Assessing the performance of demand-side strategies and renewables : cost and energy implications for the residential sector
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Many public and private entities have heavily invested in efficiency measures and renewable sources to generate energy savings and reduce fossil fuel consumption. Private utilities have invested over $4 billion in energy efficiency with 56% of these investments directed towards consumer incentives. However, the magnitude of the expected savings and the effectiveness of the technological measures remain uncertain. Multiple studies attribute the reasons driving these uncertainties to behavioral phenomena such as “the rebound effect.” This work provides insights on the uncertainties generating potential differences between expected and observed performances of demand-side measures (DSM) and distributed generation strategies, using mixed methods that employ both empirical analyses and engineering economics. This study also provides guidelines to stakeholders to effectively use the benefits from DSM strategies towards asset preservation for affordable multifamily houses. Section 2 describes how joint efficiency gains compare to similar singular efficiency gains for single-family households and discusses the implications of these differences. This work provides empirical models of marginal technical change for multiple residential electricity end-uses, including space conditioning technologies, appliances, devices, and electric vehicles. Results indicate that the relative household level of technological sophistication significantly influences the performance of demand-side measures, particularly the presence of a programmable thermostat. As to space conditioning, results demonstrate that sufficient consistent technical improvement leads to net energy savings, which could be due to technical factors or to a declining marginal rebound effect. Section 3 empirically evaluates the performance of distributed residential photovoltaic (PV) solar panels and identifies the technological and demographic factors influencing PV performance and adoption choice. Results show that modeling PV adoption choice significantly impacts the household energy demand, suggesting that the differences in the actual evaluated behavioral responses and the self-reported changes in electricity consumption are more complex than assumed by other studies. The analysis indicates that electricity use decreases marginally for PV adopters if sufficient efficiency improvements in space conditioning are made. Results further imply that households that adopt solar panels might “take back” roughly 24% of the annual electricity production for PV technologies. Section 4 describes replicable engineering economic models for estimating conventional rehabilitation, energy, and water retrofit costs for low-income multi-family housing units. The purpose of this study is to prioritize policy interventions aimed at maintaining property location and use, and to identify the capital investment needs that could be partially provided by local and state housing authorities. Section 5 synthesizes the work, describes the future work, provides guidelines for local and state efficiency program administrators, and insights on prioritizing and designing efficiency interventions.