Allocation Of Energy Storage System Research Articles

Dynamic optimal allocation of energy storage systems integrated within photovoltaic based on a dual timescale dynamics model

Energy storage systems (ESSs) operate as independent market participants and collaborate with photovoltaic (PV) generation units to enhance the flexible power supply capabilities of PV units. However, the dynamic variations in the profitability of ESSs in the electricity market are yet to be fully understood. This study introduces a dual-timescale dynamics model that integrates a spot market clearing (SMC) model into a system dynamics (SD) model to investigate the profit-aware capacity growth of ESSs and compares the profitability of independent energy storage systems (IESSs) with that of an ESS integrated within a PV (PV-ESS). Furthermore, this study aims to ascertain the optimal allocation of the PV-ESS. First, SD and SMC models were set up. Second, the SMC model simulated on an hourly timescale was incorporated into the SD model as a subsystem, a dual-timescale model was constructed. Finally, a development simulation and profitability analysis was conducted from 2022 to 2040 to reveal the dynamic optimal range of PV-ESS allocation. Additionally, negative electricity prices were considered during clearing processes. The simulation results revealed differences in profitability and capacity growth between IESS and PV-ESS, helping grid investors and policymakers to determine the boundaries of ESSs and dynamic optimal allocation of PV-ESSs.

Optimal demand response scheduling and voltage reinforcement in distribution grids incorporating uncertainties of energy resources, placement of energy storages, and aggregated flexible loads

Instead of expanding power plant capacities, which is an extremely expensive investment option, demand response offers an economical solution to the challenges arising from the variability and intermittency of the renewable energy resources and demand variations, particularly during demand peak periods. This paper proposes a multi-objective optimization framework for the optimal power flow problem that integrates a stepwise demand response involving flexible and aggregated loads. The process includes short-term demand forecasting using long short-term memory (LSTM) networks in a smart distribution grid, followed by the optimal allocation of energy storage systems, and load aggregators. By determining the optimal solution point of the multi-objective problem analytically, significant system costs and peak demand can be reduced without compromising system stability. Through numerical studies for a sample study case, a reduction of 22% in system costs, 2% in total voltage variation, and 10% in peak demand is observed for a negligible impact on customers’ convenience.

Research on an optimal allocation method of energy storage system for peak-shaving and valley-filling

Abstract In recent years, the economy has developed rapidly, and the power load has also increased substantially. As a result, the peak-valley load gap also increases gradually, which is not conducive to the stable operation of the power grid. Energy storage system (ESS) has the function of time-space transfer of energy and can be used for peak-shaving and valley-filling. Therefore, an optimal allocation method of ESS is proposed, which is applied to reduce the load gap between peak and valley. Firstly, load standard deviation is used as the index of peak-shaving and valley-filling effects. The comprehensive cost of the ESS is converted to days as an economic indicator. Then, the comprehensive cost and load standard deviation are taken as the optimization objectives and are linearly weighted. Finally, the GUROBI toolkit is called in MATLAB to solve the example, and the optimal allocation of the ESS is obtained. By comparing the load curves before and after the allocation of ESS, the analysis shows that the peak-valley difference of load decreases after the ESS is configured, which proves that the optimal allocation method of ESS is effective.

Allocation of Energy Storage Systems in a Hydro-Thermal-Wind System

Allocation of Energy Storage Systems in a Hydro-Thermal-Wind System

Stochastic power allocation of distributed tri-generation plants and energy storage units in a zero bus microgrid with electric vehicles and demand response

This work proposes a sequential stochastic coordinated energy management scheme (SCEMS) for a multi-energy carrier zero bus microgrid (ZBMG) in the presence of distributed tri-generation plants (TGPs), renewable sources, energy storage systems (ESSs), plug-in hybrid electric vehicles (PHEVs), auxiliary boiler and chiller units, and controllable loads. The first stage of the proposed architecture deals with implementing an integrated price-based demand response program to maximize consumers’ benefits and boost demand-side participation. The impacts of grid-to-vehicle and vehicle-to-grid modes of PHEV operation are investigated in this study. The second stage of this formulated problem considers the optimal power allocation of TGPs, ESS devices and auxiliary units aiming to satisfy economic, environmental, flexible, and reliability-driven objectives. Furthermore, to ensure optimal serving of heat and cooling load, novel heat and cool-following strategies are suggested. The uncertainties associated with the renewable generators, PHEV load, grid energy price, and load demands are modelled and incorporated in the SCEMS following “Hong’s 2m+1 point estimate method”. This stage also optimizes the distribution network structure for efficient microgrid operation. Moreover, a novel P−PQVδ−zero+ bus triplet approach of controlling power factor of dispatchable distributed generator is proposed. The efficacy of the framed problem is corroborated through examples. The simulation studies show that the proposed coordinated energy management strategy can: (1) economically accomplish peak shaving and valley filling for multi-energy demands; (2) reduce operation cost and electric energy loss by 8.19%∼10.38% and 41.63%∼47.91%, respectively; (3) enhance average flexibility index by 4.06%∼5.62% but with a marginal rise in emission by 0.39%∼1.68%; and (4) improve node voltage profile of the system.

Optimization of configurations and scheduling of shared hybrid electric‑hydrogen energy storages supporting to multi-microgrid system

As the energy structure undergoes transformation and the sharing economy advances, hydrogen energy and shared energy storage will become the new norm for addressing future energy demand and user-side storage applications, in order to better meet the flexibility and sustainability requirements of the energy system. This paper focuses on shared energy storage that links multiple microgrids and proposes a bi-layer optimization configuration method based on a shared hybrid electric‑hydrogen storage station for microgrids, combining cooling, heating, and power systems, to better achieve efficient energy utilization and promote sustainable development. The upper-layer model solves the energy storage station capacity configuration problem, while the lower-layer model solves the optimization operation problem of the multi-microgrid system. The lower-layer model is transformed into a constraint condition of the upper-layer model based on the Karush-Kuhn-Tucher condition of the lower-layer optimization model, and the Big-M method is used to linearize nonlinear problems. The rationality and effectiveness of the proposed bi-layer planning model are verified through analytical examples in three different scenarios. The results showed that compared to individual energy storage, shared power storage achieved an average daily net income of $430.00, reduced battery capacity by 75.94 %, and reduced daily operating costs of the microgrids by 11.53 %. Hybrid energy storage increased the daily net income of the energy storage side by 61.67 %, further reduced battery capacity by 67.13 %, and further reduced daily operating costs of the microgrid by 3.39 %. In addition, the optimal capacity allocation of energy storage systems and the operation optimization of multi microgrid systems have been achieved. The results demonstrate that the proposed hybrid energy storage services can effectively reduce user costs, save energy storage resources, and achieve mutual benefits for both users and energy storage operators.

Optimal Co-Planning of ESSs and Line Reinforcement Considering the Dispatchability of Active Distribution Networks

The paper presents a method for the co-optimization of energy storage systems allocation and line reinforcement in active distribution networks. The objective is to guarantee the capability of an active distribution network to follow a dispatch plan by appropriately coping with the high uncertainties of loads and stochastic renewable generation while ensuring the secure operation of the grid and minimizing the power grid losses. The proposed formulation relies on a modified formulation of the so-called Augmented Relaxed Optimal Power Flow (AR-OPF) method, which considers the exact (<i>i.e.</i>, non-approximated) AC-OPF in a convexified way (the AR-OPF is proven to provide the global optimal and exact solution of the OPF problem for radial power grids). To tackle the complexity and computational burden of the proposed planning problem, the Benders decomposition algorithm is used and, in order to enhance the convergence speed of the numerical solution of the proposed problem, the Benders decomposition has been suitably modified to determine the energy storage systems site and size sequentially. To assess the performance of the proposed method, simulations are conducted on a real Swiss distribution network composed of 55 nodes and hosting a large amount of stochastic photovoltaic generation. The sensitivity analysis with respect to the photovoltaic capacity is also carried out to assess the effectiveness of the proposed method.

Research on Smart Power Sales Strategy Considering Load Forecasting and Optimal Allocation of Energy Storage System in China

With the deepening reform of the power system, power sales companies need to adopt new power sales strategies to provide customers with better economic marketing solutions. Customer-side configuration of an energy storage system (ESS) can participate in power-related policies to reduce the comprehensive cost of electricity for commercial and industrial customers and improve customer revenue. For power sales companies, this can also attract new customers, expand sales and quickly capture the market. However, most of the ESS evaluation models studied so far are based on historical data configuration of typical daily storage capacity and charging and discharging scheduling instructions. In addition, most models do not adequately consider the performance characteristics of the ESS and cannot accurately assess the economics of the energy storage model. This study proposes an intelligent power sales strategy based on load forecasting with the participation of optimal allocation of ESS. Based on long short-term memory (LSTM) artificial neural network for predictive analysis of customer load, we evaluate the economics of adding energy storage to customers. Based on the premise of the two-part tariff, the ESS evaluation model is constructed with the objective of minimizing the annual comprehensive cost to the user by considering the energy tariff and the savings benefits of the basic tariff, assessing the annualized cost of ESS over its entire life cycle, and the impact of battery capacity decay on economics. The particle swarm optimization (PSO) algorithm is introduced to solve the model. By simulating the arithmetic example for real customers, their integrated electricity costs are significantly reduced. Moreover, this smart power sales strategy can provide different sales strategies according to the expected payback period of customers. This smart sales strategy can output more accurate declared maximum demand values than other traditional sales strategies, providing a more economical solution for customers.

Research on energy storage allocation strategy considering smoothing the fluctuation of renewable energy

Energy storage technology can effectively solve the problems caused by large-scale grid connection of renewable energy with volatility and uncertainty. Due to the high cost of the energy storage system, the research on capacity allocation of energy storage system has important theoretical and application value. In this paper, an optimization method for determining the capacity of energy storage system for smoothing the power output of renewable energy is proposed. First, based on the actual data of Ulanqab, the output characteristics of wind power and photovoltaic power generation are studied, and the K-means algorithm is used to select typical days. Then, the energy storage configuration model is built according to the objective function and constraints. Finally, genetic algorithm is used to solve the optimization model, obtain the corresponding parameters, and complete the configuration of energy storage capacity. Based on the results of renewable energy spectrum analysis, the minimum capacity of the energy storage system that meets the constraint of target power output volatility after compensation by the energy storage system can be optimized. The simulation results show that at 1 and 10 min, the flattened volatility is about 2% and 5%, while the actual penetration volatility is about 20% and 30%. The volatility of the optimized model is greatly reduced, which proves the effectiveness of the proposed strategy.

Voltage regulation and power loss mitigation by optimal allocation of energy storage systems in distribution systems considering wind power uncertainty

Due to the ever increasing in the energy demand and the growth of the pollution problem, further interest in renewable energy becomes prominent, especially wind energy. However, the intermittent nature of the wind speed results in the system's insecurity problems. Integrating the energy storage system (ESS) optimally is the best so far solution. In this paper, a probabilistic discretizing method is derived and generalized to consider the uncertainties of the wind generators. Moreover, a mixed integer non-linear optimisation problem is constructed comprises three objective functions to be minimized simultaneously. The objectives comprising the total operation and planning cost of ESSs, the average voltage deviation and average power losses. The solving method is a hybrid algorithm incorporates the non-dominated sorting genetic algorithm (NSGAII), multi-objective particle swarm optimisation (MOPSO) and a decision-making algorithm. The aim is to determine the optimal location and sizing of ESSs considering their reactive power capability. 69 radial bus distribution system is adopted to demonstrate the efficacy of the proposed hybrid algorithm and performing some case studies. The simulation results verified the capability of ESSs to enhance the voltage profile and reduce the power losses, notably when reactive power capability is examined.

Expansion planning of active distribution networks achieving their dispatchability via energy storage systems

This paper presents a combined framework for power distribution network expansion planning (DNEP) and energy storage systems (ESSs) allocation in active distribution networks (ADNs) hosting large amount of photovoltaic (PV) generations and loads. The proposed DNEP ensures the reliable operation of the targeted ADN with the objective of achieving its dispatchability while minimizing grid losses by determining the optimal grid expansion to connect new nodes, the reinforcement of existing lines, and the ESS allocation. The allocated ESSs compensate for the stochastic power flows caused by the stochastic loads and generation, allowing ADNs to follow a pre-defined power schedule at the grid connection point. The grid constraints are modeled by using a modified augmented relaxed optimal power flow (AR-OPF) model that convexifies the classical AC-OPF providing the global optimal and the exact solution of the OPF problem for radial networks. The DNEP problem’s complexity is handled by employing a sequential algorithm where new nodes are added one by one, following the priorities determined by the user. In each stage of the sequential planning, the Benders decomposition algorithm determines the optimal solution for investments and ADN operation iteratively. Moreover, the siting and sizing problems associated with the ESSs and line investment are solved separately to enhance the convergence speed. Simulations are conducted on a real 55-node Swiss ADN hosting sizeable stochastic photovoltaic generation.

Multistage Bilevel Planning Model of Energy Storage System in Urban Power Grid Considering Network Reconfiguration

The large-scale integration of renewable energy sources (RESs) and the rapid development of loads cause frequent transmission congestion in the urban power grid (UPG). Transmission system operators usually perform the high-voltage distribution network (HVDN) reconfiguration to mitigate the transmission congestion. However, as the loads and RESs change rapidly, the HVDN reconfiguration might be conducted frequently. This might cause severe security problem. An energy storage system (ESS) provides an effective way of alleviating the transmission congestion. If the ESS is installed and operated elaborately, the transmission congestion of UPG can be mitigated with a little HVDN reconfiguration. Hence, this study proposes a multistage bilevel planning model for the optimal allocation of ESS. The upper-level model aims at maximizing the annual comprehensive revenue of HVDN, and the lower-level model focuses on the minimization of the operational cost. Simulation results carried out on a real-world test system verify that the proposed method has the great potential of reducing the investment and operational cost while mitigating the transmission congestion.

Multi-objective chance-constrained transmission congestion management through optimal allocation of energy storage systems and TCSC devices

Multi-objective chance-constrained transmission congestion management through optimal allocation of energy storage systems and TCSC devices

Optimal allocation of energy storage systems, wind turbines and photovoltaic systems in distribution network considering flicker mitigation

Renewable energies have introduced themselves as efficient resources for power production but their integration into power grid makes some challenges during operation. One of these challenges is the negative impact of wind turbines (WTs) on power quality. Flicker produced by WTs is one of the most important parameters which significantly affects power quality of distribution networks. In this paper, to mitigate flicker produced by WTs, distribution network planning problem is solved by considering power quality issue. For this aim, in the objective function, in addition to power losses and energy storage system (ESS) cost, flicker emission and voltage deviation are also minimized. Due to the complexity of the planning problem, crow search algorithm with differential operator (CSAd) is proposed to optimally site and size WTs and ESSs. In CSAd, new solutions are generated by using the information of high-quality solutions. To investigate the impact of various factors on the network performance, the planning problem is solved at different scenarios: (1) Siting and sizing ESSs, (2) Siting ESSs, WTs and PVs as well as sizing ESSs and (3) Siting ESSs, WTs and PVs as well as sizing ESSs and WTs. Simulation results show that optimal siting and sizing of ESSs and WTs can considerably improve the distribution network parameters in terms of power losses and flicker emission. Moreover, the proposed approach produces more accurate and robust results than the other studied methods.

Optimal Allocation of Energy Storage System in DFIG Wind Farms for Frequency Support Considering Wake Effect

Energy storage systems (ESSs) are being utilized to improve wind farms’ (WF) frequency support capability due to their high reliability, fast response and the dual role of energy users and suppliers. Nevertheless, the problem of how much capacity should each ESS possesses in order to better serve the WFs has never been investigated. With this perspective, this paper proposes an optimal ESS allocation (OEA) scheme for doubly fed induction generator (DFIG) based WFs to mitigate the impact of wake effect in frequency support. Firstly, the synchronous stability of wind turbines under frequency dips is analyzed with the concept of frequency support margin (FSM), and the detrimental impact of the wake effect is also investigated. Subsequently, the role of ESSs to improve wind turbines’ synchronous stability is demonstrated. To make the OEA scheme practical, the wind turbines in a WF are segmented into different clusters based on the received wind speed. Afterwards, the OEA problem is formulated, where the objective is to optimize the coherency of the wind turbine clusters’ FSM level. The simulation results show that ESS can provide secondary frequency support under major grid frequency drops, and the proposed OEA scheme can reduce the risk of loss of synchronous stability.

Enhancing Transient Response and Voltage Stability of Renewable Integrated Microgrids

Integration of renewable generation coupled with an energy storage system (ESS) in a power system increases the complexity of networks’ stability analysis and control. Therefore, an accurate stability assessment of power networks is expected to become a big challenge in the future. In this work, an effective approach to prevent power outage by controlling the source voltage of the power network is formulated to mitigate the effects of grid faults. Small signal stability studies are conducted on a renewable integrated IEEE 9 bus system as a case study with optimized size and allocation of ESS for reducing output power variability of renewables. An assessment is performed to study the effects of load-sharing devices on parallel generators under 6-cycle three-phase fault disturbances. The damping of the power network is increased at nominal and light loading conditions with 6-cycle three-phase fault disturbances through coordinated power system stabilizer (PSS) and static VAR compensator (SVC) at bus 9. The developed framework is extensively analyzed in steady-state conditions using a load flow program. Based on the results obtained, the proposed coordinated PSS-SVC device proves to possess comparatively better performance in terms of enhancing most of the system response rate under various load conditions with overall improved stability.

Heuristic Application on Energy Storage System Allocation in Wind Integrated Power System

Increasing penetration of renewable energy significantly alters the network power flow in terms of direction as well as magnitude. The intermittent and uncertain nature of renewable inputs is a major concern since the power flow in the system is affected considerably. Hence to support bulk integration of renewable energy in the power system, bulk or distributed energy storage systems can be implemented. The energy storage system is utilized to absorb the excess power and meet the demand to avoid violation of system constraints. In this work, a multi-period ac optimal power flow (OPF) problem with energy storage systems (ESSs) is formulated and a set of candidate buses for ESS installation are identified based on economic criterion. A novel hybrid approach of conventional cum heuristic method, newton raphson based particle swarm optimization (NRPSO) method is proposed to optimize the allocation problem. Tests are carried out on IEEE 14-bus system and modified IEEE 14–bus system integrated with wind generation for variable load condition using MATLAB, to assess the optimal location of energy storages on system operation.

Multi-Input Nonlinear Programming Based Deterministic Optimization Framework for Evaluating Microgrids with Optimal Renewable-Storage Energy Mix

Integration of renewable energy sources (RES) in a distribution network facilities the establishment of sustainable power systems. Concurrently, the incorporation of energy storage system (ESS) plays a pivotal role to maintain the economical significance as well as mitigates the technical liabilities associated with uncontrollable and fluctuating renewable output power. Nevertheless, ESS technologies require additional investments that imposes a techno-economic challenge of selection, allocation and sizing to ensure a reliable power system that is operationally optimized with reduced cost. In this paper, a deterministic cost-optimization framework is presented based on a multi-input nonlinear programming to optimally solve the sizing and allocation problem. The optimization is performed to obviate the demand-generation mismatch, that is violated with the introduction of variable renewable energy sources. The proposed optimization method is tested and validated on an IEEE 24-bus network integrated with solar and wind energy sources. The deterministic approach is solved using GAMS optimization software considering the system data of one year. Based on the optimization framework, the study also presents various different scenarios of renewable energy mix in combination with advanced ESS technologies to outline an technical as well as economical framework for ESS sizing, allocation, and selection. Based on the optimal results obtained, the optimal sizing and allocation were obtained for lead-acid, lithium-ion, nickel-cadmium and sodium-sulfur (NaS) battery energy storage system. While all these storage technologies mitigated the demand-generation mismatch with optimal size and location. However, the NaS storage technology was found to be the best among the given storage technologies for the given system minimum possible cost. Furthermore, it was observed that the cost of hybrid wind-solar mix system results in the lowest overall cost.

Optimal allocation of energy storage system and its benefit analysis for unbalanced distribution network with wind generation

Battery energy storage system (BESS) can play a major role to overcome the challenges with renewable energy sources, especially in the context of Smart Grid (SG). In this manuscript, BESS optimal location and capacitance of wind power penetrations, and charging/discharging dispatches of BESS are determined using inherited competitive swarm optimization (ICSO) algorithm while improving the performance of the unbalanced distribution network subject to technical constraints. This approach utilizes the average feeder load from decisive criterion for charging or discharging battery. The wind turbine generator (WTG) charging and sustainable average load (SAL) charging methods for BESS are compared considering conservative mode of discharging. The proposed methodology is investigated on IEEE 37 bus unbalanced radial distribution system. The application outcomes displays that proposed methodology enhances various performance objectives of distribution system by optimally coordinating the dispatches of BESS.

Loss-Voltage Sensitivity Analysis Based Battery Energy Storage Systems Allocation and Distributed Generation Capacity Upgrade

Distributed Generation (DG) has become a key component in modern power industry due its significant advantages over the traditional power generation methods. Nevertheless, best expected outcomes can only be achieved with optimal allocation of DG resources where inappropriate allocation may impose problems in power system stability, protection and quality. This paper presents analytical approaches for optimizing the DG size, BESS (Battery Energy Storage Systems) capacities and power dispatch in Medium Voltage (MV) networks. Since most of the existing analytical approaches related to optimizing DG sizes for minimizing network losses and voltage deviations have considered individual objective function separately, both parameters may not be minimized simultaneously. Thus, in this paper, an analytical methodology was formulated based on an objective function built on new parameter Loss-Voltage Sensitivity Index (LVSI) that evaluates both minimum impact of network loss and voltage variations for optimizing the DG size. The results obtained from this approach were compared with a conventional Genetic Algorithm (GA) formulated by the authors. The BESS capacities are determined considering the effects of Load Proportionality Factor (LPF), State of Charge limits (SOC) of battery storages, number of load areas and the portion of daily off-peak solar generation period energy consumption expected to be served by each BESS unit. The significance of this BESS capacity determination methodology is highlighted as the BESS capacities can be numerically calculated whereas in existing work, they are heavily relying on conventional optimization techniques which do not give an idea about the internal behavior of parameters which determine the capacities of BESS units. Moreover, an optimal BESS dispatch algorithm is also presented in this paper for minimizing the energy losses and voltage deviations. The applicability of the proposed methodologies are verified using the standard IEEE-33 and IEEE-69 test bus systems. Simulations carried out in MATLAB is used to illustrate the accuracy and the appropriateness of the proposed approaches.