Residential distributed generation : decision support software to evaluate opportunities in the residential market : a thesis submitted in partial fulfilment of the requirement for the degree of Masters of Engineering at Massey University, Palmerston North, New Zealand /
The residential market in New Zealand consumes a significant proportion of our electricity production and is one of the fastest growing sectors. As a vertically integrated generator retailer in the New Zealand electricity industry, Meridian Energy Ltd is concerned at retaining and growing their customer base. They recognise that utilisation of emerging distributed generation [DG] technologies can provide a competitive advantage in the market place. A decision tool was developed to help Meridian identify opportunities within the residential market for applications of DG. The model compares the cost to serve a household's energy needs using a business as usual case with a DG case on an annual basis for a single household or a neighbourhood. A modular approach was used for ease of development and to enable future enhancements. The main modules were: load profile development, DG technology, operation control, costing and a calculation engine. The load profile module estimated space heating/cooling, water heating and other electrical loads for each 30 minute period for 8 representative days of a year based on national end-use statistics and a set of 40 reference profiles. A Gamma distribution was used to simulate diversity between houses. The calculation engine computed the amount of demand that could be met by the DG technologies and hence the residual demand or surplus for export. The pricing module estimated the annual cost including aspects such as: capital cost, fuel cost, maintenance, value of export and cost of import. The technology modules allowed different DG technologies, as well as a range of parameters to be selected. It included renewable energy resource modelling. The performance module allowed different operation control of the heat engine technologies including: base load, electrical peaking, heat peaking, load following (heat-led) and load following(electricity-led). The model was implemented using Microsoft Visual Basic for Applications, in Excel. A series of user-forms were developed to enable the model to be run with a minimum of user input. Three case studies were undertaken. In the first, five technology types were modelled, with the heat pump and Stirling engine looking the most promising. The second case study involved these two technologies in a Christchurch urban area study. A hypothetical network analysis showed the benefit that these technologies could have in reducing peak loading on the network. The third case study examined the sensitivity of the results to the value of specific variables. Load size and capital cost had the strongest influence on NPV.