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The growing demand for electricity, concerns over environmental emissions, the high risk of fossil fuel depletion, and the challenges of grid expansion to supply remote villages due to economic and technical constraints make renewable energy source (RES)-based microgrids (MGs) the most effective solution for electrifying both rural and urban areas. In addition to providing lighting, access to electricity plays a crucial role in the sustainable development of the country as a whole.
A microgrid (MG) is a small, active distribution system that incorporates RES3. MGs consist of components such as wind generation (WG), photovoltaic (PV) power, flexible loads, and an energy storage system (ESS) that serves as a buffer between electric demand and distributed generation. It is described as an aggregation of distributed energy sources, energy storage systems, and loads4. MGs also have the potential to provide electricity to areas affected by natural disruptive events, thereby enhancing the reliability and resilience of the system5.
MGs can operate in either standalone or grid-connected modes. In the event of a fault or other condition that interrupts power from the main grid, the MG disconnects and begins autonomous operation, supplying local loads. Once the fault is cleared, the isolated MG reconnects and resumes grid-connected operation. During autonomous operation, critical loads are prioritized, while other loads are supplied based on the availability of resources5. Critical loads refer to those that must remain uninterrupted under any circumstance.
Severe weather conditions can impact the power system, causing frequency deviations from the standard. While the extent of the impact varies depending on several factors, it is often difficult for the system to recover from such natural disasters. MGs have emerged as a promising source of resilience in such situations6.
Although increasing the penetration of RES into existing systems offers many benefits, it also introduces stability challenges. The intermittent nature of renewable resources (e.g., variations in solar radiation and wind speed) can lead to an imbalance between supply and demand, causing reliability issues, voltage fluctuations, and stability problems in the grid. Additionally, these fluctuations may result in power losses7. However, properly planned and developed MGs tend to have a more positive impact. According to a study in8, MGs can enhance system stability and reduce the levelized cost of energy (LCOE), although challenges remain due to the intermittent nature of RES.
The work presented in11 examines the impact of simultaneously considering battery size, cycle life, and technology in MGs during both standalone and grid-tied operations. It also highlights how the depth of discharge (DOD) affects the battery''s service life. The authors developed a model that quantifies the equivalent number of complete cycles a battery can undergo over its lifetime, illustrating the difference between a full cycle (complete discharge of a fully charged battery) and a partial cycle. Another study12 explores a different configuration of storage systems, including PV, diesel generators (DG), and a hybrid energy storage system (HESS), which combines batteries and supercapacitors (SCs). This arrangement improves the service life of the battery by incorporating SCs.
For rural areas where expanding the distribution network is either uneconomical or infeasible, renewable energy-based MGs offer a promising solution for providing electricity. Autonomous MGs with RES ensure system security and reliability while offering cost-effective solutions13.
Optimization involves achieving the best possible outcome while meeting specified targets, whether they are maximum or minimum. In the context of MG planning, optimization refers to determining the ideal size, location, and technology of MG components while adhering to various constraints such as investment costs, BESS lifecycle, reliability, greenhouse gas (GHG) emissions, and electricity prices. Researchers employ different optimization techniques based on the specific objectives and constraints of their projects.
In5, the MG components considered include PV, BESS, and loads. The study uses Genetic Algorithm (GA) to optimize the capacity of these components, select a charging strategy, and determine optimal locations while meeting requirements for minimum investment costs, maximum energy supply, and minimal reverse power flow (RPF). The research emphasizes the importance of ESS management and proper MG configuration to meet energy demand and optimize costs in autonomous MGs, which serve as backup power during extended disruptions.
For cost optimization of MGs, factors such as the initial state of charge (SoC), load profile, and operating costs of ESS and RESs are taken into account. By applying optimization techniques, it is possible to achieve efficient use of available resources and minimize operational costs15.
To determine the optimal configuration of hybrid energy sources, eight different MG energy source configurations are evaluated based on the cost of energy (COE) and annual net present cost (NPC), considering seasonally varying commercial and residential loads with different renewable energy fractions. The analysis finds that the PV + BESS configuration is the most economical compared to other simulated feasible combinations. The study also notes that the renewable penetration fraction is inversely related to COE and NPC, meaning that as the fraction of renewable sources decreases, COE increases.
During the planning phase, careful consideration should be given to accurately sizing MG components, assessing required network upgrades, and incorporating flexible components that impact cost optimization to reduce overall expenses. Various optimization techniques can be employed with different constraints to achieve this goal.
Optimization techniques focusing on capacity sizing and the optimal upgrade of generation resources are proposed in18. By considering the flexibility of different distributed assets during the planning phase, investment costs can be reduced by varying the operation scenarios. In this study, PVs and BESSs are used as generation sources, while Electric Vehicles (EVs) serve as flexible assets. The problem formulation includes storage, generation capacity, and network upgrades, and is solved using a mixed integer piecewise linear approach.
In4, the optimal sizing of a standalone MG, considering load variability, renewable sources, various battery types, and battery life cycle energy, and their impact on costs in the Indian context is analyzed. A multi-objective optimization method is presented, balancing cost-effectiveness and energy sustainability through the selection of different batteries. The MG in question includes WT, PVs, and BESS, and the Genetic Algorithm (GA) is used for optimal component sizing, factoring in environmental concerns, energy efficiency, and life cycle costs.
The study in12 proposes a PV model suited for areas without historical operational data, highlighting its impact on the MG sizing process and the inclusion of critical loads. The requirement for zero Energy Not Supplied (ENS) is balanced with economic considerations, and a sensitivity analysis based on an energy storage system is presented. In19, the environmental impact is taken into account in the optimization of microgrid component sizing, comparing the benefits of using either a grid-only system or a microgrid incorporating PV, WT, BESS, and DG to meet demand. The latter approach is found to be more economical.
The environmental and techno-economic impacts of island MGs, considering different levels of renewable energy penetration, are explored in14. The system analyzed includes PVs, BESS, a bio-gasifier, WT, DG, and loads, with the goal of minimizing the total number of components while prioritizing demand supply.
In20, a hybrid Grey Wolf with Cuckoo Search Optimization (GWCSO) method is applied to optimally size MG components at minimal cost. The simulation results show that GWCSO outperforms GA, Grey Wolf Optimization (GWO), Cuckoo Search Optimization (CSO), Particle Swarm Optimization (PSO), and Ant Lion Optimization (ALO) in terms of robustness, deviation, annual cost, LCOE, and component sizing. The study uses real-time economic models to optimize the dispatching of ESS, factoring in future operational profit and past acquisition costs. The Alternating Direction Multiplier Method (ADMM) is used to find the optimal solution.
The optimal sizing of photovoltaic, diesel, and battery components for island MGs, considering both economic and reliability factors, is analyzed in22. A tradeoff between economy and reliability is necessary, as achieving detailed reliability often incurs higher costs. The study concludes that larger-sized RES are needed to meet reliability criteria in MG planning. An autonomous MG with BESS, PVs, and DG is considered, with optimal sizing performed using the MILP algorithm.
Optimal sizing of storage systems plays a significant role in the economic performance of MG systems. ESS must be properly sized to avoid the high costs of oversizing or the reliability issues caused by undersizing. As discussed in10, key factors to consider in ESS optimization include energy storage configuration methods (single or composite), operational modes (standalone or grid-tied), optimization analysis techniques, and the impact on customers'' income and system economy. Harmonized planning of BESS and PVs with demand-side resources is also explored in the study.
In the context of MG planning, a low-carbon economy is becoming increasingly important. The double-layer optimization technique proposed in23 determines the optimal capacity and location of MG components while minimizing carbon emission costs. A carbon subsidy policy to encourage the use of clean energy (such as PV and wind) and a carbon tax system for carbon-emitting sources (such as diesel generators) are incorporated to address climate change. This optimization method significantly reduces carbon emissions and promotes the development of renewable energy sources.
A MG is a small-scale electrical grid consisting of distributed generation and loads. It can operate in either standalone mode or grid-connected mode. Standalone MGs function autonomously, isolated from the main grid, and typically include RES along with BESS and/or diesel or gas generators. Due to the intermittent nature of RES (such as solar radiation and wind speed), MGs are prone to issues like overloading and overgeneration.
Overloading occurs when the generated power is insufficient to meet the load demand, which can be mitigated by load shedding. Overgeneration, on the other hand, refers to a situation where power generation exceeds demand, and in such cases, curtailing non-dispatchable renewable sources can be a solution. Both overloading and overgeneration can impact the frequency and voltage stability of the MG.
Curtailment of renewable energy sources and load shedding are commonly used to address short-term overgeneration and overloading in standalone MGs. However, these solutions are not economical. An alternative is the use of energy storage systems, which can resolve these issues but come with high installation and operational costs. Therefore, finding an optimal solution to this problem is essential.
One such solution is to interconnect nearby MGs to allow power exchange between them. This configuration, where two or more neighboring MGs are interconnected, is known as a coupled microgrid (CMG) or microgrid cluster (MGC). In a CMG, each MG is responsible for meeting its own local load demand, but excess generation can be shared with other interconnected MGs in need. By sharing power within the cluster, both the curtailment of renewable sources and the need for load shedding are reduced, thereby enhancing the overall reliability of the system.
In24, MGs within a cluster are interconnected via a 3-phase AC link and a back-to-back converter. The effectiveness of the proposed power exchange and control strategy is demonstrated through simulation analysis using PSIM.
The interconnection of a commercial MG with a residential MG to form an MGC is discussed in8. These MGs experience different load patterns throughout the week: residential MGs typically see peak loads during weekends, while commercial MGs experience peak loads on weekdays. By leveraging the differing power requirements of residential and commercial loads, the surplus power from the commercial MG can be used to meet residential peak loads during weekends, thereby increasing the reliability of the cluster. As with any power system, maintaining voltage and frequency within standard limits requires balancing supply and demand within the cluster.
Figure 1(a) shows a standalone MG that includes PV, micro-hydro, batteries, and various types of loads, while Fig. 1(b) illustrates a MG cluster composed of three individual MGs.
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