effects of different storage technologies, rate of return requirements, and regulation market policies on energy storage participation on the optimal storage investment decisions. We also demonstrate that the proposed approach outperforms exact methods in terms of solution quality and computational performance.

Optimal Allocation of Energy Storage Systems for Voltage Control in LV Distribution Networks

This paper addresses the problem of finding the optimal configuration (number, locations, and sizes) of energy storage systems (ESSs) in a radial low voltage distribution network with the aim of preventing over- and undervoltages. A heuristic strategy based on voltage sensitivity analysis is proposed to select the most effective locations in the network where to install a given number of ESSs, while circumventing the combinatorial nature of the problem. For fixed ESS locations, the multi-period optimal power flow framework is adopted to formulate the sizing problem, for whose solution convex relaxations based on semidefinite programming are exploited. Uncertainties in the storage sizing decision problem due to stochastic generation and demand, are accounted for carrying out the optimal sizing over different realizations of the demand and generation profiles, and then taking a worst-case approach to select the ESS sizes. The final choice of the most suitable ESS configuration is done by minimizing a total cost, which takes into account the number of storage devices, their total installed capacity and average network losses. The proposed algorithm is extensively tested on 200 randomly generated radial networks, and successfully applied to a real Italian low voltage network and a modified version of the IEEE 34-bus test feeder.

Multi-Objective Optimization of a Hybrid ESS Based on Optimal Energy Management Strategy for LHDs

Energy storage systems (ESS) play an important role in the performance of mining vehicles. A hybrid ESS combining both batteries (BTs) and supercapacitors (SCs) is one of the most promising solutions. As a case study, this paper discusses the optimal hybrid ESS sizing and energy management strategy (EMS) of 14-ton underground load-haul-dump vehicles (LHDs). Three novel contributions are added to the relevant literature. First, a multi-objective optimization is formulated regarding energy consumption and the total cost of a hybrid ESS, which are the key factors of LHDs, and a battery capacity degradation model is used. During the process, dynamic programming (DP)-based EMS is employed to obtain the optimal energy consumption and hybrid ESS power profiles. Second, a 10-year life cycle cost model of a hybrid ESS for LHDs is established to calculate the total cost, including capital cost, operating cost, and replacement cost. According to the optimization results, three solutions chosen from the Pareto front are compared comprehensively, and the optimal one is selected. Finally, the optimal and battery-only options are compared quantitatively using the same objectives, and the hybrid ESS is found to be a more economical and efficient option.

Energy-storage system sizing and operation strategies based on discrete Fourier transform for reliable wind-power generation

Abstract The use of wind-power generation (WPG) increases supply-side variability, and hence reduces the reliability of power generation. Even if WPG is forecasted to deal with such a problem, the uncertainty of WPG cannot be fully resolved because of its randomness and intermittent nature. This paper proposes a two-step energy-storage system (ESS) sizing and operation strategy for enhancing the WPG reliability. From the analysis of WPG data, it is determined that the uncertainty that leads to deterioration in reliability affects the instantaneous changes in WPG significantly. In this regard, ESS sizing and operation strategies are designed based on the frequency-domain analysis using the discrete Fourier transform (DFT). The ESS sizing is firstly determined to filter out high-frequency components, and the ESS operation is determined considering the ESS sizing constraints for reducing the difference between the actual WPG and its forecasting. A case study is presented using the data from a large-scale wind farm with 4782 MW total capacity of 42 plants located in Columbia River Gorge, United States. The numerical results obtained demonstrate that the proposed ESS method can reduce the root-mean-squared error by up to 26% and further reduce by about 3% compared to the conventional method with the ESS.

Smoothing of renewable energy generation using Gaussian-based method with power constraints

Integration of renewable energy resources to a power system can cause power fluctuations due to their intermittent nature. One way to reduce these effects is to smooth power production using energy storage systems (ESS). A typical approach to tackle the intermittency problem is to use ESS with traditional moving average method. Although it is easy to implement, the moving average method is affected by peaks and troughs during power generations which results in bigger battery sizes. In this paper, we propose a Gaussian-based smoothing algorithm that solves the pitfalls of the moving average methods. Besides smoothing, in big solar plants and wind farms such as in La Réunion island, the grid operator asks energy providers to provide power with a minimum difference from one time to another. We add this constraint on top of the smoothing problem. Then, we determine a minimum possible size of ESS so that the smoothed output power is maintained during the day-ahead forecast period. To test our approach, we use a day-ahead forecast and real data of an industrial site located in France. Bench-marking the moving average methods, we compare performances of the proposed algorithm using two metrics. Based on our simulation results, we obtained at least 34% improvement in smoothness measure and at least 19% reduced ESS size by using the proposed algorithm.

Energy storage systems (ESS) and microgrids in Brittany islands

Sein Island (French Brittany) has an isolated and autonomous network (Fig. 1) that is not connected to the continental electricity grid. It is essential on these islands is to ensure the highest possible degree of robustness and quality-of-service. Currently, power is provided almost solely by diesel generators (gensets) with a high kWh cost and greenhouse gas emissions. Raising the share of renewable energies (RE) in the mix could solve these issues. Thus, 90 kWc of photovoltaic (PV) will be installed in 2017. However, because of renewable energy intermittency, network inertia and gensets features, the deployment of RE may endanger the stability of the power system. To maintain the security of the grid without curtailing PV, Electricite De France has decided to install in 2017 an energy storage system (ESS). A specific simulation tool, developed at EDF R&D, allowed for control algorithm elaboration and ESS sizing. The possibility was shown to completely switch off the gensets and operate the grid solely with the PV and the ESS. The associated issues especially in terms of (i) operation of the ESS and (ii) protection plan are discussed. As a prospect, an experimental validation is planified on our EDF R&D facility of Concept Grid.Fig. 1Single line diagram of Sein Island