Maximizing the Economic Benefits of a Grid-Tied Microgrid Using Solar-Wind Complementarity

Abstract

The increasing use of intermittent, renewable energy sources (RESs) for electricity generation in microgrids (MGs) requires efficient strategies for reliable and economic operation. Complementarity between RESs provides good prospects for integrating several local energy sources and reducing the costs of MG setup and operations. This paper presents a framework for maximizing the economic benefits of a grid-tied MG by exploiting the spatial and temporal complementarity between solar and wind energies (solar-wind complementarity). The proposed framework considers the cost of energy production from different RESs and the cost of bi-directional energy exchange with the main grid. For a given RES mix, a minimum system power loss (SPL) threshold can also be determined. However, at this SPL threshold, MG energy exchange cost is not always minimized. The framework determines the optimized SPL value (above the threshold) at which MG energy exchange cost gets minimized. Through this framework, MG operator can decide appropriate RES mix and can achieve various tradeoffs according to the energy production cost, solar-wind complementarity of the site and its required economic objectives.

Qualitative evaluation of spatial complementarity between renewable energy resources with complementarity roses

“…[4] relates the reliability of a hybrid system with the complementarity between energy resources and Refs. [[5], [6], [7], [8]] present practical situations related to complementarity. This method article summarizes a method for the qualitative evaluation of spatial complementarity by means of complementarity roses.…”

A review on the complementarity of renewable energy sources: Concept, metrics, application and future research directions

“…More recently, da Luz & Moura (2019) [90] created a model for optimizing the mix between solar, wind, hydro and biomass power, from a complementarity perspective, in order to supply Brazil's monthly and hourly load demand based on projections for the year 2050. Naeem et al (2019) [46] used the Pearson correlation coefficient for assessing solar-wind complementarity in an optimization model whose objective function aims at maximizing economic benefits of a microgrid, by means of exploiting such spatial and temporal energetic complementarity. Some other models dealing with optimization based on complementarity can be found in [42], [44], [68], [75], [91], [92], [93] and [94].…”

“…The most common purposes for calculating the Pearson correlation coefficient regarding energetic complementarity has been: 1) conducting an statistical analysis for evaluating if the renewable energies available in one region could allow the configuration of efficient power systems based on renewables ( [15], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34]); 2) as a tool for improving the operation or planning of existing power plants or systems ( [12], [13], [35], [36], [37], [38]); 3) as part of the set of equations, parameters and inequalities in an optimization model ( [39], [40], [41], [42], [43], [44], [45], [46]).…”

Combining Re-Analyzed Climate Data and Landcover Products to Assess the Temporal Complementarity of Wind and Solar Resources in Iraq

“…Time series among several renewable resources are evaluated through the use of correlation coefficients, such as Pearson's correlation coefficient (PCC), Spearman's correlation coefficient, and Kendall's correlation coefficient [5]. In this work, PCC is applied to assess the degree of complementarity between wind and solar energies as it is a widely acceptable complementarity index [3,11,12,[42][43][44][45][46][47][48][49].…”