Flexible demand-side management strategies in solar intermittency mitigation

Abstract

The acceleration in globalisation has increased international trade and boosted the global economy. With economic developments, countries are producing more goods and services, and with increased purchasing power, consumers can better enjoy these goods and services. With this, an upwards trend in energy demand can be observed globally. Together with global warming, the increase in energy demand, sparked the interest for rapid developments in the renewable energy industry. Solar energy, being one of the more prominent and viable renewable energy sources, has been widely adopted across the world. However, solar energy is extremely reliant on weather and climate conditions. Due to the drop in generated power during bad weather conditions, hybrid energy storage systems need to deliver the energy back to the grid. Demand side management strategies such as peak shaving can be applied to reduce peak energy usage, saving money for consumers. Demand response programs can be used to enforce peak shaving during a fixed period; therefore, they can be applied to further reduce the load demand during periods of solar intermittencies. The effectiveness of peak shaving and demand response programs were evaluated with the aid of HOMER Grid. Both programs were applied to a model configured to replicate a typical commercial building’s load profile. Incentive plans developed to increase the adoption rate of renewable sources were also applied to the model to determine the most economically viable incentive plan. It can be concluded from the simulation results that the larger the size of the alternate energy sources such as generators, solar photovoltaics infrastructure, and batteries, the lower the overall system costs. However, this has a point of diminishing return. As such, with proper considerations taken regarding the size of the alternate energy sources, both the peak shaving and demand response programs can be economically beneficial. With the simulation results, the most economically viable incentive plan could also be determined.