Adding renewables to the grid: Effects of Storage and Stochastic Forecasting

The electricity sector contributes to a quarter of global greenhouse emissions, and managing its evolution is a critical sustainability challenge. The context for the development and operation of electricity grids has dramatically changed in recent years. Wind and solar power have become much less expensive. Lower costs combined with increased policy action to address carbon emissions is leading to substantial shares of electricity generated by intermittent renewables. Maintaining a stable electricity supply with intermittency is a critical challenge; storage and natural gas are possible solutions. While policymakers promote storage as green grid technology, low-cost natural gas from hydrofracturing extraction raises the economic hurdle for storage. Researchers have developed complicated energy system models to help plan grids in the face of the above trends. The research in this dissertation introduces new modeling features that affect the economic and environmental outcomes of the adoption of renewable and storage technologies. First, prior models that explore the future build-out of electricity grids are nearly always deterministic, i.e., they assume that decision-makers have perfect information. Here a stochastic optimization grid expansion model is developed that presumes that expected future fluctuations, e.g. in fuel prices, influence build-out decisions. This stochastic model thus includes uncertainty and risk as core elements: Grid build-out depends on the distribution of system costs. A genetic algorithm with Monte-Carlo simulation is used for co-optimization using two objective functions: “risk-neutral,” which optimizes to minimize average system cost and “risk-averse,” which optimizes to minimize average of the top 5% of costs (also called 95% Conditional Value at Risk (CVaR)). This model is tested for the US Midwest regional grid. The results show that the risk-averse scenario does not increase mean system costs but adds significantly more wind. These results corroborate prior work showing that electricity system costs can be surprisingly inelastic to renewable adoption and further introduces quantification of how increased renewables lowers cost risk. Second, the economic and environmental performance of storage is complicated by how its introduction affects the operation of both renewable and fossil plants. In this dissertation, a model is developed that accounts for how storage operation would affect prices on the grid and in turn, the operational schedule that yields optimal revenue. Results from modeling the US Midwest region shows that this treatment of storage as a “price maker” affects results. The model indicates that storage increases carbon emissions when it enables a high emissions generator, such as a coal plant, to substitute for a cleaner plant, such as natural gas. In this case, low cost; efficient natural gas generation is relatively better than coal to realize emissions reductions with storage under economic arbitrage until renewables dominate the grid mix. Third, the operational strategies of energy storage alter the generation and profits of the other electricity generation systems. The operational effects of storage on the change in generation is investigated for all the eGRID subregions across the US based on actual historical electricity prices and the generation mix for the year 2016. Results show that storage increases the coal generation and affects the natural gas generation in the west – except in California and the Midwest regions of the US; and increases the generation of the natural gas in the eastern US regions. California, upstate New York and New England regions show an exception with an increase in natural gas generation and decrease in coal generation. The model also investigates the operational effects of storage on the profits of other generating units in California, Midwest and New York regions. Profits of other generating units are significantly affected when large capacities of storage operate as price-makers. Coal has a small increase in profits by 2% and all the other fuels continue to see a decline in profits in New York and the Midwest regions. The decrease in profits of the other generating units is because of the offset/retirements of the peaker natural gas plants that set the electricity prices. On the other hand, in California, the profits for renewables increase from the increase in electricity clearing prices set by the natural gas combined cycle plants to meet the additional demand from the storage charging

Four Essays on the Economics of Renewable Power Markets

In the scope of four related essays, this thesis analyzes potential pathways to a low-carbon European electricity supply with a large share of intermittent renewables. In particular, the thesis investigates the related costs of such a transition under different economic and technical developments as well as energy policies. For this purpose, several (stochastic) optimization models and a spatial equilibrium model have been developed. These model-based analyses yield, among others, the following findings: Under cost-efficient energy policies and favorable technical and economic developments, system costs and electricity prices may not increase significantly compared to today. However, this requires a Europe-wide common energy strategy, large amounts of usable land and an open mindset surrounding feasible technology options. Moreover, the integration of intermittent renewable generation remains technically challenging. Given a large share of wind and solar power generation, regional and intraregional weather conditions play an important role in renewable power markets. Thus, simulation models neglecting weather uncertainty, which are often used in practice, underestimate system costs substantially. Furthermore, weather uncertainty induces financial risks for conventional and renewable-based electricity producers. The effect on the financial risk for green electricity producers depends on the renewable promotion scheme and the slope of the power market's supply function. It is shown that feed-in tariffs (fixed-price compensation) do not necessarily offer producers the lowest variance in profits. Moreover, the analysis shows that renewable policies not including the hourly wholesale price set inefficient incentives. For the particular case of concentrating solar plants, flat feed-in tariffs set an inefficient incentive to invest in integrated thermal energy storages in today's electricity market. The analyses in this thesis are carried out for the European electricity system. However, the results may be useful for the assessment of a transition to a low-carbon and mostly renewable-based electricity system in other regions as well

The costs of electricity systems with a high share of fluctuating renewables - a stochastic investment and dispatch optimization model for Europe

Renewable energies are meant to produce a large share of the future electricity demand. However, the availability of wind and solar power depends on local weather conditions and therefore weather characteristics must be considered when optimizing the future electricity mix. In this article we analyze the impact of the stochastic availability of wind and solar energy on the cost-minimal power plant mix and the related total system costs. To determine optimal conventional, renewable and storage capacities for different shares of renewables, we apply a stochastic investment and dispatch optimization model to the European electricity market. The model considers stochastic feed-in structures and full load hours of wind and solar technologies and different correlations between regions and technologies. Key findings include the overestimation of fluctuating renewables and underestimation of total system costs compared to deterministic investment and dispatch models. Furthermore, solar technologies are - relative to wind turbines - underestimated when neglecting negative correlations between wind speeds and solar radiation.Stochastic programming; electricity; renewable energy

Energy management in microgrids with renewable energy sources: A literature review

Renewable energy sources have emerged as an alternative to meet the growing demand for energy, mitigate climate change, and contribute to sustainable development. The integration of these systems is carried out in a distributed manner via microgrid systems; this provides a set of technological solutions that allows information exchange between the consumers and the distributed generation centers, which implies that they need to be managed optimally. Energy management in microgrids is defined as an information and control system that provides the necessary functionality, which ensures that both the generation and distribution systems supply energy at minimal operational costs. This paper presents a literature review of energy management in microgrid systems using renewable energies, along with a comparative analysis of the different optimization objectives, constraints, solution approaches, and simulation tools applied to both the interconnected and isolated microgrids. To manage the intermittent nature of renewable energy, energy storage technology is considered to be an attractive option due to increased technological maturity, energy density, and capability of providing grid services such as frequency response. Finally, future directions on predictive modeling mainly for energy storage systems are also proposed

A Vision for Co-optimized T&D System Interaction with Renewables and Demand Response

The evolution of the power system to the reliable, efficient and sustainable system of the future will involve development of both demand- and supply-side technology and operations. The use of demand response to counterbalance the intermittency of renewable generation brings the consumer into the spotlight. Though individual consumers are interconnected at the low-voltage distribution system, these resources are typically modeled as variables at the transmission network level. In this paper, a vision for co-optimized interaction of distribution systems, or microgrids, with the high-voltage transmission system is described. In this framework, microgrids encompass consumers, distributed renewables and storage. The energy management system of the microgrid can also sell (buy) excess (necessary) energy from the transmission system. Preliminary work explores price mechanisms to manage the microgrid and its interactions with the transmission system

An economic evaluation of the potential for distributed energy in Australia

Australia’s Commonwealth Scientific and Industrial Research Organisation (CSIRO) recently completed a major study investigating the value of distributed energy (DE; collectively demand management, energy efficiency and distributed generation) technologies for reducing greenhouse gas emissions from Australia’s energy sector (CSIRO, 2009). This comprehensive report covered potential economic, environmental, technical, social, policy and regulatory impacts that could result from the wide scale adoption of these technologies. In this paper we highlight the economic findings from the study. Partial Equilibrium modeling of the stationary and transport sectors found that Australia could achieve a present value welfare gain of around $130 billion when operating under a 450 ppm carbon reduction trajectory through to 2050. Modeling also suggests that reduced volatility in the spot market could decrease average prices by up to 12% in 2030 and 65% in 2050 by using local resources to better cater for an evolving supply-demand imbalance. Further modeling suggests that even a small amount of distributed generation located within a distribution network has the potential to significantly alter electricity prices by changing the merit order of dispatch in an electricity spot market. Changes to the dispatch relative to a base case can have both positive and negative effects on network losses.Distributed energy; Economic modeling; Carbon price; Electricity markets

Addressing flexibility in energy system models

The present report summarises the discussions and conclusions of the international workshop on "Addressing flexibility in energy system models" held on December 4 and 5 2014 at the premises of the JRC Institute for Energy and Transport in Petten. Around 40 energy modelling experts and researchers from universities, research centres, the power industry, international organisations, and the European Commission (DGs ENER and JRC) met to present and discuss their views on the modelling of flexibility issues, the linkage of energy system models and sector-detailed energy models, the integration of high shares of variable renewable energy sources, and the representation of flexibility needs in power system models. The discussions took into account modelling and data-related methodological aspects, with their limitations and uncertainties, as well as possible alternatives to be implemented within energy system models.JRC.F.6-Energy Technology Policy Outloo