Placement Of Energy Storage Systems Research Articles

Reconfiguration and displacement of DG and EVs in distribution networks using a hybrid GA–SFLA multi-objective optimization algorithm

A combined algorithm for reconfiguring and placement of energy storage systems, electric vehicles, and distributed generation (DG) in a distribution network is presented in this paper. The impact of this technique on increasing the network’s resilience during critical periods is investigated, as well as improvements in the network’s technical and economic characteristics. The three objective functions in this regard are the network losses, voltage profile, and running costs, which include the costs of purchasing electrical energy from the upstream network and the devices used, as well as the cost of load shedding. In addition, the impacts of the presence of DG on power flow and the modeling of the problem’s objective function are examined. To solve the problem of reconfiguration and placement of a multi-objective distribution feeder, a genetic algorithm (GA) and shuffled frog leaping algorithm (SFLA) hybrid algorithm is used. The proposed GA–SFLA algorithm is used to solve the problem with changes in its structure and its combination in three stages. Finally, the proposed method is implemented on the 33-bus distribution network. The simulation results show that the proposed method has an effective performance in improving the considered objective functions and by establishing a suitable fit between the different objective functions based on the three-dimensional Pareto front. Moreover, it introduced a more optimized architecture with lower loss, lower operating costs, and greater reliability compared to other optimization algorithms.

Optimal Placement of Multiple DG Units With Energy Storage in Radial Distribution System by Hybrid Techniques

In recent years, distributed generations (DGs) are extremely fast in detecting their location, which helps to satisfy the ever-increasing power demands. The placement of energy storage systems (ESSs) could be a substantial opportunity to enhance the presentation of radial distribution system (RDS). The major part of DG units in RDS deals with the detection of ideal placement and size of the DGs, which efficiently balance the power loss and voltage stability. The ideal location and size of ESSs are examined in standard IEEE-33 and 69 bus systems, which is important to reduce power losses. Nowadays, several algorithms or techniques are modified for the development of hybrid algorithms to improve the quality of DG allocation. In this research, a hybrid shuffled frog leap algorithm (SFLA) with ant lion optimizer (SFLA-ALO) is proposed for the optimal placement and size of the DG and ESS in the RDS to reduce power losses and maintain the stability of voltage. The performance of the proposed SFLA-ALO technique is compared with the implemented BPSO-SFLA technique.

Coordinated Optimal Placement of Energy Storage System and Capacitor Bank Considering Optimal Energy Storage Scheduling for Distribution System Using Mixed-Integer Particle Swarm Optimization

Coordinated Optimal Placement of Energy Storage System and Capacitor Bank Considering Optimal Energy Storage Scheduling for Distribution System Using Mixed-Integer Particle Swarm Optimization

Optimized Energy Storage System Configuration for Voltage Regulation of Distribution Network With PV Access

With the large-scale integration of renewable energy such as wind power and PV, it is necessary to maintain the voltage stability of power systems while increasing the use of intermittent renewable energy sources. The rapid development of energy storage technologies permits the deployment of energy storage systems (ESS) for voltage regulation support. This paper develops an ESS optimization method to estimate the optimal capacity and locations of distributed ESS supporting the voltage regulation of a distribution network. The electrical elements of the network integrated with PV and ESS are first modelled to simulate the voltage profile of the network. Then an improved multi-objective particle swarm optimization (PSO) algorithm is employed to minimise a weighted sum of the overall nodal voltage deviation from the nominal level across the network and across the time horizon and the energy capacity of ESS reflecting the associated investment. The improved PSO algorithm adaptively adjusts the inertia weight associated with each particle based on its distance from the best known particle of the population and introduces the cross-mutation operation for a small distance to avoid falling into local optimal solutions. Then the dynamic dense distance arrangement is taken to update the non-inferior solution set and indicate potential global optimal solutions so as to keep the scale and uniformity of the optimal Pareto solution set. To mitigate the impact of decision makers’ preference, the information entropy based technique for order of preference by similarity to ideal solution is used to select the optimal combination of the ESS access scheme and capacity from the Pareto solution set. The proposed ESS optimization method is tested based on the IEEE 24-bus system with additional imports from high-voltage power supply. The voltage profile of the network simulated without the ESS or with the random or optimized ESS placement is compared to illustrate the effectiveness of the optimized ESS in performing voltage regulation under normal operation and supporting emergency power supply during high-voltage transmission failures.

Economic operational analytics for energy storage placement at different grid locations and contingency scenarios with stochastic wind profiles

The placement of energy storage systems (ESS) in smart grids is challenging due to the high complexity of the underlying model and operational datasets. In this paper, non-parametric multivariate statistical analyses of the energy storage operations in base and contingency scenarios are carried out to address these issues. Monte Carlo simulations of the optimization process for the overall cost involving unit commitment and dispatch decisions are performed with different wind and load demand ensembles. The optimization is performed for different grid contingency scenarios like transmission line trips and generator outages along with the location of the ESS in different parts of the grid. The stochastic mixed-integer programming technique is used for optimization. The stochastic model load demand and wind power are obtained from real data. The uncertainty in the operational decisions is obtained, considering the different stochastic realizations of load demand and wind power. The data analytics is performed on ESS operations in the base and its corresponding contingency scenarios with different locations in the grid. Moreover, it is aided by non-parametric multivariate hypothesis tests to understand their dependence amongst various parameters and locations in the grid. The numerical analysis has been shown on a simple 3-bus system considering all the locational and contingency scenarios.

Optimal sizing and placement of energy storage system in power grids: A state-of-the-art one-stop handbook

• Critical review on ESS planning covering ESS selection, modelling and algorithms. • Thorough discussion on requirements of ESS techniques in different sub-systems. • Systematic methodology of problem formulation for optimal ESS planning. • Comparison of algorithms focusing on optimization and uncertainty management. • Research directions on ESS planning considering demands in future grid. Energy storage system (ESS) has been expected to be a viable solution which can provide diverse benefits to different power system stakeholders, including generation side, transmission network (TN), distribution network (DN) and off-grid microgrid. Prudent ESS allocation in power grids determines satisfactory performance of ESS applications. Optimal sizing and placement of ESS are crucial for power quality improvement of DN and transmission system protection setting. To solve this issue, considerable researches have been done either in modelling or algorithms. However, various options and complex characteristics in different sub-systems make it difficult to appraise a specific method for an ESS application, while the existing reviews only focus on ESS applications in DN. This paper provides a critical one-stop handbook related to one hundred and four methods in six categories covering all kinds of networks and tailored applications. Meanwhile, a systematic methodology is presented with an extensive latest literature review, including ESS selection, evaluation criteria, modelling and solution methods. Particularly, different technical requirements and modelling methods of different sub-systems are outlined. Besides, pros and cons of optimization methods are thoroughly analysed and compared to reveal state-of-the-art studies. Finally, key points in optimal ESS sizing and placement are concluded along with recommendations for future research.

Loss cost reduction and power quality improvement with applying robust optimization algorithm for optimum energy storage system placement and capacitor bank allocation

Power losses cause the underutilization of distributed generation (DG) units in addition to the cost increasing in microgrid. Minimizing these losses has been focused in many papers. Using energy storage system (ESS) is a crucial solution for loss reduction. ESS can balance the power exchange in on-peak times where its location and size optimization can improve the microgrid efficiency and reduce the loss cost significantly. Moreover, to ensure the power quality by improving the voltage profile, capacitor bank can be installed optimally on some buses. Optimization of size and location of the capacitor bank can enhance the reactive power that is leading to power loss reduction. In other words, the capacitor bank is applied to compensate the total reactive power and consequently, the current is reduced that results in power loss reduction. In this article, the problem is defined as the optimum location and size of ESS and capacitor bank in the microgrid. Due to the complexity of the problem in many options for selecting the buses to implement these elements (ESS and capacitor bank), robust approach using the particle swarm optimization algorithm and general algebraic modeling system are applied for optimization process. In addition, the uncertainty of renewable DGs such as photovoltaic and wind turbine is modeled by probability density functions and Monte-Carlo is used for selecting more probable cases in optimization processes. The results show the loss cost reduction and improvement in voltage and power profile with less fluctuations and more stability.

Increasing urban tram system efficiency, with battery storage and electric vehicle charging

This paper examines the possible placement of Energy Storage Systems (ESS) on an urban tram system for the purpose of exploring potential increases in operating efficiency through the examination of different locations for battery energy storage. Further, the paper suggests the utilisation of Electric Vehicle (EV) batteries at existing Park and Ride (P&R) sites as a means of achieving additional energy storage at these locations. The study achieves this through MATLAB modelling utilising captured GPS data and publically available information. This study examines the scenario of uni-directional substations with no interconnection between the overhead catenary for both directions of travel, and discusses the trade-offs between ESS size and required current limits.The results show the savings in both energy and basic CO2 emissions alongside the discussion of Return on Investment (RoI) that can be achieved through the potential installation of ESS at identified ideal locations along the tram network. Moreover, this may be extended to the use of EVs as stationary ESS sited at the existing P&R facilities. Further, the model may also be used to inform future infrastructure upgrades and potential improvements to air quality within urban environments.

Optimal placement and sizing of energy storage systems to improve the reliability of hybrid power distribution network with renewable energy sources

Concept of renewable hybrid energy systems have attracted many utilities and implemented by them too. With such energy systems, customers are not only supplied more economically but also more reliably. Moreover, optimal placements of energy storage systems (ESSs) increase the reliability of such hybrid renewable system up to a great extent. Therefore, this paper presents a new approach for optimal placement and sizing of energy storage systems (ESSs) in hybrid renewable radial distribution system to improve the reliability of such system without violating the system constraints. The cost of energy not supplied (CENS) associated with power service interruption and power shortage is also been considered in the objective function during placement of ESSs. Hence, the proposed optimal placement planning of ESSs is presented with an aim of minimizing the objective function includes cost of energy not supplied (CENS), investment cost and operational cost of ESSs, and power loss in distribution system. It is to be noted that the Particle swarm optimization (PSO) technique is adopted to minimize the objective function. The presented methodology is demonstrated by considering several case studies on 11 kV, 30 bus radial distribution system. Further, a rigorous sensitivity analysis is performed by limiting the number of applied ESSs and varying the maximum capacity of participating ESSs.

An optimal allocation and sizing strategy of distributed energy storage systems to improve performance of distribution networks

Abstract The allocation of grid-scale energy storage systems (ESSs) can play a significant role in solving distribution network issues and improving overall network performance. This paper presents a strategy for optimal allocation and sizing of distributed ESSs through P and Q injection by the ESSs to a distribution network. The investigation is carried out in a renewable-penetrated (wind and solar) medium voltage IEEE-33 bus distribution network for two different scenarios: (1) using a uniform ESS size and (2) using non-uniform ESS sizes. DIgSILENT PowerFactory is used for system modeling and testing, and simulation events are automated using Python scripting. A hybrid meta-heuristic optimization algorithm such as the fitness-scaled chaotic artificial bee colony algorithm is applied to optimize parameters of the objective function. The artificial bee colony algorithm is also applied to justify the results attained from the fitness-scaled chaotic artificial bee colony algorithm. A performance comparison, in relation to proposed PQ injection approach with previously applied P injection technique, is presented. The obtained results suggest that the proposed PQ injection-based ESS placement strategy performs better than the P injection-based approach, which can significantly improve distribution network performance by minimizing voltage deviation, power losses, and line loading.

Optimal sizing and siting of smart microgrid components under high renewables penetration considering demand response

The purpose of this article is to determine the size and place of different components in microgrids (MGs) including renewable energy resources (RERs). Various factors like reliability, the uncertainty of wind speed, solar irradiance, load, and load growth are considered. The Ekbatan residential complex is studied as the pilot case study placed in Tehran, Iran. Ekbatan complex has three separate sets of buildings called phase 1, 2, and 3 considered as smart MGs. The multi-objective optimisation problem is solved considering RERs uncertainties, improving reliability and power quality and minimizing power loss by particle swarm optimisation algorithm. Different constraints in terms of voltage, frequency, resources, and energy storage systems (ESSs) capacity are taken into consideration. The effect of load growth, photovoltaic (PV) and ESSs placement, changing the capital cost of RERs, and demand response of controllable loads are studied on optimal sizing and siting. The proposed method is tested on a wind turbine/PV/fuel cell (FC)/hydrogen tank MGs system and the optimal sizing and siting of mentioned sources could decelerate the rate of increase in the total cost of MG considering the load growth.

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.

Optimal placement of distributed energy storage systems in distribution networks using artificial bee colony algorithm

The deployment of utility-scale energy storage systems (ESSs) can be a significant avenue for improving the performance of distribution networks. An optimally placed ESS can reduce power losses and line loading, mitigate peak network demand, improve voltage profile, and in some cases contribute to the network fault level diagnosis. This paper proposes a strategy for optimal placement of distributed ESSs in distribution networks to minimize voltage deviation, line loading, and power losses. The optimal placement of distributed ESSs is investigated in a medium voltage IEEE-33 bus distribution system, which is influenced by a high penetration of renewable (solar and wind) distributed generation, for two scenarios: (1) with a uniform ESS size and (2) with non-uniform ESS sizes. System models for the proposed implementations are developed, analyzed, and tested using DIgSILENT PowerFactory. The artificial bee colony optimization approach is employed to optimize the objective function parameters through a Python script automating simulation events in PowerFactory. The optimization results, obtained from the artificial bee colony approach, are also compared with the use of a particle swarm optimization algorithm. The simulation results suggest that the proposed ESS placement approach can successfully achieve the objectives of voltage profile improvement, line loading minimization, and power loss reduction, and thereby significantly improve distribution network performance.

Energy storage system utilisation to increase photovoltaic penetration in low voltage distribution feeders

This work introduces a new droop control for energy storage system (ESS) dispatch commands generation. The droop characteristic generates the ESS dispatch commands as a function of the power surplus at the substation transformer. The ESS with the new droop control aims to increase the level of Photovoltaic (PV) penetration in low voltage (LV) distribution networks, for the most economical solution in terms of ESS placement and sizing. The new droop control maintains two independent modes of operation; the first mode is fixed droop (FD) control, and the second mode is variable droop (VD) control. When FD control is employed, the droop characteristic remains invariable during hours of excessive PV generation, while the dispatch commands maintain the total of the grid parameters within the allowable range at all times. When VD control is performed, the slope of the droop characteristic was modified hourly, providing a sound grid operation at all times, while the accuracy of the dispatch commands was enhanced compared to the FD control mode. For the research, a real-life LV network case study that maintains a loop arrangement was utilised for modelling purposes. The operational grid constraints include transmission lines loading, terminal voltage variation, and substation transformer loading. Simulation results revealed that both of the proposed control modes maintain a sound grid operation for 70% PV penetration. The VD control performed better resulting in a lower cost ESS. The study demonstrated that an ESS located in the LV network performed a number of positive functions, including load management, peak load levelling and voltage support.

Utilising energy storage systems to mitigate power system vulnerability

Vulnerability mitigation is one of the most important issues for power system planning and operation. Recent research reveals that energy storage systems (ESSs) have a great potential to be used to mitigate power system vulnerability. However, their benefits greatly depend on the locations and capacities of ESSs installed. Therefore this study aims to find the optimal locations and capacities of ESSs for power system vulnerability mitigation. A vulnerability assessment is proposed in this study to calculate the impact factors for power systems based on generator and line outages. A bus impact severity (BIS) analysis is then designed and used to find the vulnerable buses in the power system. Formulations of optimal locations and capacities for ESS placement are derived and solved by a genetic algorithm-based method. Since better locations for ESS placement can be found, the search space for ESS placement can be reduced effectively. Accordingly, the computational time for finding the optimal solution can be saved and the possibility to obtain a global optimum solution can be improved. Test results show that the proposed method can be used to find the optimal locations and capacities for ESS placement and mitigate system vulnerability effectively.