Intermittency and the Value of Renewable Energy

A key problem with renewable energy is intermittency. This paper develops a method to quantify the social costs of large-scale renewable energy generation. The method is based on a theoretical model of electricity system operations that allows for endogenous choices of generation capacity investment, reserve operations, and demand-side management. We estimate the model using generator characteristics, solar output, electricity demand, and weather forecasts for an electric utility in southeastern Arizona. The estimated welfare loss associated with a 20% solar photovoltaic mandate is 11% higher than the average cost difference between solar generation and natural gas generation. Unforecastable intermittency yields welfare loss equal to 3% of the average cost of solar. Eliminating a mandate provision requiring a minimum percentage of distributed solar generation increases welfare. With a $21/ton social cost of CO2 this mandate is welfare neutral if solar capacity costs decrease by 65%.

Sustainable generation mix as a reference in effective design of electricity market structures and rules

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Data-Driven Test Cases for Sustainability Assessment of Smart Grid Initiatives in Organized Electricity Markets

The primary aim of this dissertation is to deliver a technique to augment power system test cases with realistic open-source data to represent a deregulated power system. These test cases are intended to be used by power system researchers who require a test case that is capable of performing economic and environmental analysis on a bulk-power level. These test cases are capable of estimating the cost of bulk-energy for economic analysis and harmful greenhouse gas (GHG) and air polluting (AP) emissions for environmental sustainability analysis. These cases are developed for simulations that are intended to be at the transmission level where the independent system operator (ISO) has control. In the second part of this dissertation, an aggregator based demand response (DR) model is studied as-a-service to the bulk-power market, and its economic benefit is estimated using the augmented test cases. The augmentation technique presented in this dissertation has three-layer data over the existing generator information in a test case. The first layer of augmented data replaces the cost functions of the test case generators with functions developed based on the generator offers from a real electricity market. An unsupervised learning technique had to be implemented to classify the market offer data because the identity of the generators is masked to honor a fair market policy. The offer data was converted to cost functions and is sampled statistically such that the test cases represent a similar generator supply curve as the real power system. In addition to the cost functions layer, the test case generator data has an augmented generator fuel-turbine data. This data in a test case will represent the energy sources and generator technology of the system that the test case is intended to emulate. The hourly energy mix of the electricity market is utilized to augment the generator fuel-turbine type of test case generators. Because the number and capacities of test case generators may not represent the real system, assigning one fuel-turbine type to one test case generator will not result in a right energy mix. The augmentation technique creates an additional layer of information for each test case generator which can represent multiple fuel-types. The third layer of augmented data on test cases contains the heat curve and emission information. With all these layers of data, the test case is capable of representing the dynamic cost nature of a deregulated power system and is able to dispatch generators similar to the real power system. PJM interconnection data was chosen to implement the proposed augmentation technique. The marginal cost result from optimal power flow (OPF) is compared with the marginal cost of energy of the PJM interconnection along with the GHG and AP emissions. Smart-grids have opened opportunities for end customers to participate in the power system operation. DR is one of the activities that the end customers can perform to participate in the electricity market. Revenue earned from energy markets has been relatively low compared to DR used for capacity markets and ancillary services. An aggregated DR model participating in the bulk-power market as a service through a pool-based entity called demand response exchange (DRX) is proposed to improve the benefits of DR to the market. The economic benefits to the market entities have been studied using the proposed augmented test cases. The key contributions of this dissertation are: power systems test case generator data for researchers who do not have access to the real power system data, a technique that utilizes only open-source data to develop augmented data for any test case to represent the dispatch of a real power system in terms of cost, and emissions, a DR model capable of improving the revenue for DR participants in the bulk-energy market

Market and Economic Modelling of the Intelligent Grid: End of Year Report 2009

The overall goal of Project 2 has been to provide a comprehensive understanding of the impacts of distributed energy (DG) on the Australian Electricity System. The research team at the UQ Energy Economics and Management Group (EEMG) has constructed a variety of sophisticated models to analyse the various impacts of significant increases in DG. These models stress that the spatial configuration of the grid really matters - this has tended to be neglected in economic discussions of the costs of DG relative to conventional, centralized power generation. The modelling also makes it clear that efficient storage systems will often be critical in solving transient stability problems on the grid as we move to the greater provision of renewable DG. We show that DG can help to defer of transmission investments in certain conditions. The existing grid structure was constructed with different priorities in mind and we show that its replacement can come at a prohibitive cost unless the capability of the local grid to accommodate DG is assessed very carefully.Distributed Generation. Energy Economics, Electricity Markets, Renewable Energy

Framework for Electric Vehicles and Photovoltaic Synergies

Historically road transport has been exclusively dominated by petrol and diesel engines. Both alternatives are proved to be unsustainable due to their environmental impacts and the limited nature of their primary resources. Today’s transportation sector in the European Union (EU) accounts for 23% of CO2 emissions, 72% of which is being emitted by road transport. The European Union’s CO2 emission regulation for new cars, has come as a response to set emission performance limits for new passenger cars with the goal of establishing a road map change for automotive sector. Furthermore, the EU has set challenging targets to reduce greenhouse gas emissions by 40% in 2030 (relative to emissions in 1990) and for energy consumed to be generated at least with 27% from renewable sources in 2030. As regards energy efficiency, the 2030 framework also indicated that the cost-effective delivery of the greenhouse gas emissions reduction target for 2030 would require increased energy savings of the order of 27%. The renewable energy directive particularly identified: technological innovation, energy efficiency and contribution of renewable energy sources in transport sector as one of the most effective tools in reaching the expected targets in terms of sustainability and security of the supply. In such context it is obvious that reaching these challenges will be certainly depending on the rollout of Electric Vehicles (EV) as a mean of sustainable transport, higher penetration of distributed renewable energy sources. One consequential challenge will consist in accommodating such paradigm in the most cost-efficient fashion through active involvement of customer and better flexibility of the demand. This report highlights the current trends and expected evolution in the EU in term of electromobility, Photovoltaic (PV) systems and smart grids, with the aim of identifying mutual synergies aiming at enabling: energy efficiency, sustainable transport and higher share of renewable energy sources in the final energy mix. A technical conceptual architecture for integration of EV facilities and distributed generation sources in the context of smart grid is proposed to identify the predictable penetration limits of PV systems and EV users.JRC.F.3-Energy Security, Systems and Marke

Cloud Computing Strategies for Enhancing Smart Grid Performance in Developing Countries

In developing countries, the awareness and development of Smart Grids are in the introductory stage and the full realisation needs more time and effort. Besides, the partially introduced Smart Grids are inefficient, unreliable, and environmentally unfriendly. As the global economy crucially depends on energy sustainability, there is a requirement to revamp the existing energy systems. Hence, this research work aims at cost-effective optimisation and communication strategies for enhancing Smart Grid performance on Cloud platforms