Energy Management for Techno-Economic Hybrid Microgrid Optimization: Comparison
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Please note this is a comparison between Version 2 by Camila Xu and Version 1 by Adrian Ilinca.

Integrating energy management techniques into renewable energy systems (RESs) improves microgrids’ energy efficiency and stability. This implementation reduces peak loads and energy costs by aligning consumption patterns with producing renewable energy sources.

  • energy management
  • techno-economic optimization
  • hybrid systems
  • PV–wind

1. Introduction

Recently, renewable energy systems (RESs) have emerged as highly attractive solutions for mitigating global warming and reducing greenhouse gas (GHG) emissions in the electricity generation sector [1]. The rapid technological and market advancements in renewable energy have led to a significant decrease in the costs of RES components and, consequently, the cost of energy (COE). Moreover, the recent price escalation of fossil-based resources such as oil, gas, and fuel has positioned RES as a competitive alternative to conventional electricity generators [2]. While developed countries have expanded their installed RES capacity due to the decline in costs, economic and financial constraints have impeded this progress in developing countries, especially in Africa. As a result, the installed capacity of RES in Africa accounts for only about 1.97% of the global total [3].
Despite the potential for natural resources suitable for RES installations in African countries, limitations persist, particularly in securing adequate budgets for these projects [4,5,6,7,8][4][5][6][7][8]. Enhancing the techno-economic feasibility (TEF) of RES projects in Africa is crucial. This improvement could contribute significantly to addressing the electricity deficit in a continent that harbors over half of the global population without access to electricity, according to the World Bank [9]. It would assist these regions in achieving universal electrical supply goals, reducing the reliance of remote areas on diesel generators (DGs) for electricity production, curbing harmful emissions, and promoting energy generation through sustainable technologies [10].
Hence, harnessing the region’s two most abundant and cost-effective renewable energy resources, namely, solar and wind, has become imperative for African countries. Additionally, optimizing the electrical consumption of local communities is essential to ensure a cost-effective electrical supply [11,12][11][12]. Solar and wind, as renewable sources, can complement each other to some extent. Wind energy, for example, can compensate for the lack of daylight in electricity production [13]. However, their availability may only sometimes be sufficient to meet the electricity demand. Therefore, their utilization often requires support from other systems, commonly a storage bank and/or a diesel generator. While electrochemical storage is typically preferred over diesel generators to increase renewable energy penetration and avoid greenhouse gas (GHG) emissions [14], it remains a costly component of hybrid renewable energy systems. Oversizing electrochemical storage significantly impacts the net present cost (NPC) and the cost of energy (COE) of the entire system [15].
The International Renewable Energy Agency (IRENA) posits that electrical production from renewable sources in Africa can competitively meet the growing energy consumption, contributing to energy security and effective participation in the global energy transition [16]. This is particularly crucial for continents where attracting sufficient finance for renewable energy investments poses a significant challenge [17]. In Africa, electrical supply ranks among the top five priorities for local development, as identified by the African Development Bank (AfDB). Nevertheless, the lack of funding and investments in renewable energy projects has constrained the economic feasibility of RES, as evident in various studies [17,18][17][18].
Hafner et al. [19] highlight that the estimated potential for solar and wind energy in Africa is 1000 GW and 110 GW, respectively, according to the AfDB. However, the continent lags significantly in the policy and finance aspects of renewable energies, with only USD 8 billion invested annually. As a result, Africa is far from meeting the required annual investment budget of USD 70 billion by 2030 for the electricity sector and energy transition to achieve global objectives. Encouraging private investments in the green energy sector and securing support from international financial institutions becomes essential to facilitate this energy transition while ensuring an affordable COE for users [20].
Edward et al.’s work [10] identifies insufficient funding as hindering the energy transition. Despite increasing global financing to combat climate change, funding for renewable energy projects remains inadequate for the African continent [17,21][17][21]. Closing this financial gap requires adapting investment policies and streamlining administrative procedures, as local governments encourage the private sector to cover only 10% of energy investments in Africa [22]. Moreover, improved co-operation between African countries is essential to reduce political barriers and expedite the energy transition [23]. Osiolo [3] underscores that despite the remarkable growth in renewable energy investments from USD 45.2 billion in 2004 to USD 303.5 billion in 2020 and the promising returns expected from investments in the electrical production sector using renewable sources in Africa, the continent still faces challenges due to the high initial cost of installing such systems [23,24][23][24]. These initial costs can be mitigated at the expense of comfort in some remote regions where economic feasibility precedes technical considerations [25].
According to the International Energy Agency (IEA), the building sector consumes 40% of globally produced energy [23]. Commercial and service institutions, particularly heating, cooling, and lighting systems, are significant consumers of energy budgets. State coverage for non-profit institutions, such as public schools and hospitals, underscores the need to rationalize energy consumption and reduce costs, aligning with global energy-saving plans [26,27][26][27]. The integration of control systems, especially those based on artificial intelligence (AI), plays a pivotal role in managing electrical load demand, correlating it with energy production, and optimizing the temporal cost of energy (COE) [28]. The optimal integration of solar (thermal or photovoltaic (PV)) and wind systems, coupled with storage systems, reduces the dependence on conventional energy sources [29]. Implementing energy management techniques in renewable energy systems (RES) enhances microgrids’ energy efficiency and stability, reducing peak load and energy costs by aligning consumption patterns with RES production [30,31][30][31].

2. Bayesian Inference-Based Energy Management Strategy for Techno-Economic Optimization of a Hybrid Microgrid

Integrating energy management techniques into renewable energy systems (RESs) improves microgrids’ energy efficiency and stability. This implementation reduces peak loads and energy costs by aligning consumption patterns with producing renewable energy sources. Recent works, such as that of Babaei et al. [32], emphasize the critical role of demand management (DM) in ensuring power system reliability for standalone buildings, particularly in remote areas reliant on diesel generators (DGs) for electricity [32,33][32][33]. Therefore, the transition to PV–wind hybrid systems, proven to be the best alternative for electrical production in such areas, should be accompanied by DM systems [34,35][34][35]. This process has significantly improved unsupplied load and extra power generation by 78% and 61%, respectively [36]. Combining microgrids with DM systems operating in grid and off-grid modes has demonstrated advantages in numerous residential and commercial applications [37,38][37][38]. DM systems aim to reduce consumption during peak hours, increase the penetration of renewable energies, and limit the dependence on grid purchases. Applying load planning mechanisms has effectively managed the demand for residential loads. Additionally, swarm-based intelligent methods, as demonstrated by Feng et al. [39], prove effective in solving engineering problems. Abdelrahman et al.’s literature review [40] emphasizes the advisability of using RESs in microgrids. Therefore, whether residential or commercial, DM for these microgrids is crucial for transforming them into smart microgrids (SMG), monitoring the building’s electrical consumption and daily operations, and adapting them to electrical production. A recent study by Rona et al. comprehensively examined energy management systems, focusing on distributed generation, control methods, and microgrid configurations for reliable hybrid renewable energy. It also highlighted the need for robust optimization algorithms, hardware-in-the-loop validation, and data privacy in networked microgrids for future research in energy management [41]. The case study conducted by Ma and Yuan explored the integration of hybrid renewable energy systems in green buildings, focusing on the optimal design and comparison of photovoltaic panels with two storage systems: PV/battery and off-grid PV/hydrogen. Using particle swarm optimization (PSO) in MATLAB, it focuses on designing the system for minimum total annual cost (TAC) and maximum reliability [42]. Load uncertainties pose a significant challenge to the scheduling of microgrids, affecting their operation to improve energy accessibility and efficiency at a low cost. Hence, it is essential to consider these uncertainties in RES smart microgrids [43,44][43][44]. Mansouri et al.’s work [45] addresses uncertainties in load scheduling, achieving a robust schedule in the face of consumption fluctuations. Various methods showed a 5.65% decrease in operating costs, significantly increasing the consumer comfort index. Automatic switching further reduced the total cost of operation by 9.71% and increased the consumer comfort index by approximately 0.2%. Considering the stochastic availability of renewable resources, Komala [46] highlights the challenge of balancing electrical production from standalone RES and load demand. Electrochemical storage was identified as a facilitator in managing such electrical systems. Saffar and Ahmad’s work [47] proposes a new energy management system that achieved a 65% reduction in operating costs and a 96% reduction in unsupplied energy using dump load and electric vehicles. Integrated programming models in autonomous RES, as developed by Kiptoo et al. [48], demonstrated the optimization of microgrids under different DM strategies. Wang et al. [49] proposed a DM method based on the produced power of a grid-connected RES equipped with electrochemical storage, considering the uncertainties of renewable resources. Sarker et al. [50] used a swarm-based algorithm in MATLAB to reduce household electricity expenses by shifting loads to periods of lower electricity prices, maximizing energy consumption from renewable sources. Vincent et al.’s [51] numerical results showed that using a prediction corrector model and Markov chains with local scaling improves the satisfaction rate, particularly for microgrids with low storage capacity. Their proposed model, especially when using 100 kWh storage, is 2.8% more cost-effective, confirming that batteries mitigate the poor performance of baseline prediction models. DM and technical–economic optimization strategies of RESs must be precise and adapted to the site characteristics where the microgrid is installed. Artificial intelligence (AI) methods such as genetic algorithms and neural networks, as developed by Leonori et al. [52[52][53],53], can be utilized to test and validate these two aspects. The actual reduction in the cost of RES components is expected to make the COE produced by competitive renewable resources even lower than that produced by conventional fossil fuels shortly [54]. In the United States, the COE generated by photovoltaics has declined to less than 2.5 CAD/kWh, reaching a world-record low of 2.175 CAD/kWh in Idaho, with an anticipated 66% drop by 2040 [55]. Terrestrial wind turbines (WTs) in the same country have a COE not exceeding 2 CAD/kWh, which is expected to decrease by 47% by 2040 for WTs [56]. However, despite the 85% decrease recorded between 2010 and 2018 in the COE produced by lithium-ion (Li-Ion) batteries, reaching 18.7 CAD/kWh [57], electrical storage remains more expensive than other energy sources in RES. Still, it is a barrier to realizing RESs [58]. Some European governments financially support electrical storage for RES, leading to the installation of batteries in 40% of small-scale PV systems in Germany. In remote areas, batteries provide various services at a competitive cost, with further cost reductions expected for high-performance technologies like Li-ion batteries, projected to drop by 54% by 2030 [59]. Pascual et al.’s actual conditions test [60] of the DM strategy resulted in a 25% reduction in battery capacity without affecting power availability and microgrid stability, significantly reducing NPC and COE [61].

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