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According to the International Energy Agency, energy efficiency programs make up 72% of global greenhouse gas abatement strategies. However, there is extensive literature that shows compelling evidence for an “energy efficiency gap” in which expected energy savings from energy efficiency programs are not realized. Due to the importance of energy efficiency in global climate mitigation, as well as the significant federal, state, and local budgets for energy efficiency, there is a clear need for further research in this domain to evaluate the energy efficiency gap and prioritize methods for reducing the gap. Further, there is significantly less research on the gap as it applies to commercial buildings; the majority of research does not take advantage of advancements in available statistical modeling techniques; and there is very limited research evaluating the gap as it applies to the new field of fault detection and diagnostics (FDD). With FDD, building owners are able to closely monitor on an ongoing basis any faults that begin to occur in a commercial building that can waste energy and lead to the gap in energy efficiency. However, there has been very little research evaluating these systems in real buildings and calculating the energy efficiency impact. This thesis proposes and tests a modeling approach using novel machine learning algorithms to estimate counterfactual energy usage in real buildings and calculate the energy efficiency savings associated with an existing FDD system.
In this thesis, I propose a modeling technique using novel machine learning algorithms to estimate counterfactual energy usage of commercial buildings. I take advantage of high-frequency 15-minute interval electricity, chilled water, and steam energy usage data over several years in four campus buildings. I then compare the accuracy of these models applied to brand-new data using three different machine learning modeling techniques, the Lasso Model, Ridge Regression, and an Elastic Net Model. Finally, I applied these models to 8 time periods in which the existing FDD system identified a fault, thus isolating the energy impact of the fault. With this approach, I found that each of the three modeling techniques outperformed the other two techniques in at least one of the models, indicating that there is likely a benefit from using three approaches in building energy modeling. Further, I found that the models are likely able to isolate the energy increase associated with these faults, with some models yielding a higher confidence level than others. In addition to the overall average increase in energy, the faults showed consistent results in the daily load profile shifts after the fault occurred. Overall, the faults yielded monthly energy cost increases of $800-$1600 each. This methodology could therefore be used in more buildings and with different types of fault detection diagnostics systems to better evaluate the benefits of FDD software across applications. By using this method more extensively, we can better inform policy that can in turn aim reduce the energy efficiency gap in commercial buildings.
We raise the question if improvements to current energy-only markets are sufficient to maintain resource adequacy in electricity markets or whether the rapid increase in wind and solar power gives stronger arguments for additional capacity mechanisms. A comparative analysis between Europe and the United States reveals some fundamental differences, but also many similarities in electricity market design on the two continents. We provide a list of general and specific recommendations for improved electricity markets and argue that lessons can and should be learned in both directions. The key to achieve a market-compatible integration of renewable energy is to focus on correct price formation in the short-term. Increased demand-side participation, improved pricing during scarcity conditions, and a transition from technology-specific subsidies of renewables towards adequate pricing of carbon emissions are important measures towards this end. In contrast, an increasing reliance on administrative capacity mechanisms would bring the industry back towards the centralized integrated resource planning that prevailed at the outset of electricity restructuring more than 25 years ago.
Keywords: Electricity Market Design, Resource Adequacy, Renewable Electricity Generation, Europe, United States, Price Formation, Energy-Only Markets, Capacity Mechanisms.
This paper addresses the implications of the emergence of distributed energy resources (DERs) for industry structure in the electric power sector. Regulations on industry structures dictate which actors can perform which roles in the power sector and play a key role in enabling or preventing efficient power sector planning, investment, and operation. However, the structures in place today were designed in an era characterized by centralized resources, unidirectional power flows, and relatively price inelastic demand. In light of the decentralization of the power sector, regulators and policy makers must carefully reconsider how industry structure at the distribution level affects system planning, coordination, and operation, as well as competition, market development, and cost efficiency. To address this critical issue, we analyze the economic characteristics of the actors necessary for efficient and reliable distribution system planning, investment, and operation: distribution network owners and operators, DER owners, aggregators and retailers, and data managers. We translate the foundational theories in industrial organization and the lessons learned during the previous wave of power system restructuring to the modern context in order to analyze the implications of these characteristics on the potential for competition in the roles of DER ownership and aggregation. This analysis provides deep insight into questions such as whether or not monopoly distribution utilities should be allowed to own distributed resources. We then analyze how the mechanisms for coordinating vertically and horizontally disaggregated actors need to be updated, focusing on the need to improve the price signals present at the distribution level. We argue that the price signals governing transactions at the distribution level must increasingly internalize the cost of network externalities, revealing the marginal cost or benefit of an actor’s decisions. This will require a dramatic rethinking of electricity tariffs.
In order to reduce the influence of corruption on electricity sector performance, most Sub-Saharan African countries have implemented power sector reforms. However, after nearly two and half decades of reforms, there is no evidence whether these reforms have mitigated or exacerbated corruption. Neither is there evidence of performance improvements of reforms in terms of technical, economic or welfare impact. This paper aims to fill this gap. We use a dynamic panel estimator with a novel panel data set of 47 Sub-Saharan African countries from 2002 to 2013. We analyse the impact of corruption and two key aspects of electricity reform model - creations of independent regulatory agencies and private sector participation - on three performance indicators: technical efficiency, access to electricity and income. We find that corruption can significantly reduce technical efficiency of the sector and constrain the efforts to increase access to electricity and national income. However, these adverse effects are reduced where independent regulatory agencies are established and privatisation is implemented. Our results suggest that well-designed reforms not only boost economic performance of the sector directly, but also indirectly reduce the negative effects of macro level institutional deficiencies such as corruption on micro- and macro-level indicators of performance.
Keywords: Panel data, dynamic GMM, electricity sector reform, corruption, Sub-Saharan Africa.
JEL classification: Q48, D02, K23, D73
Nico Keyaerts, Michelle Hallack, Jean-Michel Glachant and William D'haeseleer, September 2010
This paper analyses the value and cost of line-pack flexibility in liberalized gas markets through the examination of the techno-economic characteristics of gas transport pipelines and the trade-offs between the different ways to use the infrastructure: transport and flexibility. Line-pack flexibility is becoming increasingly important as a tool to balance gas supply and demand over different periods. In the European liberalized market context, a monopolist unbundled network operator offers regulated transport services and flexibility (balancing) services according to the network code and the balancing rules. Therefore, gas policy makers should understand the role and consequences of line-pack regulation. The analysis shows that the line-pack flexibility service has an important economic value for the shippers and the TSO. Furthermore, the analysis identifies distorting effects in the gas market due to inadequate regulation of line-pack flexibility: by disregarding the fixed cost of the flexibility in the balancing rules, the overall efficiency of the gas system is decreased. Because a full market based approach to line-pack pricing is unlikely, a framework is presented to calculate a cost reflective price for pipeline flexibility based on the trade-offs and opportunity costs between the right to use the line-pack flexibility and the provision of transport services.