Renewable integration is the process of plugging renewable energy sources into the electric grid. Renewable sources generate energy from self-replenishing resources like wind, sunshine, and water and could provide enough energy to power a clean future. These energy sources are very different from fossil-based energy sources, which can create challenges when integrating renewable energy with the grid. Overcoming these challenges is key to increasing renewable integration and achieving a clean energy future. The energy transition is changing the landscape of electricity generation. As decarbonisation drives renewable energy demand, renewable energies are expected to account for 45 to 50 per cent of the global power supply by 2030 and 60 to 70 per cent by 2040. For instance, in 2023, clean energy resources provided about 41% of electricity in the United States. Sources of renewable energy, such as water and geothermal, can generate steady and consistent energy to meet base load power needs, which is the minimum amount of power the grid needs at any given time. These renewables function similarly to fossil fuel plants, which can provide reliable power to meet changing customer demands.
One of the primary hurdles is the inherent intermittency and variability of renewable energy sources like solar and wind power. Unlike traditional fossil fuel-based power plants, these sources cannot be easily controlled to match fluctuating energy demand. This variability can lead to grid instability, as sudden fluctuations in power output can disrupt the delicate balance between supply and demand. Another challenge is the limited capacity of existing urban grids to accommodate the bidirectional flow of electricity generated by distributed renewable energy sources. Traditional grids were designed for unidirectional power flow from centralised power plants to consumers. Integrating rooftop solar panels, community solar farms, and other distributed generation systems can overload distribution networks and cause voltage fluctuations.
The uncertainty and variability of wind and solar generation in urban areas can pose challenges for grid operators in urban areas. Variability in generation sources can require additional actions to balance the system. Greater flexibility in the system may be needed to accommodate supply-side variability and the relationship to generation levels and loads. Sometimes wind generation will increase as load increases, but in cases where renewable generation increases when load levels fall (or vice versa), additional actions to balance the system are needed. System operators must ensure sufficient resources to accommodate significant up or down ramps in wind generation to maintain system balance. Another challenge occurs when wind or solar generation is available during low load levels; in some cases, conventional generators may need to turn down to their minimum generation levels. Utilising all of the wind energy would require conventional generators to meet the net load, defined as the demand minus the wind energy.
In contrast to wind, solar generation often coincides more with load. However, in regions with evening load peaks, loss of solar generation at sunset can exacerbate ramping needs to meet the evening demand. Extreme event analysis in the Western Wind and Solar Integration Study Phase 2 (WWSIS-2), which examined up to 33% of renewable energy penetrations, showed that sunrise and sunset events dominate ramping needs. However, these events can be anticipated because we know when the sun will rise and set each day. Because it is possible to plan for this aspect of solar power variability, increased operating reserve levels need to focus only on the unpredictable cloud variability, which is reduced by the aggregation of geographically diverse solar power plants (as well as aggregation with wind and load variability). As a result, WWSIS-2 found that operating reserves were lower for the high solar scenario (25% solar) than for the high wind scenario (25% wind).
INNOVATIONS
- Smart grid technology: Grid Automation & Control can use AI and machine learning to predict renewable energy fluctuations and maintain grid stability with minimal human intervention. Smart grids integrate digital communication and automation technologies to improve the efficiency and reliability of electricity distribution. They use Advanced Metering Infrastructure (AMI) to collect real-time data, enabling better energy management and optimizing grid operations. Demand Response (DR) adjusts energy consumption based on supply availability, helping balance intermittent renewable energy generation like solar and wind.
- Energy storage solutions: Energy storage is crucial for supplying excess renewable energy during high-demand or low-generation periods. Batteries (e.g., lithium-ion) store energy for later use and are commonly deployed in grid-scale and residential applications.
- Decentralised and Distributed Energy Resources (DERs): DERs involve localised energy generation that reduces dependency on centralised power sources. Solar PV on Buildings allows consumers to generate renewable energy on-site, contributing to grid stability while reducing energy costs. Excess solar energy can also be fed back into the grid, benefiting both the user and the grid. Wind Energy through small urban turbines can also contribute to decentralised generation, though factors like urban wind patterns limit it.
- Flexible Grid Infrastructure: Modernizing grid infrastructure is essential for accommodating renewable energy, often requiring bidirectional energy flow. Grid Modernisation upgrades power lines, transformers, and substations to handle the complexities of distributed renewable energy generation, improving efficiency and flexibility. Interconnection and Regional Coordination allow multiple regions to share renewable energy, balancing supply and demand more effectively.
- Energy Management Systems (EMS): Energy Management Systems (EMS) use advanced software and AI to optimise energy distribution and consumption across renewable sources. AI-powered Energy Optimisation forecasts renewable generation and adjusts energy dispatch accordingly, minimising reliance on fossil fuels. Predictive Analytics anticipates energy generation and demand fluctuations, enabling grid operators to manage energy storage and distribution better.
- Building Energy Efficiency: Improving building energy efficiency reduces overall grid demand, allowing more renewable energy to meet consumption needs. Smart Buildings use sensors and automation to optimise energy use, adjusting heating, cooling, and lighting based on occupancy and weather conditions. Energy Recovery Systems capture waste heat or excess energy from industrial or residential processes for reuse.
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