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.