Renewable Energy Grid
Renewable Energy Grid
Case Study

Renewable Energy Grid

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Together with smart, more energy-efficient buildings, and dynamic adaptive meters, this urban infrastructure network integrates heating, gas, cooling, and electricity, and depends solely on renewable energy.
Together with smart, more energy-efficient buildings, and dynamic adaptive meters, this urban infrastructure network integrates heating, gas, cooling, and electricity, and depends solely on renewable energy.

Background

In combination with more energy-efficient buildings and dynamic adaptive meters and measurements, Renewable Energy Grids can supply power on-demand, allowing for increased cost-efficiency while minimizing wasted resources. The concept of smart electricity grids is comparable to smart thermal grids, in which both focus on the integration and efficient use of renewable energy, as well as the operation of a grid structure allowing for distributed power generation which may involve bidirectional interaction with consumers.

District Heating vs. Smart Thermal Grids

District heating, a type of thermal grid, is comprised of a network of pipes connecting the buildings in a neighborhood, or even entire cities in order to distribute heat-producing units. Current district heating systems rely on fossil fuels and are able to power buildings with high heat demands, however, existing district heating systems do not include renewable energy in their infrastructure or can simply be implemented in buildings with low heat demands.

The next generation of district heating, or commonly known as smart thermal grids, could integrate local fuel or heat resources that would otherwise be considered wasted heat, from waste-to-energy, industrial surplus heat, geothermal and solar thermal heat. Smart thermal grids take advantage of Smart Grid Architecture Model (SGAM) to create a more reliable energy system focused on optimizing energy conservation with the help of data. In the future, smart thermal grids could calculate the essential heating level needed in a certain room, which could be supplied by surplus heat from industrial and cooling processes in commercial buildings.

Emerging Technologies

Renewable Energy Grids need to balance the complexities of fluctuating renewable energy supply and fluctuations in demand, most critically at peak times. The Smart Grid Architecture Model (SGAM) is a complex System-of-Systems (SoS) that connects and manages the interactions between energy producers, distributors, and end consumers. SGAMs can supply a range of markets and consumers within a grid such as community and neighborhood-level systems, and can be fed into from a diverse range of renewable energy sources, from small-scale renewables to bulk generation of wind, solar, thermal, and hydropower. SGAM can also be used to model the complexities of energy usage and demand and identify further opportunities for standardization within the grid in order to further optimize the SGAM.

The primary benefits of such models lie in improving reliability, increasing grid capacity, and reducing costs for consumers as they will be able to interact directly with utility providers supplying the grid. By better managing the varying loads and peak conditions, which can put stress on network assets, equipment failures and power outages can be reduced. However, this technology is still in development stages and challenges still need to be overcome in transitioning from diverse, varied, and interconnected systems to such a complex scheme. Rather than operating in silos independently from one another, suppliers will need to cooperate to share storage, computing, and data for the effective and seamless operation of any SGAM.

Another emerging technology that may be relevant in the development of Renewable Energy Grids is a Machine Learning Weather Model. Improved and accurate weather forecasting can help the Renewable Energy Grid to anticipate and predict load throughout the grid, enabling it to assess risks to minimize disruption and make any necessary adjustments to maintain comfort zones for consumers. From storm events to changes in cloud cover or rainfall, the Renewable Energy Grid could use the model outputs to change energy supply in real-time, and even temperatures within district heating networks. Data from Machine Learning Weather Models could also help to contribute to a reduction in greenhouse gas emissions as a result of more efficient use of energy resources at a grid level.

Future Perspectives

In order to be feasible, future sustainable energy systems must be primarily focused on energy efficiency and energy conservation, specifically by improving the insulation of existing buildings and reducing grid losses. A transition from the current fossil fuel and nuclear-based energy systems into sustainable energy systems require large-scale integration of intermittent renewable energy. It entails fundamentally rethinking and redesigning the energy system itself to coordinate between many smart grid infrastructures including electricity grids, district heating, and cooling grids, gas grids, and other unique fuel infrastructures. Incorporating weather data will help Renewable Energy Grids anticipate and manage loads. As more assets connect to such grids, standardization and interoperability will become even more critical.

4 topics
Adapting to Climate Change
Basic Energy Services
Energy Efficiency
Renewable Energy
4 SDGs
07 Affordable and Clean Energy
09 Industry, innovation and infrastructure
11 Sustainable Cities and Communities
13 Climate Action

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2 technology domains
2 technology applications
1 stories
2 industries
  • Energy
  • Environment & Resources
4 topics
  • Adapting to Climate Change
  • Basic Energy Services
  • Energy Efficiency
  • Renewable Energy
4 SDGs
  • 07 Affordable and Clean Energy
  • 09 Industry, innovation and infrastructure
  • 11 Sustainable Cities and Communities
  • 13 Climate Action