Reliability-Constrained Optimal Sizing of Energy Storage System in a Microgrid

Summary form only given. This paper presents a model for calculating the optimal size of an energy storage system (ESS) in a microgrid considering reliability criterion. The optimal ESS sizing problem minimizes the investment cost of the ESS, as well as expected microgrid operating cost. By increasing the ESS size the investment cost linearly increases while the operating cost exponentially reduces. Also a larger ESS size increases the reliability of the microgrid. Therefore, the objective would be to find the optimal ESS size in which the summation of ESS investment cost and microgrid operating cost is minimized and the reliability criterion is satisfied. A practical model for ESS is utilized. Mixed-integer programming (MIP) is utilized to formulate the problem. Illustrative examples are presented to show the efficiency of the proposed method.

In this paper, a model for allocating and calculating the optimal size of an energy storage system (ESS) in a microgrid will be proposed. A larger ESS requires higher investment costs while reduces the microgrid operating cost. The optimal ESS sizing and allocating problem will be proposed which minimizes the total cost that includes investment cost of the ESS, as well as expected microgrid operating cost. Utilizing the ESS, generation shortage due to outage of conventional units and intermittency of renewable units is handled. A practical model for ESS is utilized. Mixed-integer linear programming (MILP) will be utilized to solve the DC optimal power flow problem.

Utilization of energy storage system (ESS) in microgrids has turned to be necessary in recent years and now with the improvement of storage technologies, system operators are looking for an exact modeling and calculation for optimal sizing of ESS. In the proposed paper, optimal size of ESS is determined in a microgrid considering demand response program (DRP) and reliability criterion. Both larger and small-scale ESSs have their own problems. A large-scale ESS reduces microgrid operating cost but it includes higher investment costs while a small-scale ESS has less investment cost. The main goal of the proposed paper is find optimal size of ESS in which microgrid investment cost as well as operating cost are minimized. Since the renewable units may not have stable production and also because of the outages that conventional units may have, ESS is utilized and then a reliability index called reliability criterion is obtained. Furthermore, effects of reliability criterion and DRP on optimal sizing of ESS are evaluated. A mixed-integer programing (MIP) is used to model the proposed stochastic ESS optimal sizing problem in a microgrid and GAMS optimization software is used to solve it. Five study cases are studied and the results are presented for comparison.

Energy storage can play an important role by storing the surplus energy and discharging it whenever required maintaining the demand supply balance. Deploying energy storage helps in restoring the imbalance in the grid due to the integration of renewable energy sources and aids in proper utilization of these sources by minimizing the wastage of surplus energy generated at times. Energy storage systems have mostly flexible capacity and it is important to utilize an optimal capacity of the storage system for proper functioning, stability of the system and also for enhanced economic benefits by minimization of total cost of the system. In this paper, to obtain the optimal size of the energy storage system, its operation is worked out as an optimization problem with an objective function of minimization of cost of the system considering different operational constraints. The optimization model is formulated as a Nonlinear programming (NLP) model in a 24-hour time frame. It is tested with the hourly generation of a solar farm and the load demand along with other operational parameters of the storage system. It is important to select suitable energy storage system considering different technical aspects and cost of the system as per the service requirements. Two different energy storage systems have been considered in the study. The results obtained with the sizing model with the respective storage system are stated. Load uncertainty is a major uncertainty in power systems, to see the impact of the uncertain load in the sizing of the storage system, probabilistic load for 24 hours is generated using Monte Carlo Simulation (MCS) considering Normal Probability density function and fed to the sizing model as inputs. The optimization problem is solved using GAMS optimization platform. The results obtained with both the normal load and the probabilistic load are compared and studied. Difference in results is observed for both the cases. It is seen that the probabilistic load offers a realistic study on the impact of varying load demand on the sizing model of the storage system.

The deployment of renewable local generation sources at home can help reduce emission contributions within residential homes. Furthermore, the adoption of the residential PV system is increasing as the economics of installation continues to decrease. An optimally sized battery energy storage system can help maximise the benefits of the power generated from the PV systems while being economical. In this paper, a stochastic optimisation problem is formulated to determine the optimal size of the energy storage system and investigate the benefits of uncertainty consideration in the formulation of the sizing optimisation problem. A multi-objective problem is formulated consisting of two objectives: minimise the cost of purchasing the battery energy storage system, and minimise the amount of energy imported from the grid within the period. The stochastic problem is formulated as a two-stage scenario stochastic problem. The optimal sizing and operation problem was formulated as a mixed-integer linear programming problem and solved using the CPLEX solver. This work also utilises a Monte Carlo approach to deal with the uncertainty in load forecasting. Simulation results show that the proposed approach can estimate an optimal battery energy storage system at the current cost of BESS and clearly indicate the benefit of a stochastic approach.

This paper aims to find the optimal place and size of an energy storage system in a microgrid, considering the grid-connected mode and autonomous mode simultaneously. Energy storage systems are one of the most effective components in today's power grids to improve the power quality of power grids, therefore attracting more attention in this field. Specially, in microgirds which use various kinds of distributed generations, using energy storage systems is necessary to improve their power quality. Finding the optimal place and size of energy storage systems is a common action in microgrids. However, it should be noted that most microgrids can be operated in both of their operation modes and finding optimal place and size of an energy storage system for one of these operation modes doesn't mean that they are optimal for the other mode. This paper presents a new method to find the optimal place and size of an energy storage system for microgrids during daily operation, considering both grid-connected mode and autonomous mode simultaneously. The presented method is based on applying the AC-optimal power flow to find the optimal place and size of the energy storage system.

Optimal sizing of energy storage system in islanded microgrid using incremental cost approach

A sensitivity analysis of Energy Storage System (ESS) sizing is proposed in this paper, in order to evaluate the influence of the ESS capacity in technical market requirements, market benefits and total profitability of the plant. With this objective, several market scenarios have been defined modifying ESS capacity, ESS costs, reserve band availability and energy imbalances prices. An increase in the ESS sizing allows a better real-time operation. On the other hand, the high ESS cost could results in a reduction of the plant profitability. Under current Iberian market conditions, a reduction of ESS costs is not sufficient to increase its profitability. However, under a scenario which penalizes more the energy imbalances, calculating the optimal ESS sizing will increase the overall plant profitability. For this purpose, the optimal ESS sizing should be found for each scenario, as well as optimizing the reserve band offers and applying an advanced energy management strategy in real-time.

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

In this paper, a multi-objective optimization model is proposed to calculate best possible size of energy storage system (ESS). The proposed model is solved utilizing weighted sum method. Positive effects of demand response program (DRP) are considered in the proposed paper. By utilizing the weighted sum method, many various solutions are obtained. Then to select the best possible solution, fuzzy satisfying approach is employed. The proposed multi-objective model includes two conflicting objective functions: 1) the first objective function is minimization of microgrid investment cost as well as operation cost; 2) the second objective function is minimization of loss of load expectation (LOLE). Microgrid includes some local units inside itself which may have some unknown outages and also due to variable and unstable output of renewable units, utilization of ESS is essential to improve stability of microgrid. Impact of DRP implementation is evaluated on microgrid related costs and the results are compared to validate the proposed technique. In order to simulate and model the proposed stochastic ESS optimal sizing problem in a microgrid, a mixed-integer program (MIP) is utilized.

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.

Storage system utilization provides a pivotal support for techno-economic integration of renewables. However, a precise modeling and estimation for optimal sizing of an energy storage system (ESS) combined with their optimal allocation is pertinent for operational and economical planning. While oversizing of ESS results in high capital costs, under-sizing ESS deters its integrative significance. In this paper, a technique for calculating an ESS size under solar and wind uncertainties is presented based on two stage stochastic programming. An AC-OPF probabilistic optimization problem is solved using the formulated two stage stochastic programming method that aims to improve system reliability by increasing its availability and reducing the total cost of maintenance and operation with the integration of optimal sized ESS. The efficacy of the proposed stochastic framework is presented for a modified IEEE RTS 24 bus system that is integrated with hybrid renewable energy sources considering. Numerous scenarios of summer and winter generation profiles are considered to outline the effectiveness of the proposed framework. The results obtained proves the efficacy of optimal ESS integration related to cost optimization, reliability, and optimal power flow.

Determining the size of energy storage system to maximize the economic profit for photovoltaic and wind turbine generators in South Korea

The current development of fuel cell hybrid electric vehicles is facing many technical challenges, evolving the power sources, the power electronic configuration, the energy management strategy, and the control techniques. Among these challenges rises the optimal sizing issue as fuel cell hybrid electric vehicle efficiency is highly dependent on the on-board energy storage system. Thus, this paper focuses on the optimal sizing of the hybrid energy storage systems by considering the energy management strategy based on frequency separation. Best solutions are computed for different load profiles through a multi-objective grey wolf optimizer. Then, the obtained results are presented and discussed.

Hybrid energy storage systems are a popular alternative to traditional electrical energy storage mechanisms for electric vehicles. Consisting of multiple heterogeneous storage elements, these systems require thoughtful design and control techniques to ensure adequate electrical performance and minimal added weight. In this work, a graph-based design optimization framework is extended to facilitate design and control optimization of a battery-ultracapacitor hybrid energy storage system. For a given high ramp rate load profile, a hybrid electrical energy storage system consisting of battery and ultracapacitor packs with proportional-integral controllers is considered. A multi-objective optimization problem is formulated to simultaneously optimize sizing and performance of the system by minimizing mass and deviations from ideal controller performance. This optimization is achieved by adjusting the size of the energy storage system and parameters of the feedback controller. A Pareto curve is provided, which exhibits the tradeoffs between sizing and performance of the hybrid energy storage system. Dynamic simulation results demonstrate optimized designs outperform initial designs in both sizing and electrical performance objectives. The design and control optimization approach is shown to outperform a similar sizing optimization approach.