A Probabilistic Method for Energy Storage Sizing Based on Wind Power Forecast Uncertainty

Multi-objective optimization dispatching of a micro-grid considering uncertainty in wind power forecasting

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.

A multivariate time series wind power forecast model, dynamic factor model (DFM), is proposed to tackle a stochastic dynamic optimal power flow (OPF) problem integrated with wind energy and storage devices in this paper. The uncertainty of wind power forecasting is represented by a set of scenarios generated by the DFM. As opposed to other common methods of generating scenarios, such as Monto Carlo simulation, the DFM considers the spatial correlations among wind farms, which manifests a more accurate and real situation. DFM models the wind power as a multivariate stochastic process, which can be decomposed as the multiplication of fixed polynomial matrices and dynamic shocks. Such dynamic shocks are spatially and temporally uncorrelated white noises which in turn become the impulses to generate forecast scenarios. The stochastic optimization is to minimize the total expected generating cost over all scenarios in one day ahead. Storage devices are installed to compensate for the operation cost and power deficiency. The proposed forecast model is verified in both time and frequency domain, and stochastic optimization model is tested over modified IEEE 30-bus system.

We propose a novel statistical wind power forecast framework, which leverages the spatio-temporal correlation in wind speed and direction data among geographically dispersed wind farms. Critical assessment of the performance of spatio-temporal wind power forecast is performed using realistic wind farm data from West Texas. It is shown that spatio-temporal wind forecast models are numerically efficient approaches to improving forecast quality. By reducing uncertainties in near-term wind power forecasts, the overall cost benefits on system dispatch can be quantified. We integrate the improved forecast with an advanced robust look-ahead dispatch framework. This integrated forecast and economic dispatch framework is tested in a modified IEEE RTS 24-bus system. Numerical simulation suggests that the overall generation cost can be reduced by up to 6% using a robust look-ahead dispatch coupled with spatio-temporal wind forecast as compared with persistent wind forecast models.

The uncertainty of wind power generation imposes significant challenges to optimal operation and control of electricity networks with increasing wind power penetration. To effectively address the uncertainties in wind power forecasts, probabilistic forecasts that can quantify the associated probabilities of prediction errors provide an alternative yet effective solution. This article proposes a hybrid artificial neural network approach to generate prediction intervals of wind power. An extreme learning machine is applied to conduct point prediction of wind power and estimate model uncertainties via a bootstrap technique. Subsequently, the maximum likelihood estimation method is employed to construct a distinct neural network to estimate the noise variance of forecasting results. The proposed approach has been tested on multi-step forecasting of high-resolution (10-min) wind power using actual wind power data from Denmark. The numerical results demonstrate that the proposed hybrid artificial neural network approach is effective and efficient for probabilistic forecasting of wind power and has high potential in practical applications.

This paper presents a model for calculating the optimal size of an energy storage system (ESS) in a microgrid considering reliability criterion. A larger ESS requires higher investment costs while reduces the microgrid operating cost. The optimal ESS sizing problem is proposed which minimizes the 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; hence microgrid reliability criterion is satisfied. A practical model for ESS is utilized. Mixed-integer programming (MIP) is utilized to formulate the problem. Illustrative examples show the efficiency of the proposed model.

Unlike other traditional energy resources, wind power outputs depend on natural wind resources that vary over space and time. Accurate wind power forecasting can reduce the burden of balancing energy equilibrium in electrical power systems. In this paper, we propose the short-term probabilistic forecasting of wind energy resources using the enhanced ensemble method. The enhanced ensemble forecasting methods are grouped into two main categories: temporal ensemble and spatial ensemble forecasting. The temporal ensemble forecasting is implemented by autoregressive integrated moving average with explanatory variable model, polynomial regression with time-series data, and analog ensemble for a probabilistic approach. The spatial ensemble forecasting is implemented by geostatistical model and interpolation with geographical property data. In addition, the stochastic approach, analog ensemble is applied to reduce the uncertainty in wind power forecasting and use of Numerical Weather Prediction models for accurate wind power forecasting is considered. We conduct stochastic wind power forecasting using practical data of Jeju power system and evaluate the system reliability on wind power generation variations. As a result, the proposed model shows better performances than single models, while at the same time providing probabilistic forecasts. Based on these forecasts, the grid operators can identify critical operating time points to prepare for problems that can occur in the system due to wind power variations in advance.

There is growing interest in the use of energy storage systems (ESS) to create combined “renewable energy plus storage” power plants. ESS based on lithium-ion batteries have drawn much attention due to their high energy density and low self-discharge. However, as lithium-ion batteries are still costly, a power producer should determine ESS capacity in a sophisticated manner to ensure profitability of the PV plus storage projects. During the project horizon, lithium-ion batteries undergo severe capacity degradation, which must be considered in ESS planning. The degradation rate depends on various stress factors which are affected by ESS sizes and operation. Therefore, this paper aims to propose an advanced framework for calculating the capacity of an ESS supplementing a photovoltaic system considering the effect of the size and operation of ESS on battery degradation while maximizing profitability. Depending on how batteries are used during the project horizon, two scenarios are discussed and an ESS sizing framework for each scenario is suggested. To deal with non-convexity and black-box parameters of the optimal ESS sizing problems, we introduce an iterative algorithm that finds a solution by accessing battery degradation and optimizing profitability repetitively. We adopted the South Korean market for analysis and simulation of the frameworks.

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.

One of the most important issues that must be taken into consideration during the planning of energy storage systems (ESSs) is improving distribution network economy, reliability, and stability. This paper presents a two-layer optimization model to determine the optimal siting and sizing of ESSs in the distribution network and their best compromise between the real power loss, voltage stability margin, and the application cost of ESSs. Thereinto, an improved bat algorithm based on non-dominated sorting (NSIBA), as an outer layer optimization model, is employed to obtain the Pareto optimal solution set to offer a group of feasible plans for an internal optimization model. According to these feasible plans, the method of fuzzy entropy weight of vague set, as an internal optimization model, is applied to obtain the synthetic priority of Pareto solutions for planning the optimal siting and sizing of ESSs. By this means, the adopted fuzzy entropy weight method is used to obtain the objective function’s weights and vague set method to choose the solution of planning ESSs’ optimal siting and sizing. The proposed method is tested on a real 26-bus distribution system, and the results prove that the proposed method exhibits higher capability and efficiency in finding optimum solutions.

This paper concentrates on determining the size of energy storage system (ESS) for microgrid (MG). Renewable energies are intermittent, and their productions are stochastic and can cause insufficiency in supply of electrical systems. Applying an ESS can alleviate impact of renewable energies. ESS acting a main function in the MG, it is advantageous to shave the high demand and store the excess energy. Sizing of ESS can be viewed as primary while considering ESS in the MG. The majority of MGs work in grid-connected mode. ESS is used to store additional power created by sustainable power sources for the duration of low demand period and redispatching during high demand period. To determine size of an ESS, the aim is to minimize total annual operating cost, and this can be accomplished by using power flow decision.

In a power system where there are many fast and/or large changes in generation or consumption, e.g. in a system with large amounts of wind power, it is more complicated to handle the frequency control efficiently. Minimizing regulation costs for the system operator in such a system requires the possibility to simulate the frequency control in the time range from minutes to a few hours. In this paper, it is shown how the frequency control during normal operation can be optimized using a multi‐area model, without exceeding frequency limits or transmission capacity. The model has also been expanded to include uncertainties in wind power forecasts, which may lead to an increase of the regulating costs. The optimization model is applied to numerical examples to show the impact of wind power on costs for regulating power and to show the value of better wind speed forecasts. No load forecast errors are taken into account. Copyright © 2007 John Wiley & Sons, Ltd.

This chapter proposes an optimization framework for dynamic planning of sizing and siting of energy storage systems (ESSs) in an AC microgrid (MG) in the presence of different kinds of distributed generators. Considering the high capital cost of ESS units, it is an economical way to invest ESS capacities according to each year situation. An AC optimal power flow is formulated as mixed-integer nonlinear programming with the objective of minimizing the total operational cost of the MG and the investment cost of the ESS. Three case studies are considered. An optimal power flow calculation without ESS and demand response program (DRP) has been done in case 1 to create a baseline for comparison. In other cases, the optimal sizing and siting of ESS have been done for each year of the planning horizon, where the effects of DRP on the ESS allocation problem are studied in the last case.

Energy storage scheduling design on friendly grid wind power

This paper focuses on the basic framework of operating risk evaluation method considering two key uncertain factors: the wind power prediction and extreme weather. The output model of large scale wind farms including the uncertainty of wind power forecast is proposed in this paper. Then the system generation margin model considering the ramp rate of conventional generators is built up to study the operating risk of power system. At last, based on the forecasted and measured data of wind farms in Gansu province, a simulation on RBTS indicates that the fluctuation of wind power is the essential source of the risk for power system with large scale of wind farms. The outage loss of power system in extreme weather is much higher than the risk in normal weather but with a much lower probability, which is a “HILP” event.