A demand response system for wind power integration: greenhouse gas mitigation and reduction of generator cycling

A smart grid power system for a small region consisting of 1,000 residential homes with electric heating appliances from the demand side, and a generic generation mix of nuclear, hydro, coal, gas and oil-based generators representing the supply side, is investigated using agent-based simulations. The simulation includes a transactive load control in a real-time pricing electricity market. The study investigates the impacts of adding wind power and demand response (DR) on both greenhouse gas (GHG) emissions and generator cycling requirements. The results demonstrate and quantify the effectiveness of DR in mitigating the variability of renewable generation. The extent to which greenhouse gas emissions can be mitigated is found to be highly dependent on the mix of generators and their operational capacity factors. It is expected that the effects of demand response on electricity use can reduce dependency on fossil fuel-based electricity generation. However, the anticipated mitigation of GHG emissions is found to dependent on the number and efficiency of fossil fuel generators, and especially on the capacity factor at which they operate. Therefore, if a generator (the marginal seller) is forced to use less efficient fossil fuel power generation schemes, it will result in higher GHG emissions. The simulations show that DR can yield a small reduction in GHG emissions, but also lead to a smaller increase in emissions in circumstances when, for example, a generator (the marginal seller) is forced to use less efficient fossil fuel power generation schemes. Nonetheless, DR is shown to enhance overall system operation, particularly by facilitating increased penetration of variable renewable electricity generation without jeopardizing grid operation reliability. DR reduces the amount of generator cycling by an increased order of magnitude, thereby reducing wear and tear, improving generator efficiency, and avoiding the need for additional operating reserves. The effectiveness of DR for these uses depends on the participation of responsive loads, and this study highlights the need to maintain a certain degree of diversity of loads to ensure they can provide adequate responsiveness to the changing grid conditions.