Stochastic Capacity Expansion Planning of Remote Microgrids With Wind Farms and Energy Storage

Abstract

A majority of remote power systems are going to be supplied by diesel-renewable resources such as wind and photovoltaic energy in the future. However, the unpredictable nature of wind generation increases the concern about the reliable operation of these isolated microgrids. Using energy storage systems (ESSs) is recently accepted as an efficient solution to the volatility and intermittency of renewable energy sources. In this paper, a stochastic programming based on the Monte Carlo approach is introduced for optimal planning of remote systems. So far, most literatures have focused exclusively on the energy storage initial sizing. However, capacity expansion of ESS through the time span can result in significant cost saving and will be illustrated in this paper. Factors such as reliability criteria together with the investment and the operation costs are taken into account in the proposed methodology. This method utilizes practical operational constraints of ESS including efficiency and life cycle. Considering life cycle constraint reinforces the proposed method to completely investigate the difference between ESS technologies. The results of case study demonstrate that the proposed capacity expansion algorithm could lead to about 10% more profit over the traditional energy storage sizing. Index Terms-Energy storage system (ESS), MonteCarlo, planning, wind generation. NOMENCLATURE A. Variables e s ess (t) Stored energy of energy storage system (ESS) at time t. E y ess Energy capacity of ESS in year y. I y Investment cost in year y. IC s y Interruption cost in year y for scenario s. LS s (t) Load shedding at time t for scenario s. LSINDX s (t) Load shedding index at time t for scenario s. M s y Maintenance cost in year y for scenario s. O s y Operation cost in year y for scenario s. OC W Operation cost of wind units. OMC ESS Operation and maintenance cost of ESS. P s ch (t) ESS power charging at time t for scenario s. P s dch (t) ESS power discharging at time t for scenario s. ). M. Bozorg is with the Power

State-Of-The-Art in Microgrid-Integrated Distributed Energy Storage Sizing

“…Nevertheless, the reviewed papers may include all or some of the microgrid total cost components depicted in Figure 1. The works in [4][5][6][7][8][9][10] implement mixed integer linear programming (MILP) to formulate the DES sizing problem. In [4], the renewable generation is not considered and the DES is sized for a microgrid containing only dispatchable DGs which reduces the potential economic benefits of the DES and ignores one of the most important aspects of microgrids.…”

“…A loss of load expectation (LOLE) index is used to evaluate the reliability of the studied microgrid. A DES capacity expansion model is developed in [6] for an isolated microgrid. In this work, the selected DES size is not considered fixed and is updated through the planning time horizon.…”

“…Instead, a new cost associated with operating the DES at low state of charge is introduced to the objective function to prevent unnecessary deep discharge incidents. Similar to [6], the aging of the installed DES is modelled based on the weighted Ah-throughput. In this model, a weighting factor corresponding to the DES state of charge is multiplied by the amount of the actual Ah delivered to obtain what is called the effective cumulative Ah.…”

“…If the DES installation is economically feasible, the model will determine the DES optimal size, technology, location, and charging/discharging schedules. The model can be combined with any of the reviewed MIP-based methods in Section 2, i.e., [4][5][6][7][8][9][10], to improve the accuracy of the obtained results.…”

“…It can be seen from Table 1 that most of the previous works focus on either grid-connected or islanded microgrid operation but not both. Moreover, none of the previous works in DES sizing in microgrids, except the works in [6,9,11] in which a simple DES degradation model is proposed, consider the impact of the depth of discharge on the DES lifecycle and planning decisions. Besides this work, the various DES technologies' characteristics are considered only in [8,24] while the optimal location is considered in [12,31].…”