###### Abstract  
This paper proposes a management strategy for the daily operation of an isolated hybrid energy system (HES) using heuristic techniques. Incorporation of heuristic techniques to the optimal scheduling in day-head basis allows us to consider the complex characteristics of a specific battery energy storage system (BESS) and the associated electronic converter efficiency. The proposed approach can determine the discharging time to perform the load peak-shaving in an appropriate manner. A recently proposed version of binary particle swarm optimization (BPSO), which incorporates a time-varying mirrored S-shaped (TVMS) transfer function, is proposed for day-ahead scheduling determination. Day-ahead operation and greenhouse gas (GHG) emissions are studied through different operating conditions. The complexity of the optimization problem depends on the available wind resource and its relationship with load profile. In this regard, TVMS-BPSO has important capabilities for global exploration and local exploitation, which makes it a powerful technique able to provide a high-quality solution comparable to that obtained from a genetic algorithm.  
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Incorporation of ESS can improve the accommodation of renewable generation while reducing greenhouse gas (GHG) emissions. As an example, incorporation of renewable power combined with ESS in California could reduce carbon dioxide (CO\({}_{2}\)) emissions from 90% to 72%, whereas renewable power curtailment reduces from 33% to 9%. In the case of Texas, CO\({}_{2}\) emissions could be reduced from 58% to 54% and renewable power curtailment could be reduced from 3% to 0.3% [4]. Combination of carbon capture and storage devices with conventional generation units is also an option to reduce GHG emissions. However, the combination of renewable generation with ESS can be energetically more effective [5].  
Historically, the acquisition costs of a battery energy storage system (BESS) have been considerably high, limiting their economic performance and consequently their mass adoption. However, when very low GHG emissions are required, BESS can be a critical device to achieve such an ambitious goal.  
In the case of energy provision for an isolated hybrid energy system (HES), incorporation of BESS becomes profitable due to the fact that the fuel consumption and operating hours of a conventional generator are considerably reduced. In the case of a grid-connected HES, retailing rates and feed-in tariffs as well as favorable resources are crucial for their successful adoption [6].  
Heuristic techniques are commonly used to carry out the optimal sizing of a specific HES. Consequently, some of them have been implemented in computational programs such as HOMER Pro(r)[7], iHOGA(r)[8], and Hybrid2(r)[9], among others. Dispatch strategies implemented in most of these tools are based on load following and cycle charging concepts. Load following consists of generating power from conventional units only to satisfy net load (NL), and this approach is frequently suggested in a HES with high share of renewable power, which is much higher than load demand over the year. Conversely, a cycle charging strategy forces conventional generator to operate at its rating power when needed to charge BESS with the remaining energy, so this strategy is frequently implemented when renewable generation is limited [10]. It is important to mention that these strategies do not require any forecast of renewable generation or load demand. However, they are very effective in the management of HES of small scale used on rural electrification projects.  
In the case of a HES of larger scale, energy forecasts are frequently employed to optimize the daily operation. This is a topic that has been widely studied and it is the focus of this work. A complete literature review is presented in the next section.