Residential Demand Response has shown promising results in smart grid applications. It can be achieved manually or autonomously. The variety of algorithms applied to achieve autonomous Demand Response have lacked a common baseline. The selection of testing environment is typically skewed by the desire to find one in which a particular algorithm performs well. This work evaluates several algorithms in a common evaluation environment. Which has been designed to encompass the range of conditions in a typical Residential Demand Response application. The environments described exhibit the following characteristics: non-stationary, dynamic, multi-actor, multi-objective. These characteristics will then be used to provide heuristics for algorithm selection. The algorithms used were selected to cover the spectrum of possible approaches to Demand Response. Some are centralised, others distributed. There are collaborative approaches and non-collaborative ones. Some are learning based, others require no training. This work provides criteria for which particular algorithms should be applied to a given application.